Devices, systems, and methods for monitoring symptoms of neurological conditions

Abstract

A system for controlling delivery of electrical stimulation to a subject, the system comprising a control device configured to send stimulation instructions to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes disposed to be positioned proximate to a targeted region of the subject's brain. The stimulation instructions include stimulation parameter values. The control device is configured to receive sensor data from an optical sensor disposed to be positioned proximate to the targeted region, and transmit updated stimulation instructions to the electrical stimulation generator, the updated stimulation instructions including updated simulation parameter values, to cause the electrical stimulation generator to modify stimulation characteristics. The system is configured to analyze the sensor data to determine an activity measure, and determine updated stimulation parameter values ​​based on the determined activity measure.

Description

Technical Field

[0001] Embodiments generally relate to methods, devices, and systems for monitoring symptoms of neurological conditions in a subject, particularly symptoms of neurobehavioral disorders such as attention deficit hyperactivity disorder (ADHD), and in some embodiments for treatment.

Background Art

[0002] Subjects with ADHD tend to exhibit symptoms such as difficulty paying attention, difficulty controlling impulsive behavior, and / or being overly or abnormally active. There are three main types of ADHD: the inattentive-predominant type, in which the subject has difficulty following instructions and paying attention, is easily distracted, and tends to be disorganized; the hyperactive-impulsive-predominant type, in which the subject is fidgety, restless, and impulsive; and the combined type, in which the subject exhibits symptoms of both types.

[0003] Techniques for assessing ADHD symptoms include psychometric tests that measure the performance of executive functions such as testing working memory, attention, and impulse control, and ADHD symptoms can manifest as reduced performance in these tests.

[0004] Electrical brain stimulation is known to have a significant effect on cognitive processes and has various different effects depending on the type of stimulation applied. According to "Effects of transcranial electrical stimulation on cognition" (2012) (43(3) Clinical EEG and Neuroscience 192-199) by Min-Fang Kuo and Michael A. Nitsche (the entire content of which is incorporated herein by reference), non-invasive electrical brain stimulation techniques can amplify the neurophysiological processes required during cognition and / or can mimic the physiological processes of cognition, and furthermore, different types of stimulation can produce different responses. For example, transcranial direct current stimulation (tDCS) can induce physiological changes similar to the neuroplastic changes in cortical functions that are thought to be important for learning and memory formation. Many investigations have shown beneficial effects of tDCS on task performance. Additionally, Kuo and Nitsche (2012) have observed that other techniques such as alternating current stimulation (tACS) and random noise stimulation (tRNS) can also modulate other cortical activities depending on the frequency of the electrical stimulation.

[0005] It is desired to address or improve one or more deficiencies or drawbacks associated with prior art techniques for the treatment of neurological conditions in a subject, such as ADHD and other neurobehavioral disorders, or at least provide a useful alternative thereto.

[0006] Any consideration of the documents, acts, materials, devices, or articles, etc. included in this specification should not be regarded as admitting that any or all of these matters form part of the prior art base existing before the priority date of each of the appended claims or were common general knowledge in the field related to this disclosure. SUMMARY OF THE INVENTION

[0007] Some embodiments are systems for controlling the delivery of electrical stimulation to a subject. The system includes a control device that transmits a stimulation instruction to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes disposed to be positioned proximate to a targeted region of the subject's brain, where the stimulation instruction includes at least one stimulation parameter value; receives sensor data from one or more optical sensors disposed to be positioned proximate to the targeted region; and transmits an updated stimulation instruction including one or more updated simulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation. The system further includes a control device configured to analyze the sensor data to determine an activity metric and determine one or more updated stimulation parameter values based on the determined activity metric.

[0008] The sensor data can include pre-stimulation sensor data acquired prior to delivery of the stimulation to the one or more electrodes, during-stimulation sensor data acquired while the stimulation is being delivered to the one or more electrodes, and post-stimulation sensor data acquired after the stimulation has been delivered to the one or more electrodes. The system is configured to determine pre-stimulation, during-stimulation, and post-stimulation values for one or more characteristics from the pre-stimulation sensor data, the during-stimulation sensor data, and the post-stimulation sensor data; determine relative changes in the values of the one or more characteristics from (i) the pre-stimulation value to the post-stimulation value, (ii) the pre-stimulation value to the during-stimulation value, and (iii) the during-stimulation value to the post-stimulation value; provide the relative changes in the values of the one or more characteristics and the stimulation parameter values to an activity determination model; and determine an activity metric by the activity determination model.

[0009] The activity metric can indicate that sufficient stimulation has been delivered to the subject.

[0010] One or more characteristics may include a functional connectivity between pairs of optical sensor channels and / or a statistical measure of data obtained from the optical sensor channels.

[0011] One or more characteristics may be extracted from sensor data obtained from pairs of optical channels around and / or between stimulation electrodes of one or more electrodes.

[0012] The sensor data may include data obtained from the left lateral prefrontal cortex of the subject, the medial prefrontal cortex of the subject, and / or a boundary region between the medial prefrontal cortex and the left lateral prefrontal cortex of the subject.

[0013] The sensor data may include data obtained from the right lateral prefrontal cortex of the subject, the medial prefrontal cortex of the subject, and / or a boundary region between the medial prefrontal cortex and the right lateral prefrontal cortex of the subject.

[0014] The system is configured to determine one or more updated stimulation parameter values based on the determined activity measure, and in response to a determination that the activity measure is below a threshold, increase the stimulation parameter value and re-apply the stimulation with the increased stimulation parameter value, and in response to a determination that the activity measure has reached the threshold, determine the stimulation parameter value as a user-specific calibrated stimulation parameter.

[0015] The system may further comprise a head-wearable array carrying an optical sensor, the optical sensor being a functional near-infrared spectroscopy (fNIRS) sensor. The head-wearable array may further carry one or more electrodes.

[0016] This system is an optical sensor module coupled to one or more optical sensors. The optical sensor module is configured to emit light from the respective light emitters of the one or more optical sensors and to receive signals indicative of reflected light from the respective detectors of the one or more optical sensors. The signals indicate a cerebrohemodynamic response related to neural activity in a targeted region. The optical sensor module may further comprise an optical sensor module configured to provide sensor data to a control device. The sensor data is based on signals received from the respective one or more sensors. The optical sensor module is configured to operate in response to instructions received from the control device.

[0017] The control device may be configured to cause the light emitters of the one or more optical sensors to be switched on and off at a relatively high frequency to create a lock-in amplifier effect and improve the signal-to-noise ratio (SNR) of the respective detected reflected light signals.

[0018] Each of the one or more optical sensors may comprise a light emitter and first and second detectors, forming two detector channels. The control device is configured to demodulate the signals of the detector channels of each optical sensor.

[0019] Sensor data from multiple functional channels may be downsampled.

[0020] The sensor data may include measurement data including (i) the reflected light intensity at two specific wavelengths detected by one or more sensors, (ii) the concentration of oxygenated hemoglobin (HbO), (iii) the concentration of deoxygenated hemoglobin (HbR), (iv) the total hemoglobin (ThB) concentration, and (v) one or more of the relative changes in any of scales (i) to (iv).

[0021] The control device may be configured to cause a stimulator to supply one or more of (i) transcranial direct current stimulation (tDCS), (ii) transcranial alternating current stimulation (tACS), (iii) transcranial random noise stimulation (tRNS), (iv) transcranial pulsed current stimulation (tPCS), (v) transcranial random noise stimulation (tRNS), and (vi) oscillatory tDCS (otDCS).

[0022] The stimulation instructions may include one or more of (i) voltage, (ii) current, (iii) frequency, (iv) duration, and (v) offset.

[0023] The control device may be configured to cause an electrical stimulator to deliver one or more pulses of relatively short electrical stimulation to one or more electrodes and, after one or more pulses of relatively short electrical stimulation have been delivered to one or more electrodes, cause an optical sensor module to record reflected signals from respective sensors.

[0024] The control device may be configured to cause an electrical stimulator to deliver a relatively long session of electrical stimulation to one or more electrodes and, while the electrical stimulation is being delivered to one or more electrodes, cause an optical sensor module to record reflected signals from respective sensors.

[0025] The control device may be configured to receive data recorded from one or more optical sensors before, during, and / or after delivery of electrical stimulation to one or more electrodes.

[0026] The control device may be configured to continuously monitor the brain activity of a subject.

[0027] The control device may be configured to initiate a session in response to instructions received from a cognitive performance monitoring application deployed on a computing device that communicates with the control device.

[0028] The system may further include a computing device or a server that communicates with the control device over a communication network, and the computing device or the server is configured to receive sensor data from the control device, analyze the sensor data to determine an activity measure, determine one or more updated stimulation parameter values based on the determined activity measure, and transmit the updated stimulation parameter values to the control device.

[0029] The control device may be configured to transmit sensor data to a computing device or a server for processing and to receive updated stimulation parameter values from each computing device or server.

[0030] Some embodiments are systems for determining stimulation parameters for controlling the delivery of electrical stimulation to a subject, the system comprising one or more processors and a memory containing executable instructions that, when executed by the one or more processors, cause the system to receive sensor data from a control device, the sensor data being derived from one or more optical sensors positioned proximate a targeted region of the subject's head, analyze the sensor data to determine an activity measure, determine one or more updated stimulation parameter values based on the determined activity measure, the updated stimulation parameter values indicating characteristics of transcranial electrical stimulation to be delivered to the subject by an electrical stimulation generator under the control of the control device, and transmit the updated stimulation parameter values to the control device.

[0031] The system may further include an activity determination model configured to receive, as input, characteristics extracted from the sensor data and to provide, as output, an activity measure.

[0032] The control device enables selection of a subset of electrodes to be used when applying a stimulus, whereby the control device can be configured to adjust to fit a particular subject's head size. The control device can be configured to receive a head size indicator from the subject via a user interface and to determine a subset of electrodes to be used based on the head size indicator. The control device can be configured to deliver at least a first test signal to each of one or more subsets of the array of electrodes, analyze test responses detected by respective sensor modules, determine a subset of electrodes suitable for the subject based on the detected test responses, and select a subset of electrodes suitable for use when applying a stimulus to the subject's head.

[0033] The system may further comprise a positioning feedback module configured to assist the subject in the correct placement of the array on the subject's head. The positioning feedback module can determine one or more images of the subject wearing the array of the control device, detect the positions of one or more facial features of the subject in the one or more images, detect the position of the array in the one or more images relative to the determined facial features, compare the determined position of the array to a target position, determine that the array is correctly positioned in response to a determination that the position of the array is within an acceptable range, determine that the array is improperly positioned in response to a determination that the position of the array is within an acceptable range, and provide feedback to the subject via the user interface to assist in repositioning the array for the purpose of the subject achieving the target position.

[0034] Some embodiments are methods for controlling the delivery of electrical stimulation to a subject. The method includes transmitting a stimulation instruction to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes disposed to be positioned proximate to a targeted region of the subject's brain, where the stimulation instruction includes at least one stimulation parameter value; receiving sensor data from one or more optical sensors disposed to be positioned proximate to the targeted region; analyzing the sensor data to determine an activity metric; determining one or more updated stimulation parameter values based on the determined activity metric; and transmitting an updated stimulation instruction including the one or more updated stimulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation.

[0035] Some embodiments are methods for determining stimulation parameters for controlling the delivery of electrical stimulation to a subject. The method includes receiving sensor data from a control device, where the sensor data is derived from one or more optical sensors positioned proximate to a targeted region of the subject's head; analyzing the sensor data to determine an activity metric; determining one or more updated stimulation parameter values based on the determined activity metric, where the updated stimulation parameter values indicate characteristics of transcranial electrical stimulation to be delivered to the subject by an electrical stimulation generator under the control of the control device; and transmitting the updated stimulation parameter values to the control device.

[0036] Some embodiments relate to a non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause a computing device to perform the disclosed methods.

[0037] Some embodiments relate to a system for predicting the symptom severity and / or behavioral progression of a subject undergoing treatment for one or more symptoms of a neurological condition, the system comprising a control device configured to receive sensor data from one or more optical sensors arranged to be positioned proximate to a targeted region of the subject's brain, the system further configured to determine task data including one or more scores related to the performance of the subject in performing one or more respective tasks, determine a symptom severity and / or progression measure based on the sensor data and the task data, and output the symptom severity and / or progression measure.

[0038] The symptom severity and / or progression measure may include a plurality of scores each indicating a severity level of a behavior or experience associated with the neurological condition.

[0039] The neurological condition may be ADHD, and the symptom severity and / or progression measure may include scores for one or more of (i) a comprehensive ADHD assessment scale score, (ii) an ADHD core symptom score, (iii) an inattention score, (iv) a hyperactivity score, and (v) an impulsivity score.

[0040] In some embodiments, the system includes a symptom severity and / or progression determination model configured to determine a symptom severity and / or progression measure based on the task data and the sensor data, the symptom severity and / or progression determination model being trained using data derived from a clinical population.

[0041] In some embodiments, the system comprises a feature extraction module configured to determine one or more feature values from the sensor data and provide the feature values to the symptom severity and / or progression determination model. The one or more feature values may indicate a functional connectivity between pairs of optical sensor channels and / or a statistical measure of data obtained from the optical sensor channels.

[0042] One or more characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the right lateral prefrontal cortex of a subject can be provided to a symptom severity and / or progression determination model to determine a comprehensive ADHD symptom severity scale. Characteristic values based on reaction time and omission error criteria of task data can be provided to a symptom severity and / or progression determination model to determine a comprehensive ADHD symptom severity scale.

[0043] One or more characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the right lateral prefrontal cortex can be provided to a symptom severity and / or progression determination model to determine an ADHD core symptom severity scale.

[0044] One or more characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the medial prefrontal cortex of the subject, and / or in the medial prefrontal cortex that faces, overlaps, or touches the left lateral prefrontal cortex of the subject can be provided to a symptom severity and / or progression determination model to determine an inattention severity scale. Characteristic values based on reaction time and omission error criteria of task data can be provided to a symptom severity and / or progression determination model to determine an inattention severity scale.

[0045] One or more characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the right lateral prefrontal cortex, left lateral prefrontal cortex and / or an area overlapping the left lateral prefrontal cortex and medial prefrontal cortex of the subject can be provided to a symptom severity and / or progression determination model to determine a hyperactivity severity scale.

[0046] One or more characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the medial prefrontal cortex of interest and / or in the medial prefrontal cortex that faces, overlaps, or abuts the right lateral prefrontal cortex of the subject may be provided to a symptom severity and / or progression determination model for determining an impulsivity severity scale. Characteristic values based on the reaction time and omission error criteria of task data may be provided to a symptom severity and / or progression determination model for determining an impulsivity severity scale.

[0047] The system may further comprise a head-wearable array carrying an optical sensor, the optical sensor being a functional near-infrared spectroscopy (fNIRS) sensor. The system may further comprise an optical sensor module coupled to one or more optical sensors, the optical sensor module being configured to emit light from a respective emitter of the one or more optical sensors and to receive a signal indicative of reflected light from a respective detector of the one or more optical sensors, the signal being indicative of a cerebrohemodynamic response related to neural activity in a targeted region, and the optical sensor module being configured to provide sensor data to a control device, the sensor data being based on signals received from the respective one or more sensors, and the optical sensor module being configured to operate in response to an instruction received from the control device.

[0048] The control device may be configured to cause the optical sensor module to switch the emitter of the one or more optical sensors on and off at a relatively high frequency to create a lock-in amplifier effect and improve the signal-to-noise ratio (SNR) of each detected reflected light signal. Each of the one or more optical sensors may comprise an emitter and first and second detectors, forming two detector channels, and the control device may be configured to demodulate the signals of each detector channel of the optical sensors.

[0049] Sensor data from multiple functional channels may be downsampled.

[0050] Sensor data may include measurement data including (i) reflected light intensities at two specific wavelengths detected by one or more sensors, (ii) oxyhemoglobin (HbO) concentration, (iii) deoxyhemoglobin (HbR) concentration, (iv) total hemoglobin (ThB) concentration, and (v) one or more of the relative changes in any of metrics (i) to (iv).

[0051] The system may be configured to determine a quality metric indicative of the quality of each detector channel of one or more optical sensors and exclude sensor data from each detector channel when determining symptom severity and / or progression metrics in response to the quality metric being below a quality threshold.

[0052] The system may be configured to determine a subset of sensor data including task-related sensor data acquired while the subject is performing a task based on timestamped sensor data and timestamped task data. The system may further include a feature extraction module configured to extract one or more features from the task-related sensor data, and determining symptom severity and / or progression metrics based on the sensor data and task data includes determining symptom severity and / or progression metrics based on the one or more features and task data.

[0053] The system may include a cognitive performance monitoring application configured to evaluate a subject performing one or more specific tasks and assign one or more task scores to the subject based on the subject's performance when performing the tasks, wherein the task data includes the one or more task scores.

[0054] The control device may be configured to send a stimulation instruction to an electrical stimulation generator to cause the electrical stimulation generator to deliver transcranial electrical stimulation to one or more electrodes disposed to be positioned proximate to a targeted region of the subject's brain.

[0055] The head-mounted array may further carry one or more electrodes.

[0056] The system may further include a computing device or server that communicates with the control device over a communication network. The computing device or server is configured to receive sensor data from the control device, determine task data, determine a symptom severity and / or progression scale based on the sensor data and the task data, and output the symptom severity and / or progression scale. The control device may be configured to transmit the sensor data to the computing device or server.

[0057] The control device may be configured to enable selection of a subset of the electrodes of the array to be used when applying a stimulus, thereby adjusting the control device to fit a particular subject's head size. The control device may be configured to receive a head size indicator from the subject via a user interface and determine a subset of the electrodes to be used based on the head size indicator. The control device may be configured to deliver at least a first test signal to each of one or more subsets of the electrodes of the array, analyze the test responses detected by the respective sensor modules, determine a subset of the electrodes suitable for the subject based on the detected test responses, and select a subset of the electrodes suitable for use when applying a stimulus to the subject's head.

[0058] The system may further include a positioning feedback module configured to assist a subject in the correct placement of the array relative to the subject's head. The positioning feedback module is configured to determine one or more images of the subject wearing the system's array, detect the positions of one or more facial features of the subject in the one or more images, detect the position of the array in the one or more images relative to the determined facial features, compare the determined position of the array to a target position, determine that the array is correctly positioned in response to a determination that the position of the array is within an acceptable range, determine that the array is inappropriately positioned in response to a determination that the position of the array is within an acceptable range, and provide feedback to the subject via a user interface to assist the subject in repositioning the array for the purpose of achieving the target position.

[0059] Some embodiments relate to a system for inferring symptom severity and / or behavioral progression of a subject undergoing treatment for one or more symptoms of a neurological condition, the system being configured to receive sensor data from a control device, the sensor data being derived from one or more optical sensors positioned proximate a targeted region of the subject's head, determine task data including one or more scores related to performance when the subject performs one or more respective tasks, determine a symptom severity or progression measure based on the sensor data and the task data, and output the symptom severity or progression measure.

[0060] Some embodiments are methods for inferring the behavioral progression of a subject undergoing treatment for one or more symptoms of a neurological condition, the method comprising receiving sensor data from one or more optical sensors disposed to be positioned proximate to a targeted region of the subject's brain; determining task data including one or more scores related to the subject's performance when performing one or more respective tasks; determining a symptom severity or progression measure based on the sensor data and the task data; and outputting the symptom severity and / or progression measure.

[0061] Some embodiments are methods for inferring the symptom severity and / or behavioral progression of a subject undergoing treatment for one or more symptoms of a neurological condition, the method comprising receiving sensor data from a control device, the sensor data being derived from one or more optical sensors positioned proximate to a targeted region of the subject's head; determining task data including one or more scores related to the subject's performance when performing one or more respective tasks; determining a symptom severity or progression measure based on the sensor data and the task data; and outputting the symptom severity and / or progression measure.

[0062] Some embodiments are servers arranged to communicate across a communication network with a control device for detecting a subject's brain activity, the server being configured to receive sensor data from the control device, the sensor data indicating the reflected light intensity at two unique wavelengths detected by sensors coupled to the control device while one or more sensors are positioned proximate a targeted region of the subject's brain and the subject is performing a specific task; receive one or more task scores from a cognitive evaluation application deployed on a computing device associated with the subject, the cognitive evaluation application being configured to evaluate a subject performing a specific task and assign one or more task scores to the subject based on their performance; provide the sensor data and the one or more task scores as inputs to a symptom severity or progression determination model; and determine a symptom severity or progression measure as an output of the symptom severity or progression determination model, the symptom severity or progression measure indicating the symptom severity of a neurological condition or the progression exhibited by the subject in the treatment of symptoms of a neurological condition.

[0063] Some embodiments are computer-implemented methods for inferring the behavioral progression of a subject undergoing treatment for one or more symptoms of a neurological condition, the method comprising receiving sensor data from a control device, the sensor data indicating the reflected light intensity at two unique wavelengths detected by sensors coupled to the control device while one or more sensors are positioned proximate to a targeted region of the subject's brain while the subject is performing a particular task; receiving one or more task scores from a cognitive assessment application deployed on a computing device associated with the subject, the cognitive assessment application being configured to evaluate a subject performing a particular task and assign one or more task scores to the subject based on their performance; providing the sensor data and the one or more task scores as inputs to a symptom severity or progression determination model; and determining a symptom severity or progression measure as an output of the symptom severity or progression determination model, the symptom severity or progression measure indicating the symptom severity of the neurological condition or the progression exhibited by the subject in the treatment of the symptoms of the neurological condition.

[0064] Some embodiments relate to a non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause a computing device to perform the methods disclosed herein.

[0065] Some embodiments are head - mountable devices that include an array of a plurality of optical sensor components, the components being arranged along the length of the array, each optical sensor component comprising a light emitter, a first detector, and a second detector, the light emitter being disposed proximate to the first detector so as to form a first relatively short channel, the light emitter being disposed at a relatively large distance from the second detector so as to form a first relatively long channel, an array, and an optical sensor module configured to emit light from a selected light emitter of the optical sensor components and receive a signal indicative of reflected light from the first and second detectors of the selected optical sensor component, the signal indicating a cerebro - hemodynamic response related to neural activity in a region targeted by the pair of the light emitter and the detector, the array further including a plurality of electrodes, each electrode being disposed between a pair of adjacent optical sensor components. Both the light emitter and the first detector are disposed towards a first end of the array, the second detector may be disposed at a second end of the array, and one or more electrodes may be configured to deliver an electrical stimulus to the subject. One or more electrodes may be configured to determine electroencephalogram (EEG) signals from the subject.

[0066] Throughout this specification, the word "comprise", or variations such as "comprises" or "comprising", is to be understood to mean the inclusion of a stated element, integer, or step, or group of elements, integers, or steps, but not the exclusion of any other element, integer, or step, or group of elements, integers, or steps.

Brief Description of the Drawings

[0067] The various accompanying drawings merely illustrate exemplary embodiments of the present disclosure and are not to be regarded as limiting its scope.

[0068]

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[0069] Embodiments generally relate to methods and systems for monitoring the neurological state of a subject, particularly the symptoms of neurobehavioral disorders such as attention deficit hyperactivity disorder (ADHD), and in some embodiments for treatment.

[0070] The application of electrical stimulation to the brain of a subject having a neurobehavioral disorder is effective in inducing physiological changes that result in neuroplastic changes in cortical functions that are thought to be important for learning and memory formation over time. However, in such subjects, for example, the degree and duration of stimulation required to improve cognitive performance during a particular period while performing a task can vary from task to task and from subject to subject. Some of the described embodiments disclose techniques for controlling the delivery of electrical stimulation to a subject and adjusting the parameters of the electrical stimulation based on the measured brain activity of the subject that can be improved in response to the electrical stimulation.

[0071] In some embodiments, a system is provided for controlling the delivery of electrical stimulation to a subject's brain and monitoring or detecting the cerebrohemodynamic response related to neural activity. In some embodiments, a system is provided for detecting the cerebrohemodynamic response related to neural activity. The system is further configured to determine an activity metric based on the cerebrohemodynamic response and adjust one or more parameters of the simulation based on the activity metric. Thus, in some embodiments, the system provides "closed-loop" monitoring of brain activity, which enables confirmation that the applied stimulus has actually reached the subject's brain and determination of whether it is producing the desired effect. It can also enable action to be taken if the current has not reached or the current that has reached is not sufficient to produce the desired effect. For example, the electrodes used to deliver the stimulus can be adjusted and / or the stimulation parameters can be changed to improve the application of the stimulus and the results achieved. This can improve the patient's outcome.

[0072] The system or at least a part of the system includes a head-worn or head-mountable device such as a headset, carrying electrodes for delivering the stimulus and / or sensors for monitoring brain activity. The subject can be asked to perform a specific task or cognitive assessment while wearing the head-worn device to target a specific area of the brain. In some embodiments, a cognitive performance monitoring application deployed on a computing device (such as a smartphone) can be configured to cooperate with the system to coordinate the task to be performed with the operation of the system.

[0073] In some embodiments, the system, or the devices of the system, are arranged such that a control device detects or monitors activity in a targeted area via a plurality of optical sensors or optodes, such as functional near-infrared spectroscopy sensors (fNIRS), positioned at important locations on the head of the subject. For example, a head-mounted device can be configured to properly position the sensors relative to the subject's head. Performance of a particular task by the subject will result in activity in a particular part of the subject's brain, and this activity is detected and monitored by the control device via the sensors.

[0074] The control device is arranged to deliver transcranial electrical stimulation (by an electrical stimulator) to a plurality of electrodes positioned at a particular location on or near the head of the subject, such as the frontal lobe, for the purpose of producing a treatment or beneficial effect on symptoms of neurobehavioral disorders such as ADHD. Stimulation in a particular area of the brain increases the likelihood of neuron firing (Chhatbar et al., 2018, Voroslakos et al., 2018, Islam, Aftabuddin, Moriwaki, Hattori & Hori, 1995, Polania, Nitsche & Paulus, 2010). Such repeated neuron firing has been found to enhance the strength of neuron connections via long-term potentiation and upregulation of brain-derived neurotrophic factor (Liebetanz, Nitsche, Tergau & Paulus, 2002, Cocco et al., 2020, Cavaleiro, Martins, Goncalves & Castelo-Branco, 2020). This is consistent with Hebb's theory of learning and neural plasticity, which states that cells that fire together wire together (Hebb, 1949, Shatz, 1992).

[0075] The excitability of the target nerve changes in response to the application of electrical stimulation, and fluctuations in cortical activity can be detected by a control device via a sensor. This system is configured to analyze measurement data (cerebral hemodynamic response data) from an optical sensor that can be positioned in proximity to the electrodes, where the measurement data is based on or includes optical signals from the respective sensors. For example, the optical signal can indicate the intensity of reflected light at two or more distinguishable wavelengths, and the measurement data can include curves indicating changes in the concentration of the optical signal and / or oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and / or a combination of both (total hemoglobin ThB). Based on this analysis, the system determines an activity measure indicating the activity level of the targeted brain region that may be affected by the stimulation, determines the value of the stimulation parameter based on the activity measure, and transmits a stimulation instruction including the stimulation parameter to an electrical simulation generator to modify or adjust the simulation being delivered or to be delivered to the subject via the electrodes.

[0076] In some embodiments, the control device of the system is configured to analyze the measurement data. The control device can further be configured to determine an activity measure indicating the activity level of the targeted brain region, determine the value of the stimulation parameter based on the activity measure, and transmit a stimulation instruction including the stimulation parameter to an electrical simulation generating device to modify or adjust the simulation being delivered or to be delivered to the subject via the electrodes.

[0077] In some embodiments, the control device may be configured to provide or stream measurement data to a computing device or a server via a wireless communication network such as Bluetooth. The computing device or the server may be configured to determine an activity metric indicative of the activity level of the targeted brain region and to determine a value of a stimulation parameter based on the activity metric. The computing device or the server may transmit a stimulation instruction including the stimulation parameter to the control device to cause the control device to modify or adjust the simulation being delivered or to be delivered to the subject via the electrodes by the electrical simulation generator.

[0078] The activity metric may be derived from properties or features extracted from the optical signals received from the sensors and may indicate biomarkers related to changes in cortical activity. For example, when the subject begins to engage in a task and / or is exposed to the delivered electrical stimulation, the resulting neural activity causes physiological changes in the local network of blood vessels in the subject's brain, which can cause changes in the cerebral blood volume (CBV) per unit of brain tissue, the velocity of cerebral blood flow, and the concentrations of oxyhemoglobin and deoxyhemoglobin. The detected optical signals indicate these cerebrohemodynamic responses related to the activity level.

[0079] In some embodiments, the system (e.g., the control device, the computing device or the server) may employ a univariate or multivariate activity determination model configured to receive, as input, properties extracted from the sensor signals and to provide, as output, an activity metric. For example, the activity metric may indicate whether sufficient stimulation has been delivered to the subject to achieve a desired activity level in the targeted area or a confidence score related to whether it has been delivered. The activity metric may indicate whether the user's brain activity is considered to be sufficiently active, underactive, overly active, too reactive, sufficiently reactive, or underreactive.

[0080] In such an embodiment, the system can determine a stimulation parameter or a change to a stimulation parameter based on an activity metric. In some embodiments, the system is configured to receive, as input, an activity metric and, in some embodiments, a previously applied stimulation parameter, and to provide, as output, an updated stimulation parameter value, employing a univariate or multivariate stimulation control determination model. The stimulation parameter can be simply an on / off parameter value or can include values for parameters such as frequency, duration, amplitude, etc. The control device transmits a stimulation instruction including the stimulation parameter to an electrical stimulator to adjust the stimulation being delivered to the patient, for example, to cause cessation of stimulation or to adjust the characteristics of the stimulation. In some embodiments, a computing device or server can transmit the stimulation instruction to the control device.

[0081] In some embodiments, the activity determination model and / or the stimulation control determination model can be trained on data derived from a subject so that the control device is configured to provide a customized treatment for a particular subject. By customizing the model for a user, the model tends to be more accurate, resulting in an improved control device. In some embodiments, a global model for the activity determination model and / or the stimulation control determination model can be trained on a global dataset that includes examples from multiple subjects. The global dataset can be filtered to include examples derived from subjects having some or more factors in common with a candidate subject, such as age and gender. The trained global model can then be refined and customized based on data related to or collected from the candidate subject to provide a user-specific model. By training the model on the global dataset and refining the trained global model based on user-specific data, the resulting customized user-specific model can be determined based on a relatively small set of candidate subject data.

[0082] In some embodiments, the subject performs a task or activity for activating a specific region of the brain, aiming to target the effect of stimulation to the region of the brain activated by those tasks. Such tasks can focus on behaviors or symptoms such as working memory, attention, and / or impulse control. The scores (task data) related to these tasks, and the sensor data recorded before, during, and after the execution of the tasks can be analyzed by the system to determine the symptom severity or progression scale. In some embodiments, the system can be configured to determine severity scores for multiple symptoms, behaviors, and / or experiences of the neurobehavioral disorder.

[0083] The symptom severity or progression scale can be derived from characteristics or features extracted from the optical signals received from the sensors, and can indicate biomarkers related to changes in cortical activity. For example, when the subject starts to engage in a task, the resulting neural activity causes physiological changes in the local network of blood vessels in the subject's brain, which can cause changes in the cerebral blood volume (CBV) per unit of brain tissue, the velocity of cerebral blood flow, and the concentrations of oxyhemoglobin and deoxyhemoglobin. The detected optical signals indicate these cerebrohemodynamic responses related to the activity.

[0084] In some embodiments, the system (e.g., a control device, computing device, or server) may employ a univariate or multivariate symptom severity or progression determination model configured to receive, as inputs, characteristics extracted from sensor signals and task data, and provide, as outputs, symptom severity or progression measures. The progression measure may indicate the symptom severity relative to a previously predicted symptom severity, and thus may be the progression that a subject exhibits in the treatment of symptoms of a neurological condition. For example, if the symptom severity or progression determination model 327 is configured to determine the symptom severity or progression of features or behaviors associated with ADHD, the symptom severity or progression determination model 327 may provide, as outputs, values for one or more of an overall ADHD assessment scale score, an ADHD core symptom score, an inattention score, a hyperactivity score, and an impulsivity score.

[0085] In some embodiments, information related to a session for treating or monitoring symptoms of a neurobehavioral disorder being performed by a subject may be transmitted to a cognitive performance monitoring or evaluation application or other application deployed on the subject's computing device, such as process-related notifications, or criteria for physical state and / or performance during the session.

[0086] This system can be used by a user on an ad - hoc basis, for example, to assist in stimulating a specific area or region of the brain when performing a task, thereby improving the user's immediate performance when performing the task. Thus, this system can provide short - term benefits to the user. For example, a child may choose to use this system when doing homework. In some embodiments, this system can be used to assist in monitoring a specific area or region of the brain when performing a task, thereby providing information about the user's neural activity in a specific area or region of the brain when performing the task. This system can be used regularly, for example, as part of a treatment plan, thereby improving the user's long - term cognitive performance by enhancing the strength of neural connections through long - term enhancement and up - regulation of brain - derived neurotrophic factors.

[0087] FIG. 1 depicts an embodiment of a device 100 for monitoring and / or treating symptoms of neurobehavioral disorders such as ADHD. In some embodiments, the symptoms are targeted working memory, attention, and / or impulse control.

[0088] In some embodiments, and as illustrated, the device 100 can include a mount 125 configured to be worn on the head by a subject or user 115. For example, the mount 125 can be a band or cap worn on the head. Further examples of embodiments of the mount 125 of the device 100 are illustrated in FIGS. 4, 5, and 6, as will be discussed in more detail below. The device 100 can be a portable or wearable headset.

[0089] The device 100 may comprise one or more electrodes 120 configured to be attached near or to the head of the user 115 in a target area or area of interest. In some embodiments, and as illustrated, the electrodes 120 may be arranged relative to each other in an array and / or may be carried by a mount 125. The electrodes are configured to receive electrical stimulation from an electrical stimulation generator or source 350 (FIG. 3) under the control of the control device 110 and to deliver transcranial electrical stimulation or transcranial nerve stimulation to a target area of the brain of the user 115. For example, the electrodes 120 may be configured to deliver a supplied current, such as transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and / or transcranial random noise stimulation (tRNS), to the target area. The electrodes 120 may include several individually attachable electrodes or more than one electrode in an array configuration. The application of electrical stimulation to the brain affects brain activity and thus, when the device 100 is worn by the user 115, the set location or position of the electrodes 120 relative to each other and relative to the head and brain of the user 115 enables a particular area of the brain to be targeted by the stimulation.

[0090] The machine 100 further includes one or more optical sensors 130. The optical sensor 130 includes a functional near-infrared spectroscopy sensor (fNIRS) configured to emit near-infrared light (typically having a wavelength of 650 to 1000 nm), and may be configured to measure the cerebrohemodynamic response related to brain activity. Specifically, the optical sensor 130 is configured to emit light at two or more different wavelengths. For this purpose, each optical sensor 130 includes a light emitter 130A and a corresponding photodetector 130B (FIGS. 7a and 7b). The pair of the light emitter 130A and the photodetector 130B are disposed or arranged on the same side on the mount 125 and / or alternatively on the user's head as shown in FIGS. 7a and 7b. Therefore, the measured values to be recorded are due to the backscattered (reflected) light following an elliptical path. The reflected light detected by the optical sensor 130 indicates the concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR), and these sensing signals (or measurement information derived from the recorded data) are provided to the control device 110 for analysis. The functional near-infrared spectroscopy sensor (fNIRS) indirectly measures the neuronal activity of the cerebral cortex via the neurovascular coupling by quantifying the change in the hemoglobin concentration in the brain based on the light intensity measurement, as described in "fNIRS-based brain-computer interfaces: a review" by Noman Naseer and Keum-Shik Hong ((2015) Frontiers in Human Neuroscience) (the entire content of which is incorporated herein by reference).

[0091] The optical sensor 130 can be configured to record changes in blood oxygenation in a specific region of the user 115's brain, depending on the placement of the sensor relative to the user's brain. Changes in blood oxygen can be increased due to an increase in neuronal activity, as indicated by oxygen metabolism, or decreased due to a decrease in neuronal activity, and reflect changes in brain activity to represent the energy requirements of the area of the brain. This can be achieved by analyzing raw signals based on light reflection and absorption, and / or by converting the raw signals into hemodynamic responses.

[0092] When the device 100 is applied to the subject's head, the optical sensor 130 is configured to measure brain activity at a location or position in the subject's brain that is intermediate or at the midpoint between a pair of a light emitter 130A and a light detector 130B. Typically, the optimal or maximum of the electrical stimuli delivered to the subject's brain via each electrode 120 is at the point directly beneath each respective electrode 120. Accordingly, the optical sensor 130 is configured to measure brain activity around and between the stimulation electrodes. By setting the arrangement of the optical sensor 130, i.e., the pair of the light emitter 130A and the light detector 130B, relative to each respective electrode 120, a specific part or location of the subject's brain can be targeted for the stimulation and the associated (or responsive) measured brain activity. An example of the configuration of the optical sensor 130 and the electrode 120 will be described in more detail below with reference to FIGS. 7a, 7b, and 12.

[0093] When the array 700 of the device 100 is placed on the user's head, the pairs of light detectors D1, D2 and the light emitter S1 are directed towards a part of the left anterior frontal region of the user's brain, the pairs of light detectors D3, D4, and D5, D6, and the light emitters S2 and S3 are directed towards the left lateral prefrontal cortex of the user's brain, the pairs of light detectors D7, D8 and D9, D10, and the light emitters S4 and S5 are directed towards the medial prefrontal cortex of the user's brain, the pairs of light detectors D11, D12 and D13, D14, and the light emitters S6 and S7 are directed towards the right lateral prefrontal cortex of the user's brain, and the pair of light detectors D15, D16, and the light emitter S8 are directed towards the right anterior frontal region of the user's brain.

[0094] The use of an fNIRS sensor as the optical sensor 130 can enable relatively low cost, portability, safety, accuracy, and / or ease of use compared to other sensors. In particular, the fNIRS sensor is less sensitive to motion artifacts than other blood oxygenation sensors such as magnetic resonance imaging (MRI). The fNIRS sensor is also less susceptible to the effects of electrical noise and motion artifacts compared to other electrical sensors such as electroencephalogram (EEG). Thus, the use of the fNIRS sensor as the optical sensor 130 allows the sensor 130 to be placed relatively close to the electrical stimulation site of the electrode 120, thereby providing an improved reading. Further, the fNIRS sensor has a relatively high spatial resolution compared to EEG and exhibits higher signal quality in the described embodiments. Additionally, the fNIRS sensor enables the measurement and recording of activity data while a stimulation is being delivered, which is difficult to do accurately and effectively with EEG. This is because neural signals have a very small amplitude and can be masked by other signals and noise. The stimulator delivers a current that is picked up by the EEG, and the amplitude of the stimulation signal is many orders of magnitude larger than the neural signal. Attempting to record the EEG while applying an electrical stimulation will result in most of the EEG signal being due to the electrical stimulation, making it difficult to detect the neural response. Further, the stimulation can saturate the EEG sensor, making it impossible to see the neural signal. As a result, it is difficult to record data using EEG while a stimulation is being applied, is prone to failure due to the relatively high likelihood of sensor saturation, and involves complex signal processing to attempt to recover the neuron signal. Thus, the use of the fNIRS sensor as the optical sensor 130 allows for a more efficient determination with higher accuracy and improved signal quality.

[0095] The apparatus 100 further comprises a control device 110. As illustrated, the control device 110 can be housed in a housing 112. The housing 112 can further comprise an optical sensor module 340 and / or an electrical stimulation source 350 (FIG. 3). The housing 112 can comprise an electrode (back electrode) 123 that functions as a return electrode pad for the electrodes 120. As will be discussed in more detail below with reference to FIG. 3, the control device 110 can be configured to transmit a stimulation instruction, which can include stimulation parameter values for the electrical stimulation source 350 to deliver transcranial electrical stimulation via the electrodes 120 to the electrodes 120 and a target area of the brain. The control device 110 can be configured to receive response data, such as a response signal, from the sensor 130 via the optical sensor module 340. In some embodiments, the electrodes 120 can be configured to determine a measure of the delivered electrical stimulation, such as, for example, determining a measure of the subject's brain activity. For example, in such embodiments, the electrodes 120 can be configured to determine or receive electroencephalogram (EEG) signals.

[0096] As illustrated, the control device 110 and / or the housing 112 can be mounted on the rear portion of the mount 125 with respect to the sensor 130, or on the front portion of the mount 125 with respect to the sensor 130. In some embodiments, the control device 110 can be formed as part of the mount 125. For example, the control device 110 and / or the housing 112 can be arranged to be positioned near or on the subject's forehead, or near or on the occiput, when the apparatus 100 is mounted on the subject's head.

[0097] FIG. 2 depicts a block diagram of a system architecture 200 for controlling the delivery of transcranial electrical stimulation to a subject and / or monitoring the subject's neural activity, according to some embodiments. The control device 110 can communicate with a server 230, one or more computing devices 220, and / or a database 240 through a communication network 210.

[0098] Network 210 may include at least a part of one or more networks having one or more nodes that perform transmission, reception, transfer, generation, buffering, storage, routing, switching, processing, or a combination thereof, such as one or more messages, packets, signals, or a combination thereof. Network 210 may include, for example, one or more of a wireless network, a wired network, the Internet, an intranet, a public network, a packet-switched network, a circuit-switched network, an ad-hoc network, an infrastructure network, a public switched telephone network (PSTN), a cable network, a cellular network, a satellite network, an optical fiber network, or a combination thereof.

[0099] Server 230 may include one or more processors or computing devices configured to share data or resources among a plurality of network devices. Server 230 may include a physical server, a virtual server, or a combination of one or more physical servers or virtual servers.

[0100] Database 240 may include a data store configured to store data from network devices through Network 210. Database 240 may include a virtual data store in the memory of a computing device connected to Network 210 by Server 230 or directly connected to Network 210.

[0101] The electrical stimulation source 350 is configured to receive an instruction from the control device 110 and provide electrical stimulation to one or more electrodes 120 in response to the instruction. In some embodiments, the electrical stimulation source 350 may provide information about the electrical stimulation applied to the control device 110 or live monitoring feedback, enabling the control device 110 to monitor and / or control the characteristics of the electrical stimulation provided or supplied to the electrodes 120. For example, the control device 110 may be configured to monitor the performance of the electrical stimulation source 350 to ensure that it operates as instructed and within acceptable safety limits. The electrical stimulation source 350 may also be configured to modify the stimulation parameters or the characteristics of the applied electrical stimulation based on the instruction received from the control device 110.

[0102] The electrical stimulation source 350 may be configured to supply a current, such as transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and / or transcranial random noise stimulation (tRNS), to the user 115 through one or more electrodes 120. In some embodiments, a combination of two or more stimulation types may be used, such as when the current is positive but also alternating. This may provide the beneficial effects of both tDCS and tACS. A graphical depiction of a combined current including tDCS and tACS over time is depicted in FIG. 9A. In some embodiments, the electrical stimulation source 350 may be configured to provide stimulation in a frequency range of 0.1 to 10 kHz. The amplitude between peaks may be in the range of 0.5 to 4 mA.

[0103] FIG. 9B is a graphical depiction of currents over time including transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial pulsed current stimulation (tPCS), transcranial random noise stimulation (tRNS), and oscillatory tDCS (otDCS). In some embodiments, the otDCS signal may be generated by a combination of tDCS, tACS, and tPCS. In some embodiments, any combination of the depicted signal types may be used to provide transcranial stimulation (such as a combination of tDCS and tACS as seen in FIG. 9A). In some embodiments, a combination of tDCS, tACS, and / or tRNS is used to provide transcranial stimulation, or a combination of otDCS and tRNS. Electrical stimulation source 350 may be used by electrodes 120 to provide stimulation corresponding to any of the signal types depicted in FIGS. 9A and 9B. The type of stimulation applied may be selected by a user using computing device 220, which issues an instruction to control device 110 through network 210.

[0104] The control device 110 may be configured to receive signals or measurements indicating neural activity from the optical sensor module 340. The optical sensor module 340 is configured to be connected to the optical sensor 130. The optical sensor module 340 may include an fNIRS recording module. In response to receiving an instruction from the control device 110, the optical sensor module 340 is configured to cause the light emitter 130A to emit light and receive a signal indicating the reflected light detected or measured by the detector 130B. For example, the optical sensor module 340 may modulate an instruction or signal received from the control device 110 and provide a composite signal (the instruction or input signal superimposed on a carrier wave) to the light emitter 130A to cause the light emitter to emit light having specific characteristics. In some embodiments, the reflected light signal detected by the detector 130B is demodulated by the optical sensor module 340, and the recorded data or measurements are provided to the control device 110. In other embodiments, the control device 110 may demodulate the detected reflected light signal. In some embodiments, the optical sensor module 340 may provide information on light emission and detection or live monitoring feedback to the control device 110, enabling the control device 110 to monitor and / or control the operation of the optical sensor module 340 and the sensor 130.

[0105] In some embodiments, the optical sensor module 340 may be configured to create a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the detected reflected light signal. For example, the optical sensor module 340 may modulate an instruction or signal received from the control device 110 and provide a composite signal (the instruction or input signal superimposed on a carrier wave) to the emitter 130A to cause the emitter to emit light having specific characteristics. For example, the optical sensor module 340 may be configured to switch the emitter 130A of each optical sensor module 340 on and off at a relatively high frequency, i.e., a blinking frequency. In other words, the emitter 130A is modulated at the blinking frequency. For example, the blinking frequency may be 100 hz or more, for example 125 hz. As a result, the data or signal detected by each detector 130B is shifted to a frequency near the blinking frequency. Since noise is often higher at lower frequencies than at higher frequencies in analog signal measurements, the SNR of the response signal can be improved by shifting the response signal to a relatively high frequency.

[0106] The sampling rate used by the control device to determine sensor data from the response signal detected by the detector 130B must be at least twice the highest frequency component of the detected response signal (Nyquist's theorem). However, the higher the sampling rate, the more difficult it tends to be to obtain reliable samples, more samples are obtained, and / or the time available for the processor 310 of the control device 110 to perform other processing tasks is shortened. Accordingly, in some embodiments, a sampling rate four times the highest frequency component of the emitter signal 130A is selected. For example, the blinking frequency may be 125 hz and the sampling rate may be 500 hz, for example.

[0107] In some embodiments, the control device 110 can be a wireless device configured to communicate, for example, wirelessly with a computing device and / or a server. In some embodiments, the control device 110 can be Bluetooth-compatible.

[0108] As described above, sampling at a relatively high frequency results in a correspondingly relatively large number of data samples being acquired. For example, considering an embodiment where the device 100 carries eight optical sensors 130 (or optical sensor components 130), each providing a short channel and a long channel, (500 samples / second) * (3 bytes / channel / second) * (16 detector channels (i.e., detector 130B)) = 24 kilobytes / second. Such a data rate is too high to utilize the low-energy Bluetooth protocol BLE5.0 (typically, in BLE5.0, a maximum of about 5 kilobytes / second can be transmitted). Therefore, to wirelessly transmit the acquired data at this rate to a computing device, server, or other computer using some wireless technologies such as low-energy Bluetooth, downsampling and / or demodulation may be required. In some embodiments, the data rate can be reduced by demodulating the detected response signal. For example, the 16 detector channels can be split into a larger number of functional channels, such as 44 functional channels. In some embodiments, 36 long channels and 8 short channels are used. For example, the device 100 of FIG. 7, where the central electrode is removed or omitted, can be used. The configuration of the long and short channels will be considered in more detail below with reference to FIG. 12. The data can also be downsampled to a sampling rate of 10 Hz. In this example, the new data rate is approximately 10% of the original rate, but still retains useful information, (10 samples / second) * (6 bytes / channel / second) * (44 channels) = 2.64 kilobytes / second. Therefore, sensor data can be acquired with relatively high accuracy (e.g., 3 bytes per sample).

[0109] Machine 100 may include a plurality of functional channels, such as 44 functional channels, each including a pair of data channels. The functional channels may include pairs of emitters and detectors. The depth of measurement to the head achievable by a pair of emitter and detector depends on the distance between the pair of emitter and detector. In some embodiments, the functional channels may include long channels or short channels. The long channels may include pairs of functional channels where the source and detector are relatively far apart from each other. For example, in the case of long channels, the pair of emitter and detector may be separated from each other by about 3 cm. The short channels may include pairs of functional channels where the emitter and detector are separated from each other by a relatively short distance, such as about 1 cm. The long functional channels and the short functional channels may be separated to perform different measurements. The short channels may be used to measure blood oxygenation in the scalp due to the shorter distance between the source and the detector. The long channels may be used to measure blood oxygenation from both the subject's scalp and brain. In some embodiments, the measurement of blood oxygenation from the scalp may have an undesirable effect. In such embodiments, short channels may be used to enable the determination and subtraction of the undesirable effect from the measurement values determined by each long channel during data analysis. In some embodiments, the 44 functional channels may include 8 short functional channels and 36 long functional channels.

[0110] In some embodiments, it may be preferred that the evaluation and processing of the acquired sensor data be performed on a computing device or server other than the control device 110. For example, the processor 310 of the control device 110 may not be powerful enough to perform additional data processing while executing other tasks. The control device 110 may use low-power components and may not require a large battery for long-term use. The recorded sensor data may be transmitted to an external device for additional processing and local / cloud storage. The control device may be a wireless headset used to record sensor data, and the evaluation and processing of the acquired sensor data may be performed on the computing device 220 or the server 230.

[0111] In some embodiments, the control device 110 is configured to cooperate with the electrical stimulation source 350 and the optical sensor module 340 to provide closed-loop stimulation and / or monitoring of the brain activity of the subject. Closed-loop monitoring enables the application of information-based stimulation to treat specific symptoms of neurological conditions such as neurobehavioral disorders.

[0112] In some embodiments, the control device 110 is configured to instruct the electrical stimulation source 350 to provide electrical stimulation to the electrodes 120 in the form of short pulses. Each short pulse signal can be characterized by an amplitude, a frequency, a duration, and an offset. In some embodiments, each short pulse is delivered one at a time. After the delivery of each pulse to the electrodes, the optical sensor module 340 is configured to record brain activity via the sensor 130 and provide the recording or measurement to the control device 110 for evaluation. As will be discussed in more detail below with reference to FIG. 3, the control device 110 determines an activity metric based on the information received from the optical sensor module 340. For example, in some embodiments, the activity metric can be an indicator that the user's brain activity is determined to be sufficiently active, less active, overly active, too reactive, sufficiently reactive, or less reactive. Depending on the activity level of the brain and the purpose of the stimulation, the control device 110 can instruct the electrical stimulation source 350 to deliver more pulses having the same or different characteristics.

[0113] In some embodiments, the control device 110 is configured to instruct the electrical stimulation source 350 to provide electrical stimulation to the electrode 120 in the form of a single relatively long session. The electrical stimulation signal can be characterized by amplitude, frequency, and offset. The optical sensor module 340 is configured to record brain activity via the sensor 130 during the application of stimulation to the electrode and provide the recording or measurement to the control device 110 for evaluation in real time (simultaneously, i.e., while the brain is being stimulated). The control device 110 determines an activity measure based on the information received from the optical sensor module 340. For example, in some embodiments, the activity measure can be an indicator that the user's brain activity is determined to be sufficiently active, low in activity, overly active, too reactive, sufficiently reactive, or low in reactivity. Depending on the activity level of the brain and the purpose of the stimulation, the control device 110 can instruct the electrical stimulation source 350 to adjust the stimulation parameters (i.e., the characteristics of the signal) of the delivered electrical stimulation. For example, the control device 110 can cause the electrical stimulation source 350 to stop providing electrical stimulation to the electrode 120 or can cause one or more of the characteristics of the signal being provided to the electrode 120 to be adjusted.

[0114] The control device 110 can be configured to transmit data received from the electrical stimulation source 350 or the optical sensor module 340, or data generated by the control device 110 itself, to the server 230 for further processing or to the database 240 for storage. The control device 110 can also be arranged to receive instructions or data from the server 230. For example, the server 230 can be configured to transmit configuration instructions or update information to the control device 110 to modify how the control device 110 operates.

[0115] The control device 110 can send information to and receive information from the computing device 220. The computing device 220 may include a computer, a smartphone device, a laptop, a tablet, or other suitable devices. The computing device 220 may include one or more processors 222 and a memory 224 that stores instructions (e.g., program code) that, when executed by the processor 222, cause the computing device 220 to execute a process in cooperation with the control device 110 according to the described method. The computing device 220 may be a computing device associated with a user or, for example, a computing device of the user's clinician or other clinician.

[0116] The computing device 220 includes a network interface 226 that facilitates communication with components of the communication network 210. The computer device 220 may also include a user interface 228 that enables a user to interact with the performance monitoring application 225 and other applications or functions provided by the computing device 220.

[0117] The memory 224 includes a cognitive performance monitoring or cognitive assessment application 225. In some embodiments, the cognitive performance monitoring application 225, when executed by the processor 222, enables the computing device 220 to monitor the cognitive performance of a subject when the subject is undergoing treatment using the device 100 or when the subject is being monitored using the device 100, and, in some embodiments, to control the operation of the control device 110. The cognitive performance monitoring application 225 is downloaded or otherwise deployed on the subject's computing device 220.

[0118] In some embodiments, the cognitive performance monitoring application 225 may be arranged to receive and store data from the control device 110 regarding the user's progress, which data may be displayed to the user and / or provided to the server 230 or another computing device 220. In some embodiments, the cognitive performance monitoring application 225 may be configured to receive and track behavioral data such as sleep data, mindfulness activities, exercise, diet, and / or other information that may affect an individual's cognitive performance. For example, the user may input such information via the user interface 228, or the cognitive performance monitoring application 225 may be configured to cooperate with other applications operating on the computing device 220 such as a pedometer, or other applications operating on other user devices such as a smartwatch. An authenticated clinician may be provided access to the cognitive performance monitoring application 225 deployed on the user's computing device 220, or to a file related to the user stored in the database 240 or on the server 230.

[0119] In some embodiments, the cognitive performance monitoring application 225 is used to perform a treatment using the device 100 in order to activate specific regions of the brain for the purpose of targeting the effect of stimulation to the regions of the brain activated by a task, and to monitor the effect of the resulting stimulation using the device 100, and may include one or more games, tasks, activities, or applications that can be performed by the user. In some embodiments, the tasks of the cognitive performance monitoring application 225 can be performed by the user to activate specific regions of the brain (without delivering electrical stimulation to the brain) for the purpose of monitoring or measuring neural activity using the device 100. For example, such paired activities can be focused on tasks that include working memory, attention, and / or impulse control. In some embodiments, the application 225 further includes a series of task-based activities and / or psychometric tests that are completed independently of or during electrical stimulation sessions. This can provide the beneficial effect of a baseline set of standardized activities, enable a more consistent analysis of the user's 115 brain activity, and provide a consistent baseline on which the model of the control device 110 or computing device 220 can be trained. The cognitive performance monitoring application 225 can also provide feedback to the clinician and / or user about the user's performance over time, and this feedback can be used in determining treatment options, plans, and / or operating parameters of the control device 110. Having the user track their own performance and ideally improvement over time can increase the user's motivation and encourage engagement and adherence to treatment.

[0120] The cognitive performance monitoring application 225 may receive input from a user via the user interface 228. The input may be related to instructions for performing a paired task. The input may be related to instructions for the operation of the control device 110, including sending instructions to the control device 110 and receiving instructions from the control device 110. The computing device 220 may be further configured to display information about the device 100 to the user, including data related to one or more electrodes 120 or one or more optical sensors 130, data from the control device 110, or data from the server 230 and the database 240.

[0121] In some embodiments, the cognitive performance monitoring application 225 may initiate or start the control device 110 in response to user input, e.g., via the user interface 228, and transmit instructions to deliver stimulation to the electrodes and / or to start monitoring the effect of the stimulation on the targeted area. Similarly, the cognitive performance monitoring application 225 may pause or deactivate the electrical stimulation session and / or the recording of sensor data by transmitting instructions to the control device 110. In other words, the cognitive performance monitoring application 225 may be used to control the operation of the control device 110 of the device 100. In some embodiments, the cognitive performance monitoring application 225 may be configured to transmit task data to the control device 110 or the server 230. In some embodiments, the control device 110 or the server 230 may receive task data from the control device 110. In some embodiments, the control device 110, the server 230, and / or the computing device 220 may receive supplemental task data from other locations via, e.g., user input using the user interface 360. The task data may indicate the type of task to be performed by the user during the session or while undergoing treatment. The control device 110, the computing device 220, or the server 230 may use the task data to determine one or more stimulation parameter values and / or to determine an activity metric. For example, the thresholds and / or characteristics for determining the required stimulation, activity metric, and / or stimulation parameter values may vary for each task. In some embodiments, the task data may include one or more scores achieved by the user when performing the task and may be used in combination with the measurement data by the control device 110 to infer the behavioral progression of the subject. For example, the scores may indicate the accuracy and / or reaction time associated with the execution of the task. In other embodiments, the task data, including the recorded data and / or the activity metric and scores, may be transmitted to the server 230 (such as a remote server, etc.) for processing to infer the behavioral progression of the subject.

[0122] Figure 3 depicts a schematic diagram of a system 300 for controlling the delivery of transcranial electrical stimulation and / or for monitoring brain activity, according to some embodiments. In this figure, the functional components of the control device 110 of FIG. 2 are depicted in more detail. However, in other embodiments, it will be understood that one or more of the functional components of the control device 110 may be deployed on other devices or systems, such as the computing device 220 and / or the server 230.

[0123] The control device 110 may be housed within a housing 112. The housing 112 may further include an optical sensor module 340 and / or an electrical stimulation source 350. In some embodiments, the electrical stimulation source 350 and / or the optical sensor module 340 may be outside of the housing 112. The housing 112 may further include a human machine interface (HMI) 355 configured to manually switch on or off the electrical stimulation and / or optical sensing provided to the user 115. In some embodiments, the HMI 355 may be configured to switch the system 300 between an active state, a passive state, and a powered-off state.

[0124] The control device 110 includes one or more processors 310 and a memory 320 that stores instructions (such as program code) that cause the control device 110 to function according to the methods described when executed by the processor 310. The processor 310 may include one or more microprocessors, a central processing device (CPU), a graphics / graphics processing unit (GPU), an application-specific instruction set processor (ASIP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other processors capable of reading and executing instruction code. The processor 310 may include additional processing circuitry. For example, the processor 310 may include multiple processing chips, a digital signal processor (DSP), an analog-digital or digital-analog conversion circuit, or other circuits or processing chips having processing capabilities for performing the functions described herein. The processor 310 may execute all of the processing functions described herein locally on the control device 110, or may execute some processing functions locally and outsource other processing functions to another processing system such as the server 230 or the computing device 220.

[0125] The memory 320 may include one or more volatile or non-volatile memory types. For example, the memory 320 may include one or more of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The memory 320 is configured to store program code accessible by the processor 310. The program code includes executable program code modules. In other words, the memory 320 is configured to store executable code modules configured to be executable by the processor 310. The executable code modules cause the control device 110 to perform specific functions as described in more detail below when executed by the processor 310.

[0126] Memory 320 may include a feature extraction module 322, an analysis engine 324, and / or a stimulation control module 326.

[0127] The feature extraction module 322 includes executable program code that, when executed by the processor 310, causes the control device 110 to identify or extract features or characteristics of signals or data recorded by and received from the optical sensor module 340. Signals measured or calculated by the optical sensor module 340 or the control device 110 may include changes in combinations including HbO and Hbr, HbO and Hbr curves, and total hemoglobin ThB (ThB = HbO + Hbr).

[0128] The characteristics may be features of biomarkers related to cognitive function or cognitive performance or cortical activity or indicative of such biomarkers. In some embodiments, the feature extraction module 322 is configured to determine one or more of the following: · Peak amplitude, · Peak width, · Number of peaks (e.g., bimodal signal), · Slope of a portion of the signal, · Autoregressive moving average (ARMA) coefficients, · Rise time of the amplitude or slope, · Baseline activity, · Baseline trend, · Number of zero crossings, · Hemoglobin values such as the correlation between HbO and HbR, · Rise time of the peak, · Ratio of HbO to HbR with respect to amplitude, width, slope, or other features, · Change in total blood volume, · Signal morphology, and · Area under the curve.

[0129] In other embodiments, other signal characteristics or features may be extracted. The feature extraction module 322 provides the extracted features as an input to the analysis engine 324. In some embodiments, the feature extraction module 322 may be deployed on the server 230 and / or the computing device 220.

[0130] In some embodiments, the features include, or indicate, the functional connectivity between pairs of channels of the device 100, i.e., the statistical dependence and / or similarity between pairs of data from adjacent or distinct regions of the brain. In some embodiments, the features include, or indicate, statistics applied to data derived from one or more channels.

[0131] For example, the features used to determine the activity metric by the activity determination model 328 may be extracted from sensor data obtained from the left lateral prefrontal cortex, the medial prefrontal cortex, and / or the boundary between the medial prefrontal cortex and the left lateral frontal lobe of the subject. The features used by the symptom severity or progression determination model 327 to determine the overall ADHD symptom severity metric may be extracted from sensor data obtained from the right lateral prefrontal cortex of the subject. Also, the reaction time and omission error criteria from the task data may be determined as input features to the symptom severity or progression determination model 327 and used to determine the overall ADHD symptom severity metric.

[0132] The features used by the symptom severity or progression determination model 327 to determine the primary ADHD core symptom score may be extracted from sensor data obtained from the right lateral prefrontal cortex of the subject.

[0133] The characteristics used by the symptom severity or progression determination model 327 to determine the inattention score can be extracted from sensor data obtained from the medial prefrontal cortex of the subject and / or the boundary region between the medial prefrontal cortex and the left lateral prefrontal cortex. Also, the reaction time and omission error criteria from the task data can determine the input characteristics to the symptom severity or progression determination model 327 and can be used to determine the inattention severity scale.

[0134] The characteristics used by the symptom severity or progression determination model 327 to determine the hyperactivity score can be extracted from sensor data obtained from the right lateral prefrontal cortex, the left lateral prefrontal cortex, and / or the region overlapping the left outer lateral prefrontal cortex and the medial prefrontal cortex of the subject.

[0135] The characteristics used by the symptom severity or progression determination model 327 to determine the impulsivity severity scale can be extracted from sensor data obtained from the medial prefrontal cortex of the subject and / or the medial prefrontal cortex facing, overlapping, or adjacent to the right lateral prefrontal cortex of the subject. Characteristic values based on the reaction time criterion of the task data can be provided to the symptom severity and / or progression determination model to determine the impulsivity severity scale.

[0136] The analysis engine 324 may include executable program code configured to determine an activity metric indicative of the measured brain activity of the user or subject in the targeted region based on the cerebrohemodynamic response measured by the optical sensor module 340 when executed by the control device 110. The analysis engine 324 may include executable program code configured to determine a progress metric indicative of the progress of a subject being treated for symptoms of a neurological condition based on the brain activity measured in the targeted region, based on the cerebrohemodynamic response measured by the optical sensor module 340 and task data, such as may be collected by the cognitive performance monitoring application 225, and based on a symptom severity metric indicative of symptoms of the neurological condition, when executed by the control device 110. In other words, the analysis engine 324 infers execution functional performance from the recorded fNIRS data and in some embodiments further from task data.

[0137] In some embodiments, the characteristics detected from the plurality of sensors 130 may be analyzed in combination or relative to each other to determine patterns of behavior or activity. For example, if consecutive sensors or channels are showing positive amplitudes, but the sensors surrounding those positive sensors are clearly showing negative amplitudes, it may be a biomarker indicating that oxygenated blood is moving from one region to another (e.g., as explained by the "steal hypothesis").

[0138] In some embodiments, the analysis engine 324 comprises a univariate or multivariate activity determination model 328. The activity determination model 328 can be a machine learning model. The activity determination model 328 can be configured to receive, as input, characteristics extracted from sensor signals and provide, as output, an activity metric. For example, the activity metric can indicate whether a targeted area of the brain is exhibiting a sufficient level of activity and, in addition, whether sufficient stimulation has been delivered to the subject to achieve a desired level of activity in the target area. This can include comparing the activity metric to a threshold. In other embodiments, the activity metric can be a confidence score related to whether sufficient activity is occurring in the targeted area. In some embodiments, the activity determination model 328 can use techniques such as general linear model analysis, beta values or regression analysis, logistic regression, linear regression, neural networks, and comparison of the activity in one channel of a signal to another channel.

[0139] In some embodiments, the analysis engine 324 provides the activity metric as input to the stimulation control module 326. The stimulation control module 326 can utilize a univariate or multivariate model 329. The stimulation control module 326 can be a machine learning model. The stimulation control module 326 can be configured to receive the activity metric as input and provide, as output, a stimulation parameter value. The stimulation parameter can simply be an on / off parameter value or can include values of parameters such as frequency, duration, amplitude, etc.

[0140] In some embodiments, the analysis engine 324 or the activity determination model 328 of the analysis engine 324 may be deployed on the server 230 and / or the computing device 220. In such embodiments, the server 230 and / or the computing device 220 may be configured to determine an activity metric and provide the activity metric to the control device 110, and the control device 110 may then determine a stimulation parameter value. In some embodiments, the stimulation control module 326 may be deployed on the server 230 and / or the computing device 220, and the server 230 and / or the computing device 220 may be configured to provide a stimulation parameter value to the control device 110 to control the stimulation being delivered to the subject.

[0141] In some embodiments, the control device 110 transmits a stimulation instruction including a stimulation parameter to the electrical stimulation source 350 to adjust the stimulation being delivered to the patient, for example, to cause cessation of the stimulation or to adjust the characteristics of the stimulation signal.

[0142] In some embodiments, the analysis engine 324 includes a univariate or multivariate symptom severity or progression determination model 327. The symptom severity determination model 327 or the progression determination model 327 can be a machine learning model. The symptom severity or progression determination model 327 is configured to receive, as input, one or more features or characteristics extracted from recorded data or measurement data, and one or more scores associated with each task performed by the subject while the data was being recorded, and to provide, as output, a symptom severity scale or a progression scale. For example, the scores can indicate the accuracy and / or reaction time associated with the execution of the task. A plurality of data sets, each including sensor data and associated scores for a particular task, can be used to determine the symptom severity scale or the progression scale. For example, the data sets can cover a specific period of time. In some embodiments, a baseline or an initial set of data is determined, and the progression is evaluated relative to that baseline. In some embodiments, the determined progression scale is determined relative to the most recently determined progression scale or symptom severity scale. In some embodiments, the symptom severity or progression determination model 327 can include one or more submodels configured to infer the progression of behavior related to a particular task. In other embodiments, the symptom severity or progression determination model 327 can be configured to receive, as input, scores associated with a plurality of respective tests. The symptom severity or progression determination model 327 can be configured to provide an indicator or value of symptom severity related to one or more symptoms. For example, if the symptom severity or progression determination model 327 is configured to determine the symptom severity or progression of features or behaviors related to ADHD, the symptom severity or progression determination model 327 can provide, as output, one or more values among a comprehensive ADHD assessment scale score, an ADHD core symptom score, an inattention score, a hyperactivity score, and an impulsivity score.

[0143] In some embodiments, the symptom severity or determination model 327 may be deployed on a server 230 such as a remote server or a computing device 220 such as a smartphone, and the server 230 and / or the computing device 220 may be configured to determine the symptom severity or progression scale. For example, the server 230 and / or the computing device 220 may be configured to receive or determine task data, such as scores, and recorded or measured data from the control device 110 or the feature extraction module 322 and / or the cognitive performance monitoring application 225.

[0144] The system 300 includes a network interface or communication module 330 for facilitating communication with components of the system 300 across the network 210, such as the computing device 220, the database 240, and / or other systems or servers 230. The communication module 330 may include a combination of network interface hardware and network interface software suitable for establishing, maintaining, and facilitating communication through relevant communication channels. The communication module 330 may include a wireless Ethernet interface, a SIM card module, a Bluetooth connection, or other suitable wireless adapters that enable wireless communication through the network 210. For example, in some embodiments, the control device 110 and the computing device 220 are arranged to communicate with each other via Bluetooth. In some embodiments, wired communication means are used.

[0145] For example, in some embodiments, the system and / or the control device 110 may be a wireless system or device such as a wireless headset. In such embodiments, the components of the system 300 and / or the control device 110 may be selected or configured, particularly for low-power operation, to enable long-term use without the need for, for example, a relatively large battery. This can enable a reduction in the size of the device or the overall system and can result in lower manufacturing costs.

[0146] The activity determination model 328 and / or the symptom severity or progression determination model 327 can be based on models such as logistic regression, linear regression, and neural networks, which are trained, for example, to infer activity metrics from fNIRS data. In some embodiments, the activity determination model 328 and / or the symptom severity or progression determination model 327 are trained using a supervised machine learning approach that uses a training data set split into a training data subset and a test data subset. The training data set includes data for a plurality of individuals who have answered a clinically relevant neurobehavioral disorder assessment scale questionnaire and have had their executive function performance measured during a psychometric test while the optical sensor module 340 records fNIRS data. Thus, the training data includes exemplary data for each of the plurality of individuals. The exemplary data includes the results of the psychometric test (and in some embodiments the type of test), as well as the relevant fNIRS data recorded during the execution of each test.

[0147] In some embodiments, the activity determination model 328 and / or the symptom severity or progression determination model 327 can be trained using unsupervised machine learning that helps to reveal additional relationships between the optical signal data, task data (e.g., the results of the psychometric test), and / or stimulus parameters. The activity determination model 328 and / or the symptom severity or progression determination model 327 can also form the basis for feedback provided to medical professionals in the treatment of the user 115. In this way, consistent feedback can be provided to the clinician to assist in the evaluation of the user's progression and treatment decisions, and in some embodiments the resetting of the control device 110. In some embodiments, the activity determination model 328 can include one or more submodels configured to infer brain activity related to a particular task. In other embodiments, the activity determination model 328 can be configured to receive as input scores related to each of a plurality of tests.

[0148] In some embodiments, the stimulation control model 329 can be determined or trained based on an experimental evaluation or identification of the effectiveness of different stimulation parameters. For example, the stimulation control model 329 can include a model such as a neural network trained using logistic regression, linear regression, and / or a supervised machine learning approach. In some embodiments, similar psychometric tests can be performed on participants regardless of the presence or absence of the application of the stimulation, and statistical changes in performance can be observed and recorded. By simulating many different stimulation configurations and experimentally identifying the most promising ones, a determination will be made regarding the most appropriate montage and stimulation parameters (e.g., the amplitude and type / frequency of the stimulation).

[0149] In some embodiments, one or more of the stimulation control module 326, the model 329, the activity determination model 328, the symptom severity (or) progression determination model 327, and the feature extraction module can be located within the memory 224 of the computing device 220 and can be executed by the processor 222 of the computing device 220.

[0150] Figure 4 is an example of device 100 according to some embodiments. Device 100 includes a front band 405 configured for placement at the front of user 115's head and a rear band 415 having a back electrode mount 410 configured for placement at the rear of user 115's head. The front band 405 can be pivoted about point 420 as depicted in Figure 5, and the rear band can be adjustable to accommodate different head sizes of user 115. The rear component 415 can be configured to house or support the control device 110 and thereby function as the housing 112 in Figure 2. The return electrode 123 can also be disposed on the rear component 415. In some embodiments, the rear component 410 can be omitted. The front band 405 can further include an elongated array 700 disposed to carry or wear the electrodes 120, and in this embodiment, the electrodes 120 are arranged in a spaced-apart manner along the length of the array 700. The array 700 can carry or wear an optical sensor 130 (or a component of the optical sensor), more specifically, a light emitter 130A and a detector 130B. In this example, each optical sensor 130 includes a light emitter 130A and two corresponding light detectors 130B. The electrodes 120, the light emitter 130A, and the light detector 103B are disposed or arranged on the same side on the inner surface of the array 700 so as to contact the user's head when the device is worn by the user. In such an embodiment, the front band 405 holds the array 700 in place relative to the front of user 115's head and is configured to more easily provide consistent electrical stimulation to user 115 and receive a more consistent optical response from user 115. Thus, the fixed positions on the array for the electrodes 120 and the optical sensor 130 enable the same region to be stimulated over multiple stimulation sessions when the device is placed on the user's head in this embodiment. This has the beneficial effect of achieving a high degree of consistency when applying electrical stimulation to the desired region of the head and ensuring the accuracy of the measured optical signals, which in turn has a beneficial effect on the accuracy of the determination of the beneficial effects by the analysis engine 324.In some embodiments, the control device 110 may instruct the electrical stimulation source 350 to provide electrical stimulation to a selected or particular electrode 120, or a particular combination of electrodes 120. In other embodiments, the control device 110 is configured to cause the electrical stimulation source 350 to provide electrical stimulation to all of the electrodes in the array 700.

[0151] In some embodiments, a different number of electrodes 120 and / or optical sensors 130, such as a greater number or a lesser number, may be provided, depending on the size of the array 700 on the anterior band 405.

[0152] FIG. 12 depicts a section 1200 of the array 700. The section 1200 includes an electrode 1202 having a first optical sensor 1204 disposed on a first side 1206 of the electrode 1202 and a second optical sensor 1208 disposed on a second side 1210 of the electrode 1202. The first optical sensor 1204 includes first and second photodetectors 1204A1 and 1204A2, and a light emitter 1204B. The second optical sensor 1208 includes first and second photodetectors 1208A1, 1208A2 and a light emitter 1208B.

[0153] As illustrated, in some embodiments, the emitter 1204B of the first optical sensor 1204 is disposed toward or at the first end 1212 of the electrode 1202 on the first side 1206 of the electrode 1202. The first photodetector 1204A1 is disposed toward or near the first end 1212 of the electrode 1202 on the first side 1206 of the electrode 1202 and, for example, near the emitter 1204B. The second photodetector 1204A2 of the first optical sensor 1204 is disposed toward, at, or near the second end 1214 (opposite the first end 1212) of the electrode 1202 on the first side 1206 of the electrode 1202. In some embodiments, the second photodetector 1204A2 is positioned closer to the first photodetector 1204A1 than to the emitter 1204B. In other words, the first photodetector 1204A1 is positioned closest to the emitter 1204B.

[0154] As illustrated, in some embodiments, the emitter 1208B of the second optical sensor 1208 is disposed toward, at, or near the second end 1214 of the electrode 1202 on the second side 1210 of the electrode 1202. The detector 1208A1 of the second optical sensor 1208 is disposed toward or near the second end 1214 of the electrode 1202 on the second side 1210 of the electrode 1202. The second photodetector 1208A2 of the second optical sensor 1208 is disposed toward, at, or near the first end 1212 of the electrode 1202. In some embodiments, the second photodetector 1208A2 is positioned closer to the first photodetector 1208A1 than to the emitter 1208B. In other words, the first photodetector 1208A1 is positioned closest to the emitter 1204B.

[0155] As described above, when the device 100 including the array 700 is applied to the subject's head, the optical sensor 130 is configured to measure brain activity at the location or position of the subject's brain that is intermediate or at the midpoint between the emitter 130A and the photodetector 130B.

[0156] Referring again to FIG. 12, when a stimulus is applied to the subject's head via the electrode 1202, the pairs 1204A1 and 1204B of the emitter and the detector are configured to determine or measure changes in the subject's systemic (skin, skull, etc.) blood oxygenation at a position intermediate between the position of the emitter 1204A1 and the position of the photodetector 1204B.

[0157] Referring again to FIG. 12, when a stimulus is applied to the target head via the electrode 1202, the pairs 1204A2 and 1204B of the light emitter and the detector are configured to determine or measure the brain activity of the target at an intermediate position between the position of the photodetector 1204A2 and the position of the light emitter 1204B, at position 1230A in FIG. 12. Similarly, the pairs 1204A2 and 1208B of the light emitter and the detector are configured to determine or measure the brain activity of the target at an intermediate position between the position of the photodetector 1204A2 and the position of the light emitter 1208B, at position 1230B in FIG. 12, the pairs 1208A2 and 1204B of the light emitter and the detector are configured to determine or measure the brain activity of the target at an intermediate position between the position of the photodetector 1208A2 and the position of the light emitter 1204B, at position 1230D in FIG. 12, the pairs 1208A2 and 1208B of the light emitter and the detector are configured to determine or measure the brain activity of the target at an intermediate position between the position of the photodetector 1208A2 and the position of the light emitter 1208B, at position 1230C in FIG. 12, the pairs 1208A1 and 1208B of the light emitter and the detector are configured to determine or measure the changes in the whole body (skin, skull, etc.) of the blood oxygenation of the target at an intermediate position between the position of the light emitter 1208A1 and the position of the photodetector 1208B, the pairs 1208A1 and 1204B of the light emitter and the detector are configured to determine or measure the brain activity of the target at an intermediate position between the position of the photodetector 1208A1 and the position of the light emitter 1204B, at position 1230E in FIG. 12, the pairs 1204A1 and 1204B of the light emitter and the detector are configured to determine or measure the brain activity of the target at an intermediate position between the position of the photodetector 1204A1 and the position of the light emitter 1204B, and the pairs 1204A1 and 1208B of the light emitter and the detector are configured to determine or measure the brain activity of the target at an intermediate position between the position of the photodetector 1204A1 and the position of the light emitter 1208B, at position 1230E in FIG. 12.

[0158] Pairs of light emitters and detectors 1204A2 and 1204B, 1204A2 and 1208B, 1208A2 and 1204B, 1204A1 and 1208B, 1208A1 and 1204B, and 1208A2 and 1208B each form a relatively long channel. These long channels are arranged to measure cerebral blood oxygenation at an intermediate point between the pair of light emitter and detector. Pairs of light emitters and detectors 1204A1 and 1204B, and 1208A1 and 1208B each form a relatively short channel. These short channels are configured to measure cerebral blood oxygenation of the scalp or scalp region near the subject at an intermediate point between the pair of light emitter and detector. This information (i.e., cerebral blood oxygenation in the near scalp) can be used by the analysis engine 324 to remove scalp information from the measurements of the long channels during data processing. By configuring the placement of the optical sensor 130, i.e., the pair of light emitter 130A and light detector 130B, relative to each respective electrode 120, a specific site or location in the subject's brain can be targeted for the associated (or responsive) brain activity to be stimulated and measured. In some embodiments, the control device 110 is configured to determine brain activity at a specific site in the subject's brain based on one or more sensor signals received from one or more pairs of light emitter and light detector 130A, 130B of each respective sensor module 130. For example, in the case of the control device 110 comprising the array 700 including section 1200 of FIG. 12, brain activity at positions near and surrounding the site of application of the stimulus to the subject (which may be a part or site of the subject's brain directly below the electrode 1202, shown at position 1230E), as well as at the position of the stimulus 1230E, can be determined. In some embodiments, the brain activity at position 1230E can be determined using pairs of light emitter and detector 1204B and 1208A1, and / or 1208B and 1204A1.

[0159] This configuration provides an integrated mechanism for fNIRS recording and electrical stimulation of the brain at the same location. The device 100 comprising the array 700 or the like can enable more accurate measurement and analysis of the effects of electrical stimulation of the brain using fNIRS.

[0160] In some embodiments, the position of electrode 120 is based on the EEG10-5 system and may include positions F3 - F4, FP2 - F3, P3 - FP2, F6 - F5, AF7 - AF8. However, it will be understood that the position of electrode 120 may include any combination of positions that cross the line between P7 - P8, FT9 - FT10, F9 - F10, AF7 - AF8, FP1 - FP2, PO3 - PO4, and O1 - O2. This includes the 10 - 5 locations between these landmarks on the same plane. The optical sensor 130 may be placed around the selected stimulation channel to ensure proximity to the site of electrical stimulation. In such embodiments, different band shapes and positions may be used to ensure an accurate fit with the user 115's head. In some embodiments, adjustment of the front band 405 through the pivot point 420 enables the electrode 120 to target the desired region of the head.

[0161] The fit of the array 700 may be specifically configured to allow placement of the optical sensor 130 at a desired distance from the user 115's head to ensure accurate measurements are obtained for a given user 115.

[0162] The placement of the electrodes for delivery of nerve stimulation is important as it determines the position of the brain targeted by the stimulation. The device 100 may be configured to fit or adapt to various head sizes, taking into account that head sizes vary from person to person, particularly by gender. Due to differences in head size, the position of the electrodes on the static or fixed - configured array 700 of the device 100 may vary from individual to individual in terms of the placement of electrode 120 (e.g., the position where the electrode contacts the forehead), which can result in variations in the outcome of the stimulation and / or the effectiveness of the stimulation application.

[0163] Accordingly, in some embodiments, the front band 405 and / or the array 700 of the device 100 may be adjustable to allow selective placement of the electrode 120 at a desired position on the subject's head.

[0164] In some embodiments, the control device 110 enables a software assignment of electrode positions relative to the subject's head (e.g., selection of a particular subset of electrodes 120 of the array 700), such that a suitable subset of electrodes 120 can be selected to deliver stimulation to the subject, depending on the size of the user's head.

[0165] FIG. 13 depicts a configuration 1300 of the array 700 disposed on a subject's head 1310 as viewed from above. The array 700 includes a plurality of electrodes 120, a subset of which can be selectively used to deliver stimulation to the subject's head. In this example, the array 700 comprises four electrodes, two of the outer ones being the selected electrodes 1320 and two of the inner ones being the non-selected electrodes 1330. This selection of electrodes 120 may be adapted or appropriate for a subject having a relatively large head size. Similarly, FIG. 14 depicts a configuration of the array 700 positioned on a subject's head 1310 as viewed from above. The array 700 includes a plurality of electrodes 120, a subset of which can be selectively used to deliver stimulation to the subject's head. In this example, the array 700 comprises four electrodes, two of the outer ones being the non-selected electrodes 1330 and two of the inner ones being the selected electrodes 1320. This selection of electrodes 120 may be adapted or appropriate for a subject having a relatively small head size.

[0166] In FIGS. 13 and 14, the number of electrodes depicted is merely representative. Thus, any number of electrodes, such as four, six, or eight electrodes, can be disposed on the array 700.

[0167] In some embodiments, the control device 110 may be configured to receive input from a subject or another user, for example via the user interface 360, and to indicate which size configuration is to be accommodated, which in turn may determine which combination of electrodes 120 is to be used by the control device 110 when applying stimulation. For example, the user interface 360 may enable the subject to select a small, medium, or large head size.

[0168] In some embodiments, the control device 110 may be configured to assist in determining an appropriate selection of electrodes for a given subject and an accurate placement of the electrodes on the subject's head by applying a test stimulus delivered to the subject's head via one or more sets of electrodes 120 and analyzing the responses received via the respective sensor modules 130. In some embodiments, the control device 110 may analyze the responses to determine the appropriateness or other of one or more sets of electrodes, and in some embodiments, may be configured to assist in determining or selecting a set of one or more electrode sets as the selected electrodes for application of the stimulus to the subject. In some embodiments, the control device 110 may transmit the responses through the network 210 to the cognitive performance monitoring application 225 of the computing device 220 or to the server 230 for analysis of the accurate placement of the electrodes on the subject's head. For example, the control device 110, the computing device 220, and / or the server 230 may be configured to analyze the responses to determine whether the responses meet the conditions associated with a strong or effective electrode placement indicating a preferably arranged array 700. In some embodiments, if the responses do not meet the conditions, the control device may select another set of electrodes, apply the test stimulus again, and measure the associated responses for the purpose of analyzing the responses to determine an appropriate placement of the electrodes. For example, if the analysis is performed by the computing device 220 or the server 230, this may include transmitting appropriate instructions to the control device 110. In some embodiments, the cognitive performance monitoring application 225 may output instructions to the subject or user, for example using the user interface 228, to guide the user to change the placement of the array 700 on the head.

[0169] By enabling the selective selection of a set of electrodes of the array 700 to adapt to different users, the control device 110 and / or the instrument 100 can be adjusted to individualized stimuli for the subject, which can be particularly beneficial in a home environment.

[0170] In some embodiments, a face recognition filter may be used to assist in the reliable or accurate placement of the array 700, and thus the electrodes 120, on the head. In such embodiments, the computing device 220 may include a front camera configured to enable the user to capture an image of themselves. The memory 224 of the computing device 220 may include a positioning feedback module 227 that, when executed by the processor 222, causes the computing device 220 to assist the subject or user in the correct placement of the array 700, and thus the electrodes 120, with respect to their head, based on the captured image or image stream. The positioning feedback module 227 may include a face recognition algorithm that enables face landmarks such as the nose, eyebrows, hairline, and / or other facial features to be determined from the captured image or image stream. Such features may enable the pose and / or structure of the user's face to be determined. The cognitive performance monitoring application 225 may be configured to determine the position of the array 700 of the control device 110, or the pose of the array 700 itself, relative to the determined face features. The cognitive performance monitoring application 225 may also be configured to compare the determined position of the array 700 to an ideal or target position (or range of positions) and, based on that comparison, provide feedback to the subject to guide the repositioning of the array 700 with respect to the subject for the purpose of achieving the target position. For example, the cognitive performance monitoring application 225 may display to the user, via the user interface 228, indicators of the current position of the array and the target position of the array to assist the subject in achieving the desired placement. One advantage of such a positioning feedback module 227 is that it enables a more accurate and reliable placement of the device 100 when used by the user, which can result in improved reliability of stimulation and data capture, particularly in a home environment.

[0171] FIG. 8 depicts a process flow diagram of a method 800 for controlling the delivery of transcranial electrical stimulation according to some embodiments. The method may be performed by a processor 310 of a control device 110 that executes, for example, a feature extraction module 322, an analysis engine 324, and a stimulation control module 326 of a memory 320. In some embodiments, method 800 may be performed by a processor 222 of control device 110 and computing device 220 and / or server 230. FIG. 17 illustrates an overview of method 800 according to some embodiments. While method 800 is being performed, the user may be at rest.

[0172] At 805, the control device 110 transmits or conveys an instruction from the stimulation control module 326 to the electrical stimulation source 350 to cause the electrical stimulation source 350 to deliver electrical stimulation to one or more electrodes 120. The one or more electrodes are disposed or configured for placement on the head of user 115 to deliver transcranial electrical stimulation to a targeted region of the brain of user 115. The instructions transmitted by the control device 110 may include instructions defining one or more of the voltage, current, frequency, duration, and / or offset value of the electrical signal to be applied or delivered to the electrodes. The electrical stimulation may be applied for a preselected length of time or until the stimulation is modified in some other manner. The instructions may include instructions to provide tDCS, tACS, tPCS, tRNS, otDCS, or random noise stimulation, or a combination thereof.

[0173] The instructions may further include instructions to provide relatively short pulses, where each pulse is characterized by an amplitude, frequency, duration, and offset. Each pulse is delivered one at a time. In other embodiments, the instructions may include a single long pulse. In some embodiments, the initial and updated stimulation parameter values for the delivery of electrical stimulation to the user may depend on the particular region of the brain to be targeted and / or the type of task or activity to be performed by the user during the session.

[0174] At 810, the control device 110 receives recorded data from one or more respective optical sensors positioned proximate to a targeted region of the brain. The recorded data may include one or more signals from the one or more optical sensors. The signals may indicate the intensity of reflected light detected by the detector 130B of the optical sensor. In some embodiments, and as discussed above, the optical sensor module 340 may be configured to create a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the detected reflected light signal.

[0175] In some embodiments, after electrical stimulation is delivered to one or more electrodes 120, the optical sensor module 340 records the light response detected by each optical sensor 130 positioned on the head of the user 115 and transmits or provides the recorded data to the control device 110. In some embodiments, the optical sensor module 340 records data, for example, in real time or continuously, before, during, and after the stimulation is applied, and transmits the recorded data to the control device 110 in real time, for processing or at the time of transmission. In some embodiments, the control device 110 may be configured to transmit or stream the recorded data to the server 230 or the computing device 220 for processing. In some embodiments, and as discussed above, the control device 110 may be configured to sample the data at a relatively high frequency and downsample and / or demodulate the data, for example, before transmitting the data to a computing device or server.

[0176] At 815, the control device 110 (or in some embodiments the computing device 220 and / or the server 230) analyzes the recorded data to determine a measure of activity or effectiveness. The recorded data may indicate a change in blood oxygenation.

[0177] In some embodiments, analyzing the recorded data includes removing or reducing the influence of the scalp from the recorded data. This can be achieved, for example, by determining one or more short channels related to or in the vicinity of a candidate long channel and subtracting the signals from the short channels from the long channel. In some embodiments, the signals of all available short channels are subtracted from the candidate long channel. In some embodiments, the signals from only the short channel physically closest to the candidate long channel are subtracted from the signal of the long channel.

[0178] In some embodiments, other signal processing techniques may be used to isolate the effect of the stimulus in the signals from the measured channels. Examples thereof include band-pass filtering of the measurement data, regression of accelerometer data from the signal, and / or regression of baseline fluctuations from the signal.

[0179] In some embodiments, the feature extraction module 322 extracts features or characteristics from the recorded data, or the light response signal, or the processed recorded data. The extracted characteristics may correspond to features or biomarkers related to the user's cognitive function or performance, or cortical activity. The extracted characteristics may be provided as an input to the activity determination model 328, and the activity determination model 328 may provide an activity scale as an output. In some embodiments, the activity scale includes a temporary increase in HbO.

[0180] In some embodiments, and as illustrated in FIG. 17, the control device 110 may determine, from the recorded or measured sensor data, pre-stimulus sensor data (i.e., data acquired before the stimulus is applied), stimulus sensor data (i.e., data acquired during the application of the stimulus), and post-stimulus data (i.e., data acquired after the stimulus is applied). The feature extraction module 322 may determine a set of pre-stimulus features, a set of stimulus features, and a set of post-stimulus features from each respective set of pre-stimulus sensor data, stimulus sensor data, and post-stimulus data. In some embodiments, the features include the functional connectivity between a pair of channels (two regions of the brain) and / or statistical measures of the data acquired from the data channels. In some embodiments, the features are extracted from sensor data obtained from pairs of optical channels around and between the stimulation electrodes.

[0181] The control device 110 may determine a first set of inputs for the activity determination model 328 based on the sets of pre-stimulus and post-stimulus features. The first set of inputs may include the relative change in the feature values from pre-stimulus to post-stimulus. The control device 110 may determine a second set of inputs for the activity determination model 328 based on the sets of pre-stimulus and during-stimulus features. The second set of inputs may include the relative change in the feature values from pre-stimulus to during-stimulus. The control device 110 may determine a third set of inputs for the activity determination model 328 based on the sets of during-stimulus and post-stimulus features. The third set of inputs may include the relative change in the feature values from during-stimulus to post-stimulus.

[0182] The control device 110 may provide the first, second, and third sets of inputs and the applied stimulation parameters (e.g., amplitude) to the activity determination model 328 to be configured to determine an activity metric, such as the probability that sufficient stimulation has been applied.

[0183] In some embodiments, only two of the three sets of inputs contain values for a particular characteristic. For example, the first and second input sets may contain values for a first characteristic, while the third input set does not contain a value for that characteristic.

[0184] In some embodiments, control device 110 determines only two of the first, second, and third input sets, provides only the first and second input sets, the first and third input sets, or the second and third input sets, and provides the applied stimulation parameters to activity determination model 328 to determine an activity metric such as the probability that sufficient stimulation is being applied.

[0185] The activity metric may indicate whether the user's brain activity is determined to be sufficiently active, underactive, overly active, overly responsive, sufficiently responsive, or underresponsive. In some embodiments, the activity metric is compared to one or more activity metric thresholds to determine whether the subject is sufficiently active, underactive, overly active, overly responsive, sufficiently responsive, or underresponsive.

[0186] In some embodiments, the activity metric is compared to a threshold level to determine whether the subject's brain is unresponsive or responsive to the stimulation. At 820, control device 110 may modify the stimulation instruction based on the determined activity metric. In some embodiments, stimulation control module 326 determines one or more stimulation parameter values based on the determined activity metric. The stimulation instruction may include the stimulation parameter values.

[0187] At 825, control device 110 transmits an updated stimulation instruction including the determined one or more stimulation parameter values to the electrical stimulation generator to cause the electrical stimulation generator to modify one or more characteristics of the stimulation.

[0188] In some embodiments, in response to a determination that the activity measure is below a threshold, the control device 110 increases the stimulation parameter value and reapplies the stimulation with this increased stimulation parameter value. In some embodiments, in response to a determination that the activity measure has reached a threshold, the control device 110 determines this stimulation parameter value as the user-specific calibrated stimulation parameter.

[0189] The updated stimulation instructions may include instructions to modify the frequency, amplitude, voltage, and / or current of the electrical stimulation being delivered to the electrodes 120. In an embodiment, if the activity measure indicates that sufficient stimulation has been applied, for example, there has been a change in the user 115's brain activity corresponding to the desired effect on the symptoms of a neurological condition, such as the symptoms of ADHD, the stimulation control module 326 may modify the electrical stimulation by stopping the delivery of the stimulation, i.e., the stimulation parameter value may include zero or other indicators for stopping the stimulation. In some embodiments, the stimulation parameter value generated by the stimulation control module 326 may suggest or instruct the electrical stimulation generator or electrical stimulation source 350 to continue the stimulation at the existing settings for a certain period of time, or to modify the characteristics of the electrical stimulation to target a region of the brain and / or to induce a different response.

[0190] If the desired activity level is not met, the stimulation parameter value generated by the stimulation control module 326 may suggest or instruct the electrical stimulation generator or electrical stimulation source 350 to continue the delivery of the current stimulation level, increase the level of the stimulation, decrease the level of the stimulation, or modify the characteristics of the applied stimulation. The desired activity level or activity level threshold may depend on task information such as the type and duration of the task or activity being performed or to be performed by the user during the session.

[0191] For example, prior to the application of a stimulus, the control device 110 may determine an average oxygenation concentration change of 2 micromoles in amplitude from measurement data related to the optical signals from each of the one or more sensors 130. When the stimulus is applied, the control device 110 may determine that the brain activity of the subject has increased to a change in oxygenation concentration of 3 micromoles in amplitude (based on the measurement data). In this example, the effect of the stimulus on the subject may be a change in blood oxygenation concentration of 1 micromole in the measured area. In some embodiments, this increase may be accompanied by an increase in the performance of a particular task, such as an increase in accuracy and / or reaction time. The activity determination model 328 may use, as inputs, the amplitude of the blood oxygenation concentration and optionally the task score achieved while performing a task while the sensor data is being recorded, to determine an activity measure. If the resulting activity measure meets an activity threshold, the control device 110 may determine that sufficient stimulation has been delivered, continue to deliver an appropriate level of stimulation for a period of time, and instruct the stimulator to stop delivering the stimulation at a predetermined point in time. On the other hand, if the resulting activity measure does not meet the activity threshold, the control device 110 may determine that continuous stimulation (at existing parameter values or modified parameter values) is required and accordingly instruct the stimulator to maintain or modify the stimulation.

[0192] In some embodiments, the optical sensor 130 may continuously measure or monitor the light response from the user 115 throughout the session, regardless of whether an electrical stimulus is being applied. In such embodiments, the optical sensor 130 may detect a change in the brain activity of the user 115 indicating that an electrical signal may need to be reapplied, such as when the brain activity of the user in a targeted area is below a threshold activity level. Thus, a user 115 using the device for a period of time may activate the device to initiate a stimulation session, an electrical stimulus may be applied via the electrodes 120 for a period of time until the threshold activity level is met, and after the beneficial effects on the symptoms of the neurological state of the first electrical stimulus, such as symptoms of ADHD, are no longer detected, a further electrical stimulus may be applied via the electrodes 120.

[0193] In some embodiments, method 800 may be used to calibrate device 100 for a particular subject. For example, the activity metric determined at (815) may be compared to a calibration threshold level to determine whether the subject's brain is not responding to the stimulus. In response to determining that the brain is not responding, control device 110 may be configured to modify the stimulus instruction (at 820) based on the determined activity metric. For example, control device 110 may transmit an updated stimulus instruction (at 825) that includes the determined stimulus parameter value to the electrical stimulator, and the electrical stimulator may be configured to modify one or more characteristics of the stimulus. In some embodiments, the stimulus parameter value may be a fixed increment of the stimulus to be applied. The activity metric is determined again (at 815), and the stimulus instruction may be updated (e.g., increased stimulus) until sufficient stimulus is considered to have been applied or until the electrical stimulator reaches its maximum safety limit. In some embodiments, when sufficient stimulus is considered to have been applied or the electrical stimulator reaches its maximum safety limit, the control device may instruct the electrical stimulator to continue delivering a simulation considered sufficient for the session, continue delivering a simulation considered sufficient for a particular time window during the session, or stop delivering the stimulus to the subject.

[0194] Such a calibration process may be performed at the start of a session with the subject, thereby calibrating the device for a particular subject and taking into account any factors that may affect or change how well the current is received by the subject, such as skin oiliness, electrode conductivity, hair growth / thickness / style, etc., which may vary from session to session.

[0195] In some embodiments, the control device 110 can be activated or started by activating the HMI 355 switch. In some embodiments, the control device 110 can be activated or started by a cognitive performance monitoring application 225 executed by the processor 222 of the computing device 220. In such embodiments, the user 115 can initiate an instruction using the user interface 228 of the computing device 220 while using the application 225. In some embodiments, the control device comprises a user interface 360 configured to enable a user to provide inputs for activating / deactivating and / or controlling the operation of the control device and the overall system. The user interface 360 can also comprise a display or audio output for relaying information about the operation of the control device 110 to the user.

[0196] In some embodiments, the control device 110 can be configured to transmit data associated with the detected brain activity 110, via the communication module 330, to the computing device 220 associated with the user or to the server 230 for processing or storage. This data can include criteria related to the applied electrical stimulation, the optical response of the detected brain activity, measurement data derived from the optical response, the detection of one or more biomarkers, or other data related to the stimulation session. In some embodiments, the application 225 can store this data in the memory 224 of the computing device 220, for example, to be accessed by the user 115 and displayed on the user interface 228 of the computing device 220. The application 225 can also display the stimulation history, the performance of past mental measurement tests, or other information related to the stimulation session. In such embodiments, the data can be received from the control device 110 or obtained from the database 240 through the network 210.

[0197] In some embodiments, a treatment session can be induced by the user activating the HMI switch 355, or other activation mechanism provided on the control device 110 or the device 100. In some embodiments, the cognitive performance monitoring application 225 can cooperate with the control device 110 to induce the start of a session. For example, the user or clinician can interact with the cognitive performance monitoring application 225 to start a session. In some embodiments, the cognitive performance monitoring application 225 transmits task data to the control device 110, for example, to a computing device 220 associated with a clinician, or to a server 230. The task data can include information about the type of task being performed or to be performed by the user. The task data can include one or more scores achieved by the user when performing a particular task.

[0198] In some embodiments, the task data can include one or more scores achieved by the user when performing a task, and can be used in combination with measurement data by the control device 110 to infer the behavioral progress of the subject. In other embodiments, the task data, including the recorded data and / or activity metrics and scores, can be transmitted to a server, such as a remote server, for processing to infer the behavioral progress of the subject.

[0199] FIG. 10 depicts a process flow diagram of a method 1000 for inferring symptom severity and / or behavioral progress of a subject undergoing treatment for one or more symptoms of a neurological condition, according to some embodiments. In some embodiments, the method 1000 can be executed by the control device 110. In other embodiments, the method 1000 can be executed by the control device 110 in cooperation with the server 230 and / or the computing device 220. FIG. 18 illustrates an overview of the method 1000, according to some embodiments.

[0200] At 1005, the control device 110 may cause the stimulation control module 322 to send or transmit an instruction to deliver an electrical stimulation from the electrical stimulation source 350 to one or more electrodes 120. The one or more electrodes 120 are arranged or configured for placement on the head of the user 115 to deliver transcranial electrical stimulation to a targeted region of the brain of the user 115. The instruction transmitted by the control device 110 may include an instruction defining one or more of the voltage, current, frequency, duration, and / or offset value of the electrical signal to be applied or delivered to the electrodes. The electrical stimulation may be applied for a preselected length of time or until the stimulation is modified in some other manner. The instruction may include an instruction to provide tDCS stimulation, tACS stimulation, or random noise stimulation, or a combination thereof. As discussed above with reference to process 800, the instruction may include an instruction to provide short pulses or longer pulses. However, in some embodiments of method 1000, delivering electrical stimulation to the subject is not essential, and the symptom severity and / or progression measure may be determined based on task data and sensor data only.

[0201] At 1010, the control device 110 receives or determines data recorded from one or more respective optical sensors positioned proximate to the targeted region of the brain. The recorded data may include one or more signals from the one or more optical sensors. The signal may indicate the intensity of the reflected light detected by the detector 130B of the optical sensor. The recorded data may be recorded when no stimulation is being applied (i.e., there is no applied stimulation), after stimulation has been applied, or simultaneously while stimulation is being applied. In some embodiments, and as discussed above, the optical sensor module 340 may be configured to create a lock-in amplifier effect to improve the signal-to-noise ratio (SNR) of the detected reflected light signal.

[0202] At 1015, the control device 110, the computing device 220, and / or the server 230 determine task data. For example, the task data can be determined by the cognitive performance monitoring application 225. The task data can include one or more scores related to the performance of a user when performing or participating in each of one or more tasks. For example, suitable types of tasks include psychometric tasks, or tests that measure executive function performance such as working memory, impulse control, cognitive flexibility, etc., and further include the Stroop task, the Wisconsin Card Sorting task, the Corsi block test, the go - no - go task, the continuous performance task, the N - back task, etc. The psychometric task or test can be a modified or gamified version of a standard test. In some embodiments, the task data is determined substantially simultaneously with the recorded data from one or more respective optical sensors (1005).

[0203] In some embodiments, the task data can include scores or criteria for one or more behavioral or characteristic symptoms such as accuracy, reaction time, omission errors, and / or commission errors. The system can determine task characteristic values based on the criteria of the task data. The task characteristic values can be statistical measures of the criteria such as the average value and / or standard deviation value of accuracy, reaction time, omission errors, and / or commission errors.

[0204] In some embodiments, the characteristic values derived from the task data criteria for reaction time can be used by the symptom severity and / or progression determination model 328 to determine the primary ADHD core symptom score or scale, the inattention severity score, the impulsivity severity, and / or the hyperactivity severity score.

[0205] At 1020, the control device 110, the computing device 220, or the server 230 determines a symptom severity or a progression scale based on the recorded data and the task data. In embodiments where the server 230 or the computing device 220 determines the symptom severity or the progression scale, each server 230 or computing device 220 may be configured to determine or receive the recorded data from the control device 110. In some embodiments, the control device 110 may be configured to transmit or stream the recorded data to the server 230 or the computing device 220. In some embodiments and as discussed above, the control device 110 may be configured to sample data at a relatively high frequency and, in response, downsample and / or demodulate the data, for example, before transmitting the data to a computing device or a server.

[0206] The symptom severity and / or progression measure may include one or more scores for each of one or more behaviors or characteristics of a neurobehavioral disorder such as ADHD. For example, the symptom severity or progression determination model 238 may be configured to provide a score for one or more of (i) a comprehensive ADHD evaluation scale score, (ii) an ADHD core symptom score, (iii) an inattention score, (iv) a hyperactivity score, and (v) an impulsivity score. In such an example, the symptom severity or progression determination model 238 may be trained using labeled data tasks and sensor data labeled by scores determined from an ADHD evaluation scale questionnaire. For example, a clinical population may be asked to fill out a standard ADHD evaluation scale questionnaire that can be used to determine scores for multiple ADHD symptoms such as (i) a comprehensive ADHD evaluation scale score, (ii) an ADHD core symptom score, (iii) an inattention score, (iv) a hyperactivity score, and (v) an impulsivity score. The task data and associated sensor data are determined for the clinical population and labeled according to the scores determined for the associated participants. The task data, sensor data, and labels are then used to train the symptom severity or progression determination model 238. Accordingly, the symptom severity or progression determination model 238 can be used as an automated symptom severity or progression measure or monitoring tool for measuring or monitoring neurobehavioral disorders.

[0207] In some embodiments, the control device 110, the server 230, or the computing device 220 analyzes recorded data including intensity signals to determine measurement data. For example, the measurement data may include recorded data that includes one or more of (i) oxygenated hemoglobin (HbO) concentration, (ii) deoxygenated hemoglobin (HbR) concentration, and total hemoglobin (HbR) concentration, and / or data derived from the recorded data. Exemplary plots of HbO, HbR, and HbR concentrations are illustrated in FIG. 11.

[0208] In some embodiments, control device 110 is configured to receive sensor data from a plurality of channels, each channel corresponding to a pair of a light emitter and a detector of optical sensor 130. As discussed above, one or more of the channels are relatively short channels in which the light emitter is disposed near each detector, and one or more of the channels are relatively long channels in which the light emitter is disposed at a relatively far distance from each detector.

[0209] The long channels are configured to measure cerebral blood oxygenation at an intermediate point between each pair of light emitter and detector. The short channels are configured to measure cerebral blood oxygenation in the scalp or scalp region near the subject at an intermediate point between the pair of light emitter and detector. In some embodiments, only the signals from the long channels are used.

[0210] In some embodiments, control device 110, computing device 220, or server 230 is configured to screen useful biological information provided by optical sensor 130. For example, control device 110, computing device 220, or server 230 may be configured to determine whether the signals from each channel are of sufficient quality. Channels determined to be ineffective and / or of insufficient quality in deriving useful information (i.e., "bad" channels) may be excluded from further analysis. In some embodiments, a quality measure indicative of the quality of each detector channel of the optical sensor is determined, and in response to a quality measure below a quality threshold, the sensor data from each detector channel is excluded or ignored when determining symptom severity or progression measure.

[0211] In some embodiments, a scalp coupling index (SCI) is determined for each of the channels. The SCI is a measure of the quality of the signal for a channel over a particular measurement duration. In response to the SCI falling below a threshold SCI value, the control device 110, the computing device 220, or the server 230 may each be configured to determine that the respective channel is "bad" and exclude measurements from that channel for further analysis. In some embodiments, the control device 110, the computing device 220, or the server 230 may be configured to determine saturation of the detector. For example, this saturation may be determined by detecting whether a voltage measurement is outside an acceptable range, such as -1.2V to 1.2V. In response to the saturation of the detector being determined, the control device 110, the computing device 220, or the server 230 may each be configured to determine that the respective channel is "bad" and exclude measurements from that channel for further analysis. In some embodiments, the control device 110, the computing device 220, or the server 230 may perform motion artifact correction on the signals received from the channels to reduce motion artifacts. For example, such motion artifact correction may be configured to model the motion using spline interpolation and subtract them from the respective signals. In some embodiments, a wavelet-based method is used.Further details of suitable spline interpolation and wavelet-based methods can be found in the papers by Scholkamm et al., "How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation" (https: / / pubmed.ncbi.nlm.nih.gov / 20308772 / ), and by Molavi et al., "Wavelet-based motion artifact removal for functional near-infrared spectroscopy" (https: / / iopscience.iop.org / article / 10.1088 / 0967-3334 / 33 / 2 / 259 / meta?casa_token=s1IqbEC3gYQAAAAA:gbJBtl-KCd_xpeG2oKUflnjnh5BHdGFR7UqQWmmjNjghCDwWTpWLx7j9NI99HboeTw5zosE80A), both of which are hereby incorporated by reference in their entirety.

[0212] In some embodiments, the control device 110, the computing device 220, or the server 230 may perform bandpass filtering on a plurality of channels to remove unimportant information.

[0213] In some embodiments, the control device 110, the computing device 220, or the server 230 may further process the information from the channels to remove other artifacts such as known artifacts from the recorded data, for example, increasing or maximizing the prominence of the hemodynamic response in the observed data. This can be achieved by performing regression using polynomial drift, short channel data, and accelerometer data. For example, the device 100 and / or the control device 110 may include an accelerometer (not shown) for capturing accelerometer data. The polynomial drift can be calculated by fitting a polynomial to the data, and the regression may include quantifying how much each time series (e.g., short channel, accelerometer, polynomial drift) contributes to the long channel data being measured and subtracting it.

[0214] The control device 110, the computing device 220, or the server 230 may be configured to separate the sensor data from each channel into sensor data associated when the subject was at rest or not performing a task (data under resting conditions) and sensor data associated when the subject performed a task (data under task conditions).

[0215] This can be achieved by considering the timestamps associated with the data. For example, in some embodiments, the task data may include one or more timestamps (e.g., task-related timestamps) associated with the subject performing each of the one or more operations related to the task. The sensor data may include time-series data or timestamped data. The control device 110 (or the server 230 or the computing device 220) may be configured to determine the symptom severity or progression measure by associating one or more subsets of the sensor data with the respective task data based on timing. In some embodiments, the task data may be timestamped according to an interaction or event such as a recorded button press or a task displayed to the subject. The task-related timestamp may be an additional timestamp relative to the time-series timestamp that may be associated with the determined sensor data.

[0216] In some embodiments, the control device 110, the server 230, or the computing device 220 (e.g., the cognitive performance monitoring application 225) may be configured to timestamp a section or subset of the sensor data using the task-related timestamp. For example, the control device 110 or the server 230 may be configured to timestamp the sensor data in response to receiving a timestamp instruction from the cognitive performance monitoring application 225.

[0217] In some embodiments, the control device 110 provides or streams sensor data to the computing device 220. When a task or task-related event occurs, the cognitive performance monitoring application 225 stamps the sensor data with respective task or event-related timestamps. This enables the sensor data to be associated with task data that occurs at a specific time, such as when a stimulus event or task event occurs, for example, an image is displayed on the user interface of the computing device for the subject, or the subject performs a specific task or action. Since biologically-derived data that is stamped and applied to human behavior tends to be inherently more informative than raw biologically-derived data, this can enable improved ease of data collection and / or improved accuracy of result analysis.

[0218] The control device 110, the computing device 220, or the server 230 may be configured to determine a representative response for each channel. For example, the representative response may indicate activation during a task as a function of measurement data or sensor data related to the execution of the task. For example, the response for a channel (e.g., a hemodynamic response) may be the average of measurement data or sensor data related to the execution of the task recorded or measured by that channel (e.g., blocks of data under task conditions). Thus, a representative response may be determined for each of the plurality of channels considered, which may be long channels.

[0219] In some embodiments, the feature extraction module 322 extracts features or characteristics from the recorded data or measurement data. For example, the feature extraction module 322 may extract features or characteristics, such as HbO amplitude or area under the curve, from the representative responses of one or more channels, as described above. The extracted features may correspond to features or biomarkers related to the user's cognitive function or performance or cortical activity. The extracted features, along with the scores, may be provided as inputs to the symptom severity (or progression) determination model 327, and the symptom severity or progression determination model 327 may provide a symptom severity or progression scale as an output. The progression scale may indicate the progression that the subject is presenting in the treatment of the symptoms of the neurological condition.

[0220] In some embodiments, the feature extraction module 322 may be configured to determine a characteristic value indicative of or including the functional connectivity between pairs of optical sensor channels, and / or a statistical measure of the data obtained from the optical sensor channels.

[0221] In some embodiments, the characteristic value derived from the sensor data obtained from the optical channels configured to measure the activity in the right lateral prefrontal cortex is used by the symptom severity and / or progression determination model 328 to determine the overall ADHD symptom severity scale.

[0222] In some embodiments, the characteristic value derived from the sensor data obtained from the optical channels configured to measure the activity in the right lateral prefrontal cortex is used by the symptom severity and / or progression determination model 328 to determine the primary ADHD core symptom score or scale.

[0223] In some embodiments, characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the medial prefrontal cortex of a subject, and in some examples, the medial prefrontal cortex facing, overlapping, or adjacent to the left lateral prefrontal cortex of the subject, are used by the symptom severity and / or progression determination model 328 to determine a severity scale of inattention.

[0224] In some embodiments, characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the right lateral prefrontal cortex, left lateral prefrontal cortex, and / or an area overlapping the left lateral prefrontal cortex and the medial prefrontal cortex of a subject are used by the symptom severity and / or progression determination model 328 to determine a severity scale of hyperactivity.

[0225] In some embodiments, characteristic values derived from sensor data obtained from an optical channel configured to measure activity in the medial prefrontal cortex of a subject and / or the medial prefrontal cortex facing, overlapping, or adjacent to the right lateral prefrontal cortex of the subject are used by the symptom severity and / or progression determination model 328 to determine a severity scale of impulsivity.

[0226] At 1025, the control device 110, server 230, or computing device 220 outputs a symptom severity or progression scale. For example, the control device 110 or computing device 220 may output the symptom severity or progression scale by providing the symptom severity or progression scale to the user via the user interface 360, or the control device 110 or server 230 may transmit the symptom severity or progression scale to, for example, the cognitive performance monitoring application 225 of the user's computing device 220, or to the clinician's computing device, to the server 230, or to the database 240.

[0227] In some embodiments, step 1015, step 1020, and / or step 1025 may be performed by server 230. In some embodiments, step 1015, step 1020, and / or step 1025 may be performed by computing device 220.

[0228] The first investigation was conducted for the purpose of determining one or more characteristics that can be extracted from sensor data that are corresponding to or relatively strong indicators of features or biomarkers related to the user's cognitive function, cognitive performance, or cortical activity in response to the applied stimulus.

[0229] Sixteen participants or subjects were involved in this investigation. A headset or device 100 (or an array 700 of devices 100) was placed on the forehead of each subject. Device 100 extended between the eyebrows and the hairline and across the temples. Conductive sponges (electrodes 120) were fitted to device 100 to deliver current to the subject's head, particularly to the prefrontal cortex. The subjects were instructed to lie down with their eyes closed (known as the resting state measurement). A control device 110 was used to deliver stimuli from an electrical stimulator 350 to the conductive sponges and thus to the subject's prefrontal cortex. In particular, control device 110 was used to deliver stimuli of various intensities without interruption without current between each stimulation session. For example, for many subjects, eight stimulation and recording sessions were conducted. The first session included a period without stimulation (e.g., 2 to 8 minutes) followed by stimulation with a current of 0.25 mA during an application period (e.g., 2 to 8 minutes). The second session included a period without stimulation followed by stimulation with a current of 0.5 mA during an application period. The third session included stimulation with a current of 0.75 mA during the application period. The fourth session included a period without stimulation followed by stimulation with a current of 1.0 mA during an application period. The fifth session included a period without stimulation followed by stimulation with a current of 1.25 mA during an application period. The sixth session included a period without stimulation followed by stimulation with a current of 1.5 mA during an application period. The seventh session included a period without stimulation followed by stimulation with a current of 1.75 mA during an application period. The eighth session included a period without stimulation followed by stimulation with a current of 2.0 mA during an application period. Sensor data was recorded from the sensors of device 100 between each session, thereby generating a sensor data set including a set of sensor data for each stimulation current setting from 0.25 mA to 2.0 mA in 0.25 mA steps with respective application periods and rest periods.

[0230] A training dataset was generated from the recorded sensor dataset to train a logistic regression model (sigmoid model), i.e., the activity determination model 328. The training dataset included a set of first examples, each of which corresponded to sensor data recorded during a first stimulation session (i.e., a stimulation current of 0.25 mA). The set of first examples was labeled as "insufficient", i.e., insufficient to elicit a sufficient response from the brain or to generate an insufficient activity measure. The training dataset included a set of second examples, each of which corresponded to sensor data recorded during eight stimulation sessions (i.e., a stimulation current of 2.0 mA). The set of second examples was labeled as "sufficient", i.e., sufficient to elicit a sufficient response from the brain or to generate a sufficient activity measure. The training dataset was used to train the activity determination model 328.

[0231] The coefficients of the characteristics were determined by training and testing the activity determination model 328 for 1000 different random samplings of the training set and then averaging the values.

[0232] Once the model was trained, relevant characteristic values were extracted from the remaining examples of sensor data obtained during the second through seventh stimulation sessions to predict the probability that a sufficient stimulus was delivered to each subject for each of the stimulation currents. The average results for all subjects are illustrated in FIG. 15, which is a plot of the probability of a sufficient stimulus versus the stimulation current (mA).

[0233] The acquired sensor data undergoes processing and signal enhancement using standard techniques, including removal of short channels and removal of the accelerometer, as discussed above. The sensor data is divided into three time blocks: before stimulation, during stimulation, and after stimulation. For each time block, the following statistical values are calculated for both Hbr data and HBO data.

[0234] For all fNIRS long channels, (i) the mean value (raw_mean), (ii) the standard deviation (raw_std), (iii) the mean value of the derivative (diff_mean), and (iv) the standard deviation of the derivative (diff_std). For all pairs of fNIRS long channels, (i) the correlation coefficient (raw_corrcoef) (Note: This is used to determine functional connectivity).

[0235] The changes in these statistical values between different time blocks are used to generate features. The three changes used are (i) from before the stimulus to during the stimulus (before_to_during_change), (ii) from before the stimulus to after the stimulus (before_to_after_change), and (ii) from during the stimulus to after the stimulus (during_to_after_change).

[0236] All features for each long channel are collected and tested for significant correlation with the current of the stimulus used. In the activity determination model 328, only features with a correlation with a p-value < 0.005 were used.

[0237] The characteristics used as inputs to a model that were extracted from the sensor data of these examples and found to be good predictors of activity are shown in Table I, along with the mean values of the weights or coefficients determined by training the model. Table I further includes the standard deviation, t-score, and coefficient of variation (CoV) values of the coefficient values of the characteristics. The characteristics in Table I are arranged in order from the most predictive characteristic, i.e., the characteristic that best represents the activity level of the subject, to the least predictive characteristic in the set of those characteristics. All characteristics with positive coefficient values have a positive correlation with the activity scale, and the characteristic with a negative coefficient value (“S2D6 hbr S3D8 hbr raw corr coeff during to after change”) has a negative correlation with the activity scale. According to the results in Table I, the characteristic “S3D4 HbR raw std before to after change” best indicates whether a sufficient stimulus has been applied and is a highly reliable characteristic suitable for predicting the probability of whether a sufficient stimulus has been applied. The characteristic “S3D hbo S4D8 hbo raw corr coef before to during change” is a high-performance characteristic that indicates whether a sufficient stimulus has been applied to the subject and is a highly reliable characteristic suitable for predicting the probability of whether a sufficient stimulus has been applied.

[0238] Any one or any combination of the characteristics in Table I can be used to predict the probability of whether a sufficient stimulus has been applied. Such characteristics can be used to train the activity determination model 328. Once trained, by providing the values of the characteristics extracted from the sensor data as inputs to the activity determination model, the activity determination model 328 becomes the activity scale of the activity determination model 328 for the subject.

Table 1

[0239] Table II below provides some descriptions of the terms used for the characteristics in Table 1. Although not all of the characteristics from Table I are included in Table II, it will be understood that the descriptions provided as explanations of the terms used to define the characteristics may equally apply to the other characteristics in Table I.

Table 2

[0240] Regarding the characteristic "S3D4 HbR raw std before to after", which is a measure of the change in the standard deviation of deoxygenated blood from before to after the application of stimulation in the channels around the stimulating cathode (negative stimulating electrode), a positive correlation with the activity scale was found. This could be due to an increase in stimulation that causes an increase in the negative current at the cathode. The negative current at the cathode can cause a decrease in the activity around the cathode and, therefore, an increase in the activity of deoxygenated blood.

[0241] Regarding the characteristics "S4D8 HbO S5D8 HbO raw corr coef before to during" and "S3D8 HbO S4D8 HbO raw corr coef before to during", both of these characteristics indicate the functional connectivity of oxygenated blood within the brain region at the midpoint between the anode (positive) and cathode (negative) of the stimulating electrodes, and a positive correlation with the activity scale was found. When the current moves from the anode to the cathode, the increase in functional connectivity conveys that increased synchronous activity occurs at the point between the two electrodes. This is evident when comparing the pre-stimulation functional connectivity with that during stimulation, indicating a high likelihood that the increase in connectivity is due to the increase in activity caused by the stimulation and has a positive correlation with the stimulation intensity.

[0242] Referring to FIGS. 7a and 7b, the positions of the pairs of sensors and detectors referred to in Tables II, I, and II can be readily understood.

[0243] In some embodiments, the characteristics of the activity determination model 328 are extracted from sensor data obtained from the left lateral prefrontal cortex of the subject. For example, the device 100 may be positioned on the subject's head such that a first sensor module (including sensor S2 and sectors D3 and D4) and a second adjacent sensor module (including sensor S3 and detectors D5 and D6) measure or record data from the left lateral prefrontal cortex of the subject. For example, the sensor data obtained from the left lateral prefrontal cortex of the subject may include sensor data determined from one or more of a long channel between a first sensor (S2) and a first detector (D4) of the first sensor module, a long channel between a second sensor (S3) and a second detector (D6) of the second adjacent sensor module, a long channel between the first sensor (S2) of the first sensor module and the second detector (D6) of the second sensor module, and a long channel between the second sensor (S3) of the second sensor module and the first detector (D4) of the first sensor module.

[0244] In some embodiments, the characteristics of the activity determination model 328 are extracted from sensor data obtained from the medial prefrontal cortex of the subject. For example, the device 100 may be positioned on the subject's head such that a third sensor module (including sensor S4 and sectors D7 and D8) and a fourth adjacent sensor module (including sensor S5 and detectors D9 and D10) measure or record data from the medial prefrontal cortex of the subject. For example, the sensor data obtained from the medial prefrontal cortex of the subject may include sensor data determined from one or more of a long channel between a first sensor (S4) and a first detector (D8) of the third sensor module, and a long channel between a second sensor (S5) of the fourth adjacent sensor module and the first detector (D8) of the third sensor module.

[0245] In some embodiments, the characteristics of the activity determination model 328 are extracted from sensor data obtained from the left lateral prefrontal cortex, the medial prefrontal cortex, and / or the boundary between the medial prefrontal cortex and the left lateral prefrontal cortex of the subject. In some embodiments, the sensor data can be obtained from one or more of the long channels between the second sensor (S3) of the second sensor module and the first detector (D8) of the third sensor module, and the long channels between the first sensor (S4) of the third sensor module and the second detector (D6) of the second sensor module. In some embodiments, the sensor data can be obtained from one or more of channels S2D4, S2D6, S3D4, S3D6, one or more of channels S4D8 and S5D8, and optionally one or more of S3D8 and S4D6.

[0246] As shown in FIG. 7b, the different regions have some overlap. For example, S4D6 and S3D8 are boundary channels between the medial prefrontal and the left prefrontal, and can thus be considered part of either / both regions. This applies to all places where the circles intersect in FIG. 7b.

[0247] The second investigation was conducted for the purpose of determining one or more characteristics that can be extracted from sensor data and / or task data that are characteristic of, or correspond to, or are relatively strong indicators of, the cognitive function or cognitive performance or cortical activity of the subject while the subject is receiving or performing a particular task.

[0248] Ten participants or subjects with ADHD were involved in this study. The headset or device 100 (or array 700 of devices 100) was placed on each subject's forehead, between the eyebrows and hairline, and from temple to temple. The headset or device 100 did not have conductive sponges and no stimulation was applied. In this study, the device 100 was only used to record the brain responses or cortical activities while the subject was performing a task or test, using the optical sensor 130 including the fNIRS sensor.

[0249] Each subject answered an ADHD rating scale questionnaire. The ADHD rating scale questionnaire includes a set of 18 questions often given by psychiatrists to assist in the diagnosis of ADHD. Specific questions are designed to evaluate various symptoms of ADHD and to help identify the type and / or degree of ADHD that the person has, which can be either inattentive type, hyperactive type or combined type. This scale is a self-report scale in which the patient can fill in on a scale from "not at all" to "frequently" how often and how severely they are affected by common symptoms. The answers to the questionnaire provided by the subjects were used to assign to the subjects a set of determined symptom severity or progress scores. For example, each set of determined progress scores included a total or comprehensive ADHD rating scale score, an ADHD core symptom score, an inattention score, a hyperactivity score, and an impulsivity score.

[0250] The subjects were asked to perform a first cognitive performance (executive function) task called the "Go-No / Go task", a well-known test designed to test the subjects' impulse control and attention. The subjects were also asked to perform a second performance (executive function) task called the "N-Back task", a well-known test designed to test the subjects' working memory and attention.

[0251] In this investigation, for each condition of the Go-No / Go task and the N-back task, the following criteria were calculated using the recorded button press information. a. Accuracy - Mean (accuracy_mean) b. Accuracy - Standard Deviation (accuracy_std) c. Reaction Time - Mean (reaction_time_mean) d. Reaction Time - Standard Deviation (reaction_time_std) e. Omission Errors - Mean (omission_errors_mean) f. Omission Errors - Standard Deviation (omission_errors_std) g. Commission Errors - Mean (commission_errors_mean) h. Commission Errors - Standard Deviation (commission_errors_std)

[0252] A set of task scores for each task performed was generated for each subject. For example, the task scores for the first and second performance tasks included values for reaction time, accuracy, omission errors, and commission errors.

[0253] For each subject, a first set of sensor data was recorded while the subject was performing the first performance task, and a second set of sensor data was recorded while the subject was performing the second performance task.

[0254] The sensor data was subjected to processing and signal enhancement including removal of short channels and removal of accelerometers, as discussed above.

[0255] Sensor data was averaged over all repetitions of each experimental condition. For Go No / Go, the conditions were (go, gonogo), and for N-back, the conditions were (0-back, 1-back, 2-back). Sensor data was averaged over each region of interest. In other words, data from specific sensor channels was grouped into regions, as will be considered in more detail below. For each region of interest, the following statistical values were calculated for both Hbr data and HBO data. a. Mean value (raw_mean) b. Standard deviation (raw_std) c. Maximum value (raw_max) d. Minimum value (raw_min) e. Coefficient fitted to the general linear model (theta)

[0256] An example training data set was generated from sensor data, a set of task scores, and a determined progression score (label). The training data set was used to train a linear model, i.e., a symptom severity (or progression) determination model 327. The symptom severity determination model 327 was configured to receive, as input, characteristics from sensor data obtained while a first performance task was being performed, characteristics from sensor data obtained while a second performance task was being performed, a task score from the first performance task, and a task score from the second performance task, and to provide, as output, a measure of progression or symptom severity, or a symptom severity measure for each category of symptoms (e.g., a total ADHD score, an ADHD core symptom score, an inattention score, a hyperactivity score, an impulsivity score).

[0257] The symptom severity (or progression) determination model 327 was trained on 75% of the data in the training dataset and tested on the remaining 25%. This was done 1000 times for various samplings of the training data and test data. All characteristics for each of the long channels and task characteristics were collected and tested for significant correlation with the symptom severity scale. Only the top 10 characteristics with the highest correlations among all available characteristics were used in the symptom severity (or progression) determination model 327.

[0258] Some of the high-performance characteristics extracted from the set of example sensor data and / or set of task scores and used as inputs to the model, along with the average values of the weights or coefficients determined by training the model, are shown in Tables III - VII below. These tables further include the values of the standard deviation, t-score, coefficient of variation (CoV) of the coefficient values, and p-value for the characteristics.

[0259] Referring again to FIGS. 7a and 7b, the regions "left_3", "left_2", "left_1", "mid", "right_1", "right_2", "right_3" described in the following table refer to the following. · left_3 = S1D2, S2D2, S1D4 · left_2 = S2D4, S3D4, S2D6 · left_1 = S3D6, S4D6, S3D8 · mid = S4D8, S4D10, S5D8, S5D10 · right_1 = S6D10, S5D12, S6D12 · right_2 = S7D12, S6D14, S7D14 · right_3 = S8D14, S7D16, S8D16

Table 3

[0260] Table III shows in a table the top 10 characteristics determined to be good predictors of the comprehensive ADHD symptom severity scale. The first characteristic ("1-back reaction time std") and the third characteristic extracted from the task score data have a positive correlation with the symptom severity scale. The fourth, seventh, ninth, and tenth characteristics (all extracted from sensor data) have a positive correlation with the symptom severity scale, and the other four characteristics extracted from sensor data have a negative correlation with the comprehensive ADHD symptom severity scale. Therefore, characteristics from both sensor data and task data are used as inputs to the trained symptom severity determination model 327 to determine the comprehensive ADHD symptom severity scale for a subject. Among the available task data, the criteria for reaction time and omission have the strongest (positive) correlation with the comprehensive ADHD symptom severity scale. Among the available sensor data, it is noteworthy that the channels (S7D12, S6D14, S7D14) configured to measure the activity in the right lateral prefrontal cortex have the strongest (negative) correlation with the comprehensive ADHD symptom severity scale. [Table 4]

[0261] Table IV shows in a table the top 10 characteristics determined to be good predictors of the primary ADHD core symptom score. The first, third, fourth, seventh, and eighth characteristics extracted from sensor data have a negative correlation with the primary ADHD core symptom score, and the other characteristics extracted from sensor data have a positive correlation with the primary ADHD core symptom score. Therefore, only the characteristics from sensor data are used as inputs to the trained symptom severity determination model 327 to determine the primary ADHD core symptom score for a subject. Among the available sensor data, it is noteworthy that the channels (S7D12, S6D14, S7D14) configured to measure the activity in the right lateral prefrontal cortex have the strongest (negative) correlation with the primary ADHD core symptom score. [Table 5]

[0262] Table V shows in a table the top 10 characteristics determined to be good predictors of inattentive symptoms. The 1st, 3rd, 7th, and 8th characteristics (all extracted from sensor data), and the 2nd, 5th, and 10th characteristics (extracted from task score data) have a positive correlation with inattentive symptoms, and all other characteristics extracted from sensor data have a negative correlation with inattentive symptoms. Therefore, characteristics from both sensor data and task data are used as inputs to the trained symptom severity determination model 327 to determine the inattentiveness severity scale for the subject. Among the available task data, the criteria for reaction time and omission errors have the strongest (positive) correlation with inattentive symptoms. It is worth noting that among the available sensor data, the channels (S6D10, S5D12, S6D12) configured to measure the activity in the medial prefrontal cortex of the subject, which overlaps or touches, and in some cases is directed towards the right lateral prefrontal cortex of the subject, have the strongest correlation with inattentive symptoms.

Table 6

[0263] Table VI shows in a table the top 10 characteristics determined to be good predictors of symptoms of hyperactivity. The 1st, 2nd, 4th, 5th, 6th, 8th, and 10th characteristics (all extracted from sensor data) are negatively correlated with symptoms of hyperactivity, and the other characteristics extracted from sensor data are positively correlated with hyperactivity. Therefore, characteristics from sensor data only are used as inputs to the trained symptom severity determination model 327 to determine the severity scale of hyperactivity for the subject. Of the available sensor data, channels configured to measure activity in the right lateral prefrontal cortex (S7D12, S6D14, S7D14) and in the left lateral prefrontal cortex, and / or in regions overlapping or adjacent to the left lateral prefrontal cortex and the medial prefrontal cortex (S2D4, S3D4, S2D6) have the strongest (negative) correlation with the severity scale of hyperactivity, which is worthy of note. [Table 7]

[0264] Table VII shows in a table the top 10 characteristics determined to be good predictors of symptoms of impulsivity. The 1st characteristic (extracted from task score data), the 2nd, 4th, 5th, 6th, 8th, and 9th characteristics (all extracted from sensor data) are positively correlated with symptoms of impulsivity, and similarly, the other characteristics extracted from sensor data are positively correlated with impulsivity. Therefore, characteristics from both sensor data and task data are used as inputs to the trained symptom severity determination model 327 to determine the severity scale of impulsivity for the subject. Of the available task data, reaction time has the strongest (positive) correlation with symptoms of impulsivity. Of the available sensor data, channels (S6D10, S5D12, S6D12) configured to measure activity in the medial prefrontal cortex of the subject and, in some cases, in the medial prefrontal cortex that faces, overlaps, or is adjacent to the right lateral prefrontal cortex of the subject have the strongest correlation with symptoms of impulsivity, which is worthy of note.

[0265] Table VIII below provides some descriptions of the terms used for the characteristics in Tables III - VII. Not all of the characteristics from Tables III - VII are included in Table VIII, but it will be understood that the descriptions provided as explanations of the terms used to define the characteristics may equally apply to other characteristics in these tables. [Table 8]

[0266] Referring to FIGS. 16a - 16e, for each of the categories of the overall ADHD score (FIG. 16a), the primary ADHD core symptom score (FIG. 16b), inattention (FIG. 16c), hyperactivity (FIG. 16d), impulsivity (FIG. 16e), plots of the actual symptom severity scale / score (by the ADHD evaluation scale questionnaire score) against the predicted symptom severity scale / score (as predicted by the symptom severity determination model 327) are shown.

[0267] It will be understood by those skilled in the art that many variations and / or modifications can be made to the embodiments described above without departing from the broad general scope of the present disclosure. Therefore, the present embodiments should be considered illustrative in all respects and not restrictive.

Claims

1. A headset configured to be worn on a user's forehead, a plurality of electrodes disposed on the headset and configured to provide stimulation to a predetermined region of the user's brain, a first optical sensor disposed on a first side of one of the plurality of electrodes, the first optical sensor including a first light emitter and at least two photodetectors, the first light emitter being disposed proximate a first lateral end of the one electrode, a first photodetector of the first optical sensor being disposed proximate the first lateral end inside the first light emitter, and a second photodetector of the first optical sensor being disposed proximate a second lateral end opposite the first lateral end, the first optical sensor, a second optical sensor disposed on a second side of the one of the plurality of electrodes, the second optical sensor including a second light emitter and at least two photodetectors, the second light emitter being disposed proximate the second lateral end of the one electrode, a first photodetector of the second optical sensor being disposed proximate the second lateral end inside the second light emitter, and a second photodetector of the second optical sensor being disposed proximate the first lateral end, the second optical sensor, wherein the first optical sensor and the second optical sensor are configured to detect a first signal indicative of the user's cerebral blood oxygenation corresponding to an activity scale and to detect a second signal indicative of the user's scalp blood oxygenation, a headset, a controller communicatively coupled to the first optical sensor, the second optical sensor, and the plurality of electrodes, receiving the first signal and the second signal, determining the user's cerebral blood oxygenation level based on the first signal, determining the user's scalp blood oxygenation level based on the second signal, and a controller configured to selectively activate one or more of the plurality of electrodes to stimulate the predetermined region of the user's brain A system comprising.

2. The second photodetector of the first optical sensor is disposed closer to the first photodetector of the first optical sensor than the first light emitter, and the second photodetector of the second optical sensor is disposed closer to the first photodetector of the second optical sensor than the second light emitter. The system according to claim 1.

3. The headset is adjustable to accommodate various head sizes, The controller is configured to activate a portion of the plurality of electrodes based on the respective head size of the user so as to provide a stimulus to the predetermined region of the user's brain. The system according to claim 1.

4. The predetermined region of the user's brain includes at least one of the user's left lateral prefrontal cortex, the user's medial prefrontal cortex, or a boundary region between the user's medial prefrontal cortex and left lateral prefrontal cortex, regardless of the user's head size. The system according to claim 3.

5. The controller, transmits a stimulation instruction to at least one of the plurality of electrodes to generate transcranial stimulation to the user based on a stimulation parameter value, receives the first signal and the second signal from at least one of the first optical sensor and the second optical sensor, determines the cerebral blood oxygenation level of the user based on the first signal and determines the scalp blood oxygenation level of the user based on the second signal, subtracts the scalp blood oxygenation level from the cerebral blood oxygenation level, thereby obtaining activity data. The system according to claim 1, configured as such.

6. The controller further, determines a progression measure from the activity data, the progression measure corresponding to at least one clinically relevant symptom. The system according to claim 5, configured as such.

7. The controller further, determines an activity measure from the activity data, determines one or more updated stimulation parameter values for stimulating at least one or more of the plurality of electrodes based on the activity measure to treat the clinically relevant symptom. The system according to claim 5, configured as such.

8. A headset configured to be worn on a user's forehead, A first electrode disposed on the headset and configured to provide stimulation to a predetermined region of the user's brain, the first electrode having a first side and a second side opposite the first side, the first electrode; A second electrode disposed on the headset and configured to provide stimulation to a predetermined region of the user's brain, the second electrode having a first side and a second side opposite the first side, and the first side of the second electrode being adjacent to the second side of the first electrode, the second electrode; A first optical sensor disposed on the first side of the first electrode; A second optical sensor disposed on the second side of the first electrode and between the first electrode and the second electrode, including; Each of the first optical sensor and the second optical sensor includes a light emitter and two photodetectors, a headset; A controller communicatively coupled to the plurality of electrodes, the first optical sensor, and the second optical sensor, the controller being configured to determine the user's cerebral blood oxygenation level and the user's scalp blood oxygenation level based on data received from the first optical sensor and the second optical sensor, the controller; A system for neural sensing and stimulation, including.

9. The system according to claim 8, wherein the two photodetectors of the first optical sensor are configured to detect light emitted from the light emitter of the first optical sensor.

10. The system according to claim 8, wherein the two photodetectors of the first optical sensor are configured to detect light emitted from the light emitter of the first optical sensor or the second optical sensor.

11. The system according to claim 8, wherein the controller is configured to determine the user's cerebral blood oxygenation level based on data received from the first optical sensor and determine the user's scalp blood oxygenation level based on data received from the second optical sensor.

12. The system according to claim 11, wherein the controller is configured to determine the user's cerebral blood oxygenation level and the user's scalp blood oxygenation level based on data received from the first optical sensor.

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