Ultrasound systems, related devices, and methods for modulating brain activity

Abstract

This specification discloses a neuromodulation system comprising a transcranially mounted neuromodulation device and a stimulation control computing environment. The disclosed neuromodulation device comprises at least one ultrasound transducer and at least one electroencephalogram (EEG) electrode, and the disclosed stimulation control computing environment includes a stimulation control unit and an offline computing device, the disclosed stimulation control unit including associated systems and methods for controlling the function of the neuromodulation device using acoustic simulations performed on brain imaging data, as well as methods and uses of such neuromodulation systems in modulating brain activity using focused ultrasound stimulation of the thalamus and thalamic subregions during specific phases of slow-wave brain oscillations to treat various nervous system disorders or conditions, including sleep disorders.

Description

[Technical Field]

[0001] This application claims priority and has rights as of the filing date in accordance with Section 119e of 35 U.S.C., for U.S. Provisional Patent Application 63 / 030,850, filed on 27 May 2020, the contents of which are incorporated herein by reference in their entirety.

[0002] This disclosure relates, in general terms, to devices, and related systems, methods, and uses for modulating brain activity using ultrasonic stimulation. [Background technology]

[0003] Sleep is a state of recovery in which the brain temporarily shifts neural involvement to internal processes, partially disconnecting it from the outside world. More than a century of research has revealed that sleep quality is crucial for cognition and overall health, and that insufficient sleep can have disastrous consequences. Most obviously, it leads to a significant decline in the efficiency and accuracy of decision-making, and a substantial decrease in sensory processing associated with sleep deprivation. A vast amount of evidence also points to the long-term disadvantages caused by sleep deprivation, as evidenced by its inverse correlation with motivational states, memory stability, dementia, and Alzheimer's disease. Beyond conscious behavior, the disadvantages of sleep deprivation extend to physiological health, linked to countless diseases such as Alzheimer's disease, obesity, immune disorders, and cardiovascular disease. The loss of productivity due to sleep deprivation is estimated to cost more than $400 billion annually in the United States alone, without considering its potential correlation with other costly disease conditions. Therefore, improving sleep quality and making it easier to fall asleep presents an opportunity to improve both human health and productivity.

[0004] Research shows that the most restorative period of sleep occurs when the brain enters a state characterized by delta wave or slow wave activity. Therefore, enhancing slow wave activity represents a major effort to improve sleep quality in both sick and healthy individuals. Current therapies and methodologies for enhancing slow waves include pharmacological approaches and neuromodulatory devices. Pharmacological compounds for slow wave enhancement include α2-δ calcium channel ligands and serotonin (5HT)2 A These include receptor antagonists and cytopromiscus compounds such as trazodone. Various biologically derived compounds, such as human growth hormone and prolactin, have also been identified for use in sleep therapy. While many drugs are partially effective, their benefits are offset by a range of tolerance, efficacy, adherence, and addiction problems that hinder widespread adoption.

[0005] Alternatively, non-invasive closed-loop devices have been created to enhance slow waves by delivering stimuli during the "up state" of slow waves. This state is described by enhanced cortical activity via thalamic connections during slow waves and can be seen as a peak positive voltage during slow waves. In contrast, the "down state" is the period of slow waves in which the cortex is at rest. Examples of stimuli delivered during the up state include transcranial direct current stimulation (tDCS), which drives small currents to the surface of the cortex via scalp electrodes, auditory stimulation devices, to enhance SWS by playing low-intensity audible sounds, or even stimulating the vestibular system by rocking a hammock or cradle. While these devices avoid tolerance and addiction issues, it is unclear whether their effectiveness is comparable to the pharmacological treatments available to enhance SWS. One technical problem associated with current devices is that they act opportunistically, interface with available brain tissue from the transcranial surface rather than targeting desired tissue. Another major drawback of these techniques is the overall lack of spatial resolution and specificity to the cortex, or brain regions associated with slow wave generation. This can lead to disastrous side effects and / or a lack of effectiveness.

[0006] Therefore, what is needed is a non-invasive closed-loop device that can modulate slow waves with targeted ultrasound stimulation of the thalamus. [Overview of the project]

[0007] This specification discloses a neuromodulation system comprising a wearable neuromodulation device and a stimulation control computing environment. The disclosed device comprises a wearable device housing or frame including one or more electroencephalogram (EEG) electrodes and one or more ultrasonic transducer arrays. The disclosed device may further comprise one or more EEG signal amplifiers and / or digital-to-analog converters. The disclosed device housing includes a main band, the main band having conductive wiring embedded within the core channel of the main band. The disclosed conductive wiring includes a first conductive wiring and a second conductive wiring. The first conductive wiring connects one or more EEG electrodes to one or more EEG signal amplifiers, digital-to-analog converters, and a stimulation control unit, and then exits through a port at the rear of the main band. The EEG electrodes may also have preamplifiers on the headband, eliminating the need for downstream EEG signal amplifiers. The second conductive wiring connects one or more ultrasonic transducer arrays to the stimulation control unit before exiting through a port. In some embodiments, the disclosed one or more EEG electrodes include a first anterior EEG electrode positioned in the anterior portion of the main band. Each of the disclosed ultrasonic transducer arrays comprises one or more ultrasonic emitting elements. In some embodiments, the disclosed ultrasonic transducer arrays include a first side ultrasonic transducer array located in a first side portion of the main band and a second ultrasonic transducer array located in a second side portion of the main band.

[0008] The disclosed stimulus control computing environment comprises a stimulus control unit and an offline computing device. The stimulus control unit comprises one or more processors configured to execute algorithms that process real-time information acquired by one or more electroencephalogram electrodes to classify brain activity. Offline assessment of brain and cranial anatomical structures using brain imaging or EEG-derived predictions provides cranial anatomical structures and identifies and targets one or more specific regions of the brain. Offline software calculates phase corrections for individual elements of an ultrasound array to steer the beam to one or more targets. These phase corrections are used in real time to target one or more specific regions of the brain for ultrasound stimulation and to deliver ultrasound stimulation to one or more specific regions of the brain for a specified period of time.

[0009] This specification also discloses a neuromodulation system comprising a wearable neuromodulation device and a stimulus control computing environment for use in the prevention and / or treatment of brain disorders. This specification also discloses methods and uses for the prevention and / or treatment of brain disorders. Non-limiting embodiments of brain disorders include sleep disorders, brain disorders associated with sleep disorders, psychiatric disorders, metabolic disorders, epilepsy or other seizure disorders, anxiety, depression, and / or neuropathic pain.

[0010] The accompanying drawings incorporated herein and constituting part thereof illustrate aspects of the subject matter disclosed in at least one of the exemplary embodiments thereof, which are further defined in the following description. Features, elements, and aspects of this disclosure are referred to by similar numbers in different drawings representing the same, equivalent, or similar features, elements, or aspects according to one or more embodiments. The drawings are not necessarily to scale and instead are focused on illustrating the principles described herein and provided by the exemplary embodiments of the invention. [Brief explanation of the drawing]

[0011] [Figure 1A]This is a schematic diagram of an exemplary neuromodulation system disclosed herein, showing the neuromodulation device and stimulus control computing environment disclosed herein. [Figure 1B] This is a schematic diagram of an exemplary neuromodulation system disclosed herein, showing exemplary hardware components of a neuromodulation device disclosed herein. [Figure 2] This is a schematic diagram of an exemplary algorithmic framework of the stimulus control unit disclosed herein, illustrating various exemplary embodiments and steps for processing electroencephalogram signals from a user in real time and generating ultrasonic stimuli, as taught herein. [Figure 3A] This figure shows exemplary components of the neuromodulation systems disclosed herein, illustrating data acquisition and modulation systems in various aspects of the teachings of this disclosure. [Figure 3B] This figure shows an exemplary component of the neuromodulation system disclosed herein, and is a schematic diagram of a system integrating hardware, online software, and offline software aspects as taught in this disclosure. [Figure 4A] This is an illustrative diagram of network synchronization between the thalamus and cortical layers of the brain, showing the thalamic excitability network interaction with the cortex during a slow-wave "up" state. [Figure 4B] This is an illustrative diagram of network synchronization between the thalamus and cortical layers of the brain, showing electroencephalogram (EEG) traces of sleep stages collected from individuals, revealing large, slow oscillations that occur during N2 non-REM and N3 non-REM sleep. [Figure 4C] This diagram exemplifies network synchronization between the thalamus and cortical layers of the brain, showing a color map of slow wave phases for "up" and "down" states. [Figure 4D] This diagram illustrates network synchronization between the thalamus and cortical layers of the brain, demonstrating phase prediction through sine wave fitting to slow-wave EEG sleep. [Figure 4E] This is an illustrative diagram of network synchronization between the thalamus and cortical layers of the brain, showing phase prediction by stimuli delivered at phase 0 / 360°. [Figure 5A]This is a diagram illustrating an exemplary MRI-based methodology used for computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target nerve region. The image shows a scan created by MRI taken from the axial plane with the anterior commissure marked as a control point (crosshairs) used to calculate the maximum angle relative to the most posterior and anterior portions of the thalamus. [Figure 5B] Figure 5A shows an image taken from the frontal plane, illustrating an exemplary MRI-based methodology used in computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target nerve region. The image shows the image created by an MRI scan with the anterior commissure marked as a control point (crosshairs) used to calculate the maximum angle relative to the most posterior and anterior portions of the thalamus. [Figure 5C] This is a diagram illustrating an exemplary MRI-based methodology used in computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target nerve region, showing the change in target angle relative to the transducer position on the skull to estimate the need for beam steering. [Figure 5D] This figure illustrates an exemplary MRI-based methodology used in a pre-use calibration step to determine focused ultrasound and apply it to a specific target nerve region, showing an acoustic simulation of the ultrasound beam onto the image in Figure 5A when it is steered at a 20° angle relative to the plane of the transducer. [Figure 5E] This is a diagram illustrating an exemplary MRI-based methodology used for computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target neural region, showing image registration and segmentation of brain regions to identify the thalamic region, and showing images created by axially taken MRI scans. [Figure 5F]Figure 5E shows an exemplary MRI-based methodology used in a pre-use calibration step to determine focused ultrasound and apply it to a specific target nerve region, illustrating the image and showing the changing electronic element excitation phase to achieve three-dimensional differential focusing to stimulate a thalamic region using the exemplary stimulation control unit disclosed herein. [Figure 5G] This is a diagram illustrating an exemplary MRI-based methodology used for computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target nerve region, showing the time-varying electronic element excitation parameters to achieve differential focusing. [Figure 5H] This is a diagram illustrating an exemplary MRI-based methodology used in computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target neural region, showing images created by MRI scans taken from the frontal plane, illustrating image registration and segmentation of brain regions to identify the thalamic region. [Figure 5I] Figure 5H shows an image illustrating an exemplary MRI-based methodology used for computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target nerve region, illustrating the changing electronic element excitation phase to achieve three-dimensional differential focusing to stimulate a thalamic region using an exemplary stimulation control unit disclosed herein. [Figure 5J] This is a diagram illustrating an exemplary MRI-based methodology used for computational measurements to determine focused ultrasound during a pre-use calibration step and apply it to a specific target nerve region, showing the time-varying electronic element excitation parameters to achieve differential focusing. [Figure 6] This figure shows exemplary components of the devices and systems of the present disclosure, illustrating electroencephalogram (EEG) waveform acquisition, spectral analysis, sleep state classification, and closed-loop optimization based on phase-locked loops, according to various aspects of the teachings of the present disclosure. [Figure 7A] This is a schematic diagram of an exemplary control system algorithm for augmenting slow waves, and shows a portion of the exemplary control system algorithm. [Figure 7B]This is a schematic diagram of an exemplary control system algorithm for augmenting slow waves, a continuation of Figure 7A, and shows the second part of the control system algorithm. [Figure 8A] This is a schematic diagram showing an exemplary deep learning network for multi-signal input, and shows the first part of the exemplary deep learning network. [Figure 8B] This is a schematic diagram showing an exemplary deep learning network for multi-signal input, a continuation of Figure 8A, and shows the second part of the exemplary deep learning network. [Figure 8C] This is a schematic diagram showing an exemplary deep learning network for multi-signal input, a continuation of Figure 8B, and shows the third part of the exemplary deep learning network. [Figure 9A] This figure shows results from an exemplary deep learning model for sleep stage prediction, illustrating a 2D plot of different sleep classes using dimensionality reduction. [Figure 9B] This figure shows the results from an exemplary deep learning model for sleep stage prediction, comparing human annotations with the sleep stage predictions. [Modes for carrying out the invention]

[0012] The sleep-wake cycle is a neurobiological pattern of alternating periods of rest (sleep cycle) and activity (wake cycle). The wake cycle, or wakefulness, is the period of highest brain activity. When measured using electroencephalography (EEG), brain activity during wakefulness exhibits frequencies between 15Hz and 50Hz, amplitudes less than 50mV, and faintly distinguishable waveform types. Furthermore, compared to the sleep cycle, skeletal muscles are toned and active, and heart rate and respiratory rate are regular and at their highest levels.

[0013] The sleep cycle is more complex, divided into five stages, each with its own characteristic electroencephalogram (EEG) frequency, amplitude, and waveform type, and other identifiable biological rhythms, including eye movement (EOG) and muscle movement (EMG). The first four stages of the sleep cycle (N1, N2, and N3) are classified as non-rapid eye movement (non-REM) sleep, while the fifth stage (R) is classified as rapid eye movement (REM) sleep. N1 of non-REM sleep is the lightest stage of sleep, and EEG activity is slightly slower than during the wakefulness cycle. During this stage, EEG activity shows frequencies from 4 Hz to 12 Hz, with relatively low amplitude compared to other stages of consciousness, and the waveform type consists of alpha waves. During N1, eye movements are very slow, skeletal muscle tension is present, and breathing occurs at a regular rate.

[0014] Non-REM sleep stage N2 typically follows N1 and represents deeper sleep. During this stage, electroencephalogram (EEG) activity continues to slow down with frequencies between 4 Hz and 8 Hz, relatively low amplitude compared to other stages of consciousness, and waveform types including theta waves, which include certain bursts of rapid activity known as sleep spindles mixed with sleep structures known as K-complex waves. During N2, there is no eye movement, skeletal muscle activity is reduced, and heart rate and respiratory rate are suppressed but regular. Stage N2 of non-REM sleep accounts for approximately 40-60% of total sleep time.

[0015] Stage N3 of non-REM sleep is a gradually deepening sleep stage also known as slow-wave sleep (SWS) or deep sleep, and is the most restorative stage of sleep. During this stage, electroencephalogram (EEG) activity shows increased spectral power at frequencies of 0.5 Hz–4 Hz, relatively high amplitude compared to other stages of consciousness, and waveform types including delta waves or slow waves. During N3, there is no eye movement, skeletal muscle activity is reduced, and heart rate and respiratory rate are suppressed but regular. Stage N3 of non-REM sleep accounts for approximately 5–15% of total sleep time.

[0016] REM sleep is the sleep stage associated with dreaming. During this stage, brainwave activity shows increased spectral power at frequencies of 15-30 Hz, with relatively low amplitude compared to other stages of consciousness, and eye movements are rapid. Brainwave activity and eye movements are similar to those of the wakefulness cycle, but skeletal muscles are atonic or motionless, and heart rate and respiratory rate are faster, more unstable, and irregular than during non-REM sleep. Following REM sleep, the sleep cycle resumes, with periods of non-REM sleep (N1, N2, and N3) blending together, before returning to REM sleep again for longer periods as sleep continues.

[0017] Sleep is a highly heterogeneous state composed of local and global network oscillations, though the parts contribute disproportionately to its beneficial effects. Slow-wave sleep, observed in stage N3 of non-REM sleep, primarily stems from network synchronization between the thalamus, deep within the brain, and the cortical layers on the brain's surface. A burst of thalamic activity precedes the slow-wave peak, known as the cortical "up" state, essentially linking thalamic activation to slow-wave amplitude. While these waves can permeate all sleep states, they are more pronounced during stages N2 and N3 of non-REM sleep. The standard function of SWS is to drive memory consolidation, or stabilization of long-term memory, but it is also crucial for both cognitive and physiological functions, as well as brain tissue repair. Disrupting slow waves during sleep impairs attention and concentration in human subjects, leading to general fatigue. Interestingly, counteracting interventions are also supported: cognitive processes are significantly improved by increasing the amplitude of slow waves during non-REM sleep. Overall, enhancing slow waves presents an opportunity to improve both cognitive and physiological functions.

[0018] This specification discloses a neuromodulation system comprising a wearable neuromodulation device integrated with electroencephalogram electrodes and one or more integrated ultrasound transducer arrays. The disclosed neuromodulation system further includes a stimulation control unit comprising one or more processors and software that operates and controls the characteristics and functions of ultrasound stimulation when executed by such processors. Such software includes, but is not limited to, electroencephalogram real-time analysis software that can continuously monitor brain function to identify one or more specific characteristics, phases, or states of brain activity, and brain mapping software that can plot one or more specific regions of the brain and precisely focus or steer ultrasound stimulation to that one or more specific brain regions. The disclosed neuromodulation system also includes a computing device that assists in the operation of the neuromodulation device and data storage of the collected information. Thus, the neuromodulation system disclosed herein delivers ultrasound stimulation non-invasively in a spatially and temporally controlled manner. In this way, the device disclosed herein enables the focused application of ultrasound stimulation to specific regions of the brain, largely excluding surrounding brain tissue.

[0019] In particular, the neuromodulation devices disclosed herein maximize the effect of interventions on wave enhancement, such as slow-wave enhancement, by delivering focused ultrasound stimulation that drives neural activity by targeting the thalamus and other core structures that modulate SWS located deep within the brain. Since slow waves can be enhanced by exciting cells during the "up state" of the slow wave or inhibiting cells during the "down state," the devices need to collect and analyze real-time EEG signals, automatically stage sleep, and apply ultrasound stimulation to the thalamus during the slow-wave peak phase. By leveraging the advances disclosed herein, the neuromodulation devices disclosed herein improve beam focusing and temporal neural interaction with thalamic regions at multiple levels.

[0020] Unlike devices that utilize electrical or sensory stimulation, ultrasonic stimulation can be focused to deep regions of the brain without affecting the overlying tissue, using the same principle as focusing light through a lens. In comparison, tDCS uses current throughout the skull to generate sufficient current throughout the cortex, but this current is relatively weaker than the current required to elicit a neuronal response at normal operating voltages. Therefore, if electrical stimulation interferes with the reading of electrical signals, sequentially and accurately phase-aligning the target is difficult, if not impossible. Thus, electrical stimulation needs to be delivered intermittently or imprecisely sequentially. On the other hand, the devices disclosed herein overcome this obstacle and provide focused ultrasound that delivers a pressure field for stimulating specific nerve tissue / areas. Since no current is used in the neuromodulation devices disclosed herein, there is no electrical interference, thereby enabling serial phase-targeted stimulation.

[0021] Aspects of this specification disclose neuromodulation devices. The neuromodulation devices disclosed herein are thin, cranial-mounted devices. In some embodiments, the neuromodulation devices disclosed herein are suitable for wear during sleep without impairing the functionality of the device, and deliver a spatially targeted, safe ultrasonic pressure field through the skull, appropriately classifying sleep stages and slow wave phases to deliver stimuli at the appropriate time.

[0022] In some embodiments, as shown in Figures 1A and 1B, an exemplary neurostimulation device 110 comprises a wearable device housing 120 supporting two array housings 130, each containing an ultrasonic transducer 140, and two electroencephalogram (EEG) electrodes 150, such as active dry electroencephalogram electrodes. When worn, the wearable device housing 120 is configured to surround the skull with a transverse plane that positions the main band along the forehead, temples, and occipital region. The wearable device housing 120 provides a fixed, stereotactic placement of the ultrasonic transducer array 140 over the temporal region of the user's head and positions the EEG electrodes 150 flat against the user's forehead.

[0023] The wearable device housing 120 may include a main band, a secondary band, and an optional securing strap. The main band 122, secondary band 124, and optional securing strap 126. The main band 122, secondary band 124, and securing strap 126 are adjustable to facilitate precise positioning of the neuromodulatory device 110 and its fixation to the user's skull. The secondary band 124 may be attached to the main band 122 via first and second secondary band attachment points and configured to extend over the top of the head. First and second vascular injuries caused by injection of neurotoxin through the nostrils. The attachment points may be static or configured to allow movement between the secondary band 124 and the main band 122. The optional securing strap 126 may be attached to the main band 122 via first and second securing strap attachment points and configured to extend under the chin. The first and second securing strap attachment points may be static or configured to allow movement between the securing strap 126 and the main band 122. In these embodiments, the main band 122 includes a front and rear portion made of a semi-rigid material, a side portion or temple portion made of a flexible material, a secondary band 124 and first and second mounting hubs, each made of a semi-rigid material, and a fixing strap made of an elastic material.

[0024] Aspects of this specification disclose a neuromodulation device comprising an ultrasonic transducer array. The ultrasonic transducer array disclosed herein is an array of ultrasonic emitting elements designed to provide an optimal beam profile shape, steering range, and power output for effectively stimulating a specific brain region spatially and temporally. The ultrasonic signal generated by the ultrasonic transducer array disclosed herein can be amplified using software, such as a frequency-specific MOSFET driver, which is performed by one or more processors of the stimulation control unit disclosed herein. Thus, the arrangement of ultrasonic emitting elements in the ultrasonic transducer array, the number of ultrasonic emitting elements used in the ultrasonic transducer array, the maximum output pressure of the ultrasonic emitting elements used in the ultrasonic transducer array, the spacing between each ultrasonic emitting element in the ultrasonic transducer array, and the ultrasonic signal amplification are all related to the optimal functionality of the neuromodulation device disclosed herein. For example, increasing the number of ultrasonic emitting elements or decreasing the diameter of each element improves the steering capability of the ultrasonic transducer array. Furthermore, increasing the spacing between each ultrasonic emitting element allows for the generation of a smaller beam width, while simultaneously enabling focus steering by employing a two-dimensional arrangement. Generally, the more ultrasonic emission elements used and the larger the dimensionality of those ultrasonic emission elements, the smaller the minimum focal point that can be achieved by the transducer array.

[0025] The neuromodulation device 110 comprises one or more ultrasonic transducer arrays contained within a housing attached to the main band 122 of a wearable device housing 120. The one or more ultrasonic transducer arrays are positioned on the inner surface of the main band 122 and configured to interface with the user's skull. In some embodiments, the neuromodulation device disclosed herein includes a single ultrasonic transducer array positioned on the main band. In some embodiments, the neuromodulation device 110 includes a single ultrasonic transducer array positioned on one side of the main band 122 located in the left or right temple region above the user's ear. In some embodiments, the neuromodulation device 110 includes a single ultrasonic transducer array positioned on both sides of the main band 122 located in the left and right temple regions above the user's ear. In some embodiments, the neuromodulation device 110 includes multiple ultrasonic transducer arrays positioned on both sides of the main band 122 located in the left and right temple regions above the user's ear. In aspects of these embodiments, as shown in Figures 1A-B, the neuromodulation device 110 includes two ultrasonic transducer arrays 140, one positioned to the left of the main band 122 and the other positioned to the right of the main band 122. In aspects of these embodiments, the neuromodulation device 110 comprises two ultrasonic transducer arrays positioned to the left of the main band 122 and two ultrasonic transducer arrays positioned to the right of the main band 122.

[0026] In some embodiments, the ultrasonic transducer array disclosed herein has a diameter of approximately 50 mm and includes a 64-element sparse element array made of diced PZT composite material. The width and composition of the PZT composite material are designed to operate at 700 kHz. The elements are wired to a signal input source using printed flex circuits, all of which are held within a housing unit. A matching layer is coupled to the inner surface of the transducer and further to a silicone pad that is pressed against the user's temporal window with a force of >1 Newton.

[0027] The ultrasonic transducer arrays disclosed herein comprise a planar, open-curve arc, or closed-curve arc configuration of ultrasonic emitting elements. The planar, open-curve arc, or closed-curve arc configurations of ultrasonic emitting elements used in the ultrasonic transducer arrays disclosed herein are designed to provide optimal beam shape, steering range, and power output for effectively stimulating specific brain regions spatially and temporally. In some embodiments, the ultrasonic transducer arrays disclosed herein are one-dimensional planar, curved, or closed-curve arc configurations of ultrasonic emitting elements. In some embodiments, each ultrasonic emitting element may be controlled separately or in clusters to reduce cabling.

[0028] A neuromodulation device may comprise a single ultrasound transducer array or multiple ultrasound transducer arrays. The number of ultrasound transducer arrays disclosed herein is a number designed to provide optimal spatial and temporal delivery of ultrasound to a particular brain region. In aspects of this embodiment, a neuromodulation device disclosed herein comprises, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 ultrasound transducer arrays. In aspects of this embodiment, a neuromodulation device disclosed herein includes, for example, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 ultrasound transducer arrays. In further aspects of this embodiment, a neuromodulation device disclosed herein includes, for example, up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, or up to 10 ultrasound transducer arrays.In further embodiments of this embodiment, the neuromodulation device disclosed herein is, for example, 2-3 ultrasonic transducer arrays, 2-4 ultrasonic transducer arrays, 2-5 ultrasonic transducer arrays, 2-6 ultrasonic transducer arrays, 2-7 ultrasonic transducer arrays, 2-8 ultrasonic transducer arrays, 2-9 ultrasonic transducer arrays, 2-10 ultrasonic transducer arrays, 3-4 ultrasonic transducer arrays, 3-5 ultrasonic transducer arrays, 3-6 ultrasonic transducer arrays, 3-7 ultrasonic transducer arrays, 3-8 ultrasonic transducer arrays, 3-9 ultrasonic transducer arrays, 3-10 ultrasonic transducer arrays, 4-5 ultrasonic transducer arrays, 4-6 ultrasonic transducer arrays, 4-7 Includes ultrasonic transducer arrays, 4-8 ultrasonic transducer arrays, 4-9 ultrasonic transducer arrays, 4-10 ultrasonic transducer arrays, 5-6 ultrasonic transducer arrays, 5-7 ultrasonic transducer arrays, 5-8 ultrasonic transducer arrays, 5-9 ultrasonic transducer arrays, 5-10 ultrasonic transducer arrays, 6-7 ultrasonic transducer arrays, 6-8 ultrasonic transducer arrays, 6-9 ultrasonic transducer arrays, 6-10 ultrasonic transducer arrays, 7-8 ultrasonic transducer arrays, 7-9 ultrasonic transducer arrays, 7-10 ultrasonic transducer arrays, 8-9 ultrasonic transducer arrays, 8-10 ultrasonic transducer arrays, or 9-10 ultrasonic transducer arrays.

[0029] In some embodiments, the ultrasonic transducer arrays disclosed herein include a two-dimensional plane, an open-curve arc, or a closed-curve arc configuration of ultrasonic emitting elements. In aspects of these embodiments, the two-dimensional plane, an open-curve arc, or a closed-curve arc configuration of the ultrasonic transducer array may include, for example, 2, 3, 4, 5, 6, 7, or 8 rows of ultrasonic emitting elements. In other embodiments of these embodiments, the two-dimensional plane, a curve, or a closed-curve arc configuration of the ultrasonic transducer array may include, for example, at least 2, at least 3, at least 4, at least 5 rows, at least 6 rows, at least 7 rows, or at least 8 rows of ultrasonic emitting elements. In yet another embodiment of these embodiments, the two-dimensional plane, a curve, or a closed-curve arc configuration of the ultrasonic transducer array may include, for example, up to 2, up to 3, up to 4, up to 5 rows, up to 6 rows, up to 7 rows, or up to 8 rows of ultrasonic emitting elements. In yet another aspect of these embodiments, the two-dimensional planar, curved, or closed-curve arc configuration of the ultrasonic transducer array may include, for example, 2-3 rows of ultrasonic emitting elements, 2-4 rows of ultrasonic emitting elements, 2-5 rows of ultrasonic emitting elements, 2-6 rows of ultrasonic emitting elements, 2-7 rows of ultrasonic emitting elements, 2-8 rows of ultrasonic emitting elements, 3-4 rows of ultrasonic emitting elements, 3-5 rows of ultrasonic emitting elements, 3-6 rows of ultrasonic emitting elements, 3-7 rows of ultrasonic emitting elements, 3-8 rows of ultrasonic emitting elements, 4-5 rows of ultrasonic emitting elements, 4-6 rows of ultrasonic emitting elements, 4-7 rows of ultrasonic emitting elements, 4-8 rows of ultrasonic emitting elements, 5-6 rows of ultrasonic emitting elements, 5-7 rows of ultrasonic emitting elements, 5-8 rows of ultrasonic emitting elements, 6-7 rows of ultrasonic emitting elements, 6-8 rows of ultrasonic emitting elements, or 7-8 rows of ultrasonic emitting elements.

[0030] An ultrasonic transducer array comprises multiple ultrasonic emitting elements. The number of ultrasonic emitting elements in an ultrasonic transducer array disclosed herein is a number designed to provide optimal beam shape, steering range, and power output to effectively stimulate a specific brain region spatially and temporally. In aspects of this embodiment, the ultrasonic transducer array comprises, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 ultrasonic emitting elements. In aspects of this embodiment, the ultrasonic transducer array includes, for example, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 ultrasonic emitting elements. In further aspects of this embodiment, the ultrasonic transducer array includes, for example, up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, or up to 10 ultrasonic emitting elements. In a further aspect of this embodiment, the ultrasonic transducer array may consist of, for example, 2-3 ultrasonic emitting elements, 2-4 ultrasonic emitting elements, 2-5 ultrasonic emitting elements, 2-6 ultrasonic emitting elements, 2-7 ultrasonic emitting elements, 2-8 ultrasonic emitting elements, 2-9 ultrasonic emitting elements, 2-10 ultrasonic emitting elements, 3-4 ultrasonic emitting elements, 3-5 ultrasonic emitting elements, 3-6 ultrasonic emitting elements, 3-7 ultrasonic emitting elements, 3-8 ultrasonic emitting elements, 3-9 ultrasonic emitting elements, 3-10 ultrasonic emitting elements, 4-5 ultrasonic emitting elements, 4-6 ultrasonic emitting elements, Includes 4-7 ultrasonic emitting elements, 4-8 ultrasonic emitting elements, 4-9 ultrasonic emitting elements, 4-10 ultrasonic emitting elements, 5-6 ultrasonic emitting elements, 5-7 ultrasonic emitting elements, 5-8 ultrasonic emitting elements, 5-9 ultrasonic emitting elements, 5-10 ultrasonic emitting elements, 6-7 ultrasonic emitting elements, 6-8 ultrasonic emitting elements, 6-9 ultrasonic emitting elements, 6-10 ultrasonic emitting elements, 7-8 ultrasonic emitting elements, 7-9 ultrasonic emitting elements, 7-10 ultrasonic emitting elements, 8-9 ultrasonic emitting elements, 8-10 ultrasonic emitting elements, or 9-10 ultrasonic emitting elements.

[0031] In aspects of this embodiment, each row of the ultrasonic transducer array includes, for example, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, or 64 ultrasonic emitting elements. In other aspects of this embodiment, each row of the ultrasonic transducer array includes, for example, at least 4, at least 8, at least 12, at least 16, at least 20, at least 24, at least 28, at least 32, at least 36, at least 40, at least 44, at least 48, at least 52, at least 56, at least 60, or at least 64 ultrasonic emitting elements. In yet another aspect of this embodiment, each row of the ultrasonic transducer array includes, for example, up to 4, up to 8, up to 12, up to 16, up to 20, up to 24, up to 28, up to 32, up to 36, up to 40, up to 44, up to 48, up to 52, up to 56, up to 60, or up to 64 ultrasonic emitting elements. In yet another aspect of this embodiment, each row of the ultrasonic transducer array is, for example, 4-8 ultrasonic emitting elements, 4-12 ultrasonic emitting elements, 4-16 ultrasonic emitting elements, 4-20 ultrasonic emitting elements, 4-24 ultrasonic emitting elements, 4-28 ultrasonic emitting elements, 4-32 ultrasonic emitting elements, 4-36 ultrasonic emitting elements, 4-40 ultrasonic emitting elements, 4-44 ultrasonic emitting elements, 4-48 ultrasonic emitting elements, 4-52 ultrasonic emitting elements, 4-56 ultrasonic emitting elements, 4-60 ultrasonic emitting elements, 4-64 ultrasonic emitting elements, 8-12 ultrasonic emitting elements, 8-16 ultrasonic emitting elements, 8-20 ultrasonic emitting elements, 8-24 Ultrasonic radiation elements, 8-28 ultrasonic radiation elements, 8-32 ultrasonic radiation elements, 8-36 ultrasonic radiation elements, 8-40 ultrasonic radiation elements, 8-44 ultrasonic radiation elements, 8-48 ultrasonic radiation elements, 8-52 ultrasonic radiation elements, 8-56 ultrasonic radiation elements, 8-60 ultrasonic radiation elements, 8-64 ultrasonic radiation elements, 12-16 ultrasonic radiation elements, 12-20 ultrasonic radiation elements, 12-24 ultrasonic radiation elements, 12-28 ultrasonic radiation elements, 12-32 ultrasonic radiation elements, 12-36 ultrasonic radiation elements, 12-40 ultrasonic radiation elements, 12-44 ultrasonic radiation elements, 12-48 ultrasonic radiation elements, 12-52 ultrasonic radiation elements,12-56 ultrasonic emission elements, 12-60 ultrasonic emission elements, 12-64 ultrasonic emission elements, 16-20 ultrasonic emission elements, 16-24 ultrasonic emission elements, 16-28 ultrasonic emission elements, 16-32 ultrasonic emission elements, 16-36 ultrasonic emission elements, 16-40 ultrasonic emission elements, 16-44 ultrasonic emission elements, 16-48 ultrasonic emission elements, 16-52 ultrasonic emission elements, 16-56 ultrasonic emission elements, 16-60 ultrasonic emission elements, 16-64 ultrasonic emission elements, 20-24 ultrasonic emission elements, 20-28 Ultrasonic radiation elements, 20-32 ultrasonic radiation elements, 20-36 ultrasonic radiation elements, 20-40 ultrasonic radiation elements, 20-44 ultrasonic radiation elements, 20-48 ultrasonic radiation elements, 20-52 ultrasonic radiation elements, 20-56 ultrasonic radiation elements, 20-60 ultrasonic radiation elements, 20-64 ultrasonic radiation elements, 24-28 ultrasonic radiation elements, 24-32 ultrasonic radiation elements, 24-36 ultrasonic radiation elements, 24-40 ultrasonic radiation elements, 24-44 ultrasonic radiation elements, 24-48 ultrasonic radiation elements, 24-52 ultrasonic radiation elements, 24-56 ultrasonic emission elements, 24-60 ultrasonic emission elements, 24-64 ultrasonic emission elements, 28-32 ultrasonic emission elements, 28-36 ultrasonic emission elements, 28-40 ultrasonic emission elements, 28-44 ultrasonic emission elements, 28-48 ultrasonic emission elements, 28-52 ultrasonic emission elements, 28-56 ultrasonic emission elements, 28-60 ultrasonic emission elements, 28-64 ultrasonic emission elements, 32-36 ultrasonic emission elements, 32-40 ultrasonic emission elements, 32-44 ultrasonic emission elements, 32-48 ultrasonic emission elements, 32-52 Ultrasonic radiation elements, 32-56 ultrasonic radiation elements, 32-60 ultrasonic radiation elements, 32-64 ultrasonic radiation elements, 36-40 ultrasonic radiation elements, 36-44 ultrasonic radiation elements, 36-48 ultrasonic radiation elements, 36-52 ultrasonic radiation elements, 36-56 ultrasonic radiation elements, 36-60 ultrasonic radiation elements, 36-64 ultrasonic radiation elements, 40-44 ultrasonic radiation elements, 40-48 ultrasonic radiation elements, 40-52 ultrasonic radiation elements, 40-56 ultrasonic radiation elements, 40-60 ultrasonic radiation elements, 40-64 ultrasonic radiation elements,Includes 44-48 ultrasonic emitting elements, 44-52 ultrasonic emitting elements, 44-56 ultrasonic emitting elements, 44-60 ultrasonic emitting elements, 44-64 ultrasonic emitting elements, 48-52 ultrasonic emitting elements, 48-56 ultrasonic emitting elements, 48-60 ultrasonic emitting elements, 48-64 ultrasonic emitting elements, 52-56 ultrasonic emitting elements, 52-60 ultrasonic emitting elements, 52-64 ultrasonic emitting elements, 56-60 ultrasonic emitting elements, 56-64 ultrasonic emitting elements, or 60-64 ultrasonic emitting elements.

[0032] In aspects of this embodiment, the ultrasonic transducer array comprises, for example, 4, 8, 12, 16, 24, 32, 48, 64, 80, 96, 108, 128, 144, or 256 ultrasonic emitting elements. In other aspects of this embodiment, the ultrasonic transducer array includes, for example, at least 4, at least 8, at least 12, at least 16, at least 24, at least 32, at least 48, at least 64, at least 80, at least 96, at least 108, at least 128, at least 144, or at least 254 ultrasonic emitting elements. In yet another aspect of this embodiment, the ultrasonic transducer array includes, for example, up to 4, up to 8, up to 12, up to 16, up to 24, up to 32, up to 48, up to 64, up to 80, up to 96, up to 108, up to 128, up to 144, or up to 254 ultrasonic emitting elements.In yet another aspect of this embodiment, the ultrasonic transducer array may consist of, for example, 4-8 ultrasonic emitting elements, 4-12 ultrasonic emitting elements, 4-16 ultrasonic emitting elements, 4-24 ultrasonic emitting elements, 4-32 ultrasonic emitting elements, 4-36 ultrasonic emitting elements, 4-48 ultrasonic emitting elements, 8-12 ultrasonic emitting elements, 8-16 ultrasonic emitting elements, 8-24 ultrasonic emitting elements, 8-32 ultrasonic emitting elements, 8-36 ultrasonic emitting elements, 8-48 ultrasonic emitting elements, 8-60 ultrasonic emitting elements, 16-24 ultrasonic emitting elements, 16-32 ultrasonic emitting elements, 16-36 ultrasonic emitting elements, 16-48 ultrasonic emitting elements, 16-60 ultrasonic emitting elements, 16-72 ultrasonic emitting elements, 24-32 ultrasonic emitting elements, 24-36 ultrasonic emitting elements, 24- Includes 48 ultrasonic emitting elements, 24-60 ultrasonic emitting elements, 24-72 ultrasonic emitting elements, 24-80 ultrasonic emitting elements, 24-96 ultrasonic emitting elements, 36-48 ultrasonic emitting elements, 36-60 ultrasonic emitting elements, 36-72 ultrasonic emitting elements, 36-80 ultrasonic emitting elements, 36-96 ultrasonic emitting elements, 36-108 ultrasonic emitting elements, 36-128 ultrasonic emitting elements, 48-60 ultrasonic emitting elements, 48-72 ultrasonic emitting elements, 48-80 ultrasonic emitting elements, 48-96 ultrasonic emitting elements, 48-108 ultrasonic emitting elements, 48-128 ultrasonic emitting elements, 72-96 ultrasonic emitting elements, 72-108 ultrasonic emitting elements, 72-128 ultrasonic emitting elements, 72-144 ultrasonic emitting elements, or 72-256 ultrasonic emitting elements.

[0033] The ultrasonic transducer arrays disclosed herein provide appropriately timed output pressure from ultrasonic emitting elements designed to provide optimal beam shape, spatial focus, and power output for effectively stimulating specific brain regions spatially and temporally. In aspects of this embodiment, the ultrasonic transducer arrays disclosed herein provide operating frequencies from the ultrasonic emitting elements, for example, about 200 kHz, about 250 kHz, about 300 kHz, about 350 kHz, about 400 kHz, about 450 kHz, about 500 kHz, about 600 kHz, about 650 kHz, about 700 kHz, about 750 kHz, about 800 kHz, about 850 kHz, about 900 kHz, about 950 kHz, or about 1 MHz. In other aspects of this embodiment, the ultrasonic transducer array disclosed herein provides operating frequencies for ultrasonic emitting elements of, for example, at least 50 kHz, at least 100 kHz, at least 150 kHz, at least 200 kHz, at least 250 kHz, at least 300 kHz, at least 350 kHz, at least 400 kHz, at least 450 kHz, at least 500 kHz, or at least 1 MHz. In yet another aspect of this embodiment, the ultrasonic transducer array disclosed herein provides operating frequencies for ultrasonic emitting elements of, for example, up to 50 kHz, up to 100 kHz, up to 150 kHz, up to 200 kHz, up to 250 kHz, up to 300 kHz, up to 350 kHz, up to 400 kHz, up to 450 kHz, up to 500 kHz, or up to 1 MHz.In yet another aspect of this embodiment, the ultrasonic transducer array disclosed herein includes, for example, about 50 kHz to about 100 kHz, about 50 kHz to about 200 kHz, about 50 kHz to about 300 kHz, about 50 kHz to about 400 kHz, about 50 kHz to about 500 kHz, about 100 kHz to about 200 kHz, about 100 kHz to about 300 kHz, about 100 kHz to about 400 kHz, about 100 kHz to about 500 kHz, about 150 kHz to about 200 kHz, about 150 kHz to about 300 kHz, and about 150 kHz. It provides operating frequencies for ultrasonic emitting elements in the following ranges: Hz to approximately 400kHz, approximately 150kHz to approximately 500kHz, approximately 200kHz to approximately 300kHz, approximately 200kHz to approximately 400kHz, approximately 200kHz to approximately 500kHz, approximately 250kHz to approximately 300kHz, approximately 250kHz to approximately 400kHz, approximately 250kHz to approximately 500kHz, approximately 300kHz to approximately 400kHz, approximately 300kHz to approximately 500kHz, approximately 350kHz to approximately 400kHz, approximately 400kHz to approximately 500kHz, and approximately 450kHz to approximately 500kHz.

[0034] In aspects of this embodiment, the ultrasonic transducer array disclosed herein provides operating frequencies from ultrasonic emitting elements of, for example, about 500 kHz, about 600 kHz, about 700 kHz, about 800 kHz, about 900 kHz, about 1000 kHz, about 1100 kHz, about 1200 kHz, about 1300 kHz, about 1400 kHz, or about 1500 kHz. In other aspects of this embodiment, the ultrasonic transducer array disclosed herein provides output power from ultrasonic emitting elements of, for example, at least 500 kHz, at least 600 kHz, at least 700 kHz, at least 800 kHz, at least 900 kHz, at least 1000 kHz, at least 1100 kHz, at least 1200 kHz, at least 1300 kHz, at least 1400 kHz, or at least 1500 kHz. In yet another embodiment of this embodiment, the ultrasonic transducer array disclosed herein provides from the ultrasonic emitting elements an operating frequency of, for example, up to 500 kHz, up to 600 kHz, up to 700 kHz, up to 800 kHz, up to 900 kHz, up to 1000 kHz, up to 1100 kHz, up to 1200 kHz, up to 1300 kHz, up to 1400 kHz, or up to 1500 kHz. In yet another aspect of this embodiment, the ultrasonic transducer array disclosed herein is, for example, about 500 kHz to about 600 kHz, about 500 kHz to about 700 kHz, about 500 kHz to about 800 kHz, about 500 kHz to about 900 kHz, about 500 kHz to about 1,000 kHz, about 500 kHz to about 1,100 kHz, about 500 kHz to about 1,200 kHz, about 500 kHz to about 1,300 kHz, about 500 kHz to about 1,400 kHz, about 500 kHz to about 1,500kHz, approximately 600kHz to approximately 700kHz, approximately 600kHz to approximately 800kHz, approximately 600kHz to approximately 900kHz, approximately 600kHz to approximately 1,000kHz, approximately 600kHz to approximately 1,100kHz, approximately 600kHz to approximately 1,200kHz, approximately 600kHz to approximately 1,300kHz, approximately 600kHz to approximately 1,400kHz, approximately 600kHz to approximately 1,500kHz, approximately 700kHz to approximately 800kHz, approximately 700kHz to approximately 900kHz, approximately 700kHz to approximately 1,000kHz, approximately 700kHz to approximately 1,100kHz, approximately 700kHz to approximately 1,200kHz, approximately 700kHz to approximately 1,300kHz, approximately 700kHz to approximately 1,400kHz, approximately 700kHz to approximately 1,500kHz, approximately 800kHz to approximately 900kHz, approximately 800kHz to approximately 1,000kHz, approximately 800kHz to approximately 1,100kHz, approximately 800kHz to approximately 1,200kHz 0kHz, approximately 800kHz to approximately 1,300kHz, approximately 800kHz to approximately 1,400kHz, approximately 800kHz to approximately 1,500kHz, approximately 900kHz to approximately 1,000kHz, approximately 900kHz to approximately 1,100kHz, approximately 900kHz to approximately 1,200kHz, approximately 900kHz to approximately 1,300kHz, approximately 900kHz to approximately 1,400kHz, approximately 900kHz to approximately 1,500 kHz, approximately 1,000kHz to 1,100kHz, approximately 1,000kHz to 1,200kHz, approximately 1,000kHz to 1,300kHz, approximately 1,000kHz to 1,400kHz, approximately 1,000kHz to 1,500kHz, approximately 1,100kHz to 1,200kHz, approximately 1,100kHz to 1,300kHz, approximately 1,100kHz to 1,400kHz It provides operating frequencies from ultrasonic emitting elements of approximately 1,100 kHz to 1,500 kHz, approximately 1,200 kHz to 1,300 kHz, approximately 1,200 kHz to 1,400 kHz, approximately 1,200 kHz to 1,500 kHz, approximately 1,300 kHz to 1,400 kHz, or approximately 1,300 kHz to 1,500 kHz.

[0035] The ultrasonic transducer arrays disclosed herein provide output pressure at appropriate timing from ultrasonic emitting elements designed to provide optimal beam shape, spatial focus, and power output for effectively stimulating specific brain regions spatially and temporally. In aspects of this embodiment, the ultrasonic transducer arrays disclosed herein provide output pressure from ultrasonic emitting elements that enable ultrasonic stimulation at tissue depths of, for example, 20 mm, 25 mm, 30 mm, 35 mm, 40 mm, 45 mm, 50 mm, 55 mm, 60 mm, 65 mm, 70 mm, 75 mm, 80 mm, 85 mm, 90 mm, or 100 mm. In aspects of this embodiment, the ultrasonic transducer array disclosed herein provides a focal output pressure from ultrasonic emitting elements that enables ultrasonic stimulation at tissue depths of, for example, at least 20 mm, at least 25 mm, at least 30 mm, at least 35 mm, at least 40 mm, at least 45 mm, at least 50 mm, at least 55 mm, at least 60 mm, at least 65 mm, at least 70 mm, at least 75 mm, at least 80 mm, at least 85 mm, at least 90 mm, at least 95 mm, or at least 100 mm. In aspects of this embodiment, the ultrasonic transducer array disclosed herein provides an output pressure from ultrasonic emitting elements that enables ultrasonic stimulation at tissue depths of, for example, up to 20 mm, up to 25 mm, up to 30 mm, up to 35 mm, up to 40 mm, up to 45 mm, up to 50 mm, up to 55 mm, up to 60 mm, up to 65 mm, up to 70 mm, up to 75 mm, up to 80 mm, up to 85 mm, up to 90 mm, up to 95 mm, or up to 100 mm.In aspects of this embodiment, the ultrasonic transducer array disclosed herein is, for example, 20mm to 25mm, 20mm to 30mm, 20mm to 35mm, 20mm to 40mm, 20mm to 45mm, 20mm to 50mm, 20mm to 55mm, 20mm to 60mm, 20mm to 70mm, 20mm to 80mm, 20mm to 90mm, 20mm to 100mm, 25mm to 30mm, 25mm to 35mm, 25mm to 40mm, 25mm to 45mm, 25mm to 50mm, 25mm to 55mm, 25mm to 60mm, 25mm to 70mm, 25mm to 80mm, 25mm to 90mm, 25mm to 100mm, 30mm to 35mm, 30mm to 40mm, 30mm to 45mm, 30mm to 50mm, 30mm to 55mm, 30mm to 60mm, 30mm to 70mm, 30mm to 80mm, 30mm to 90mm, 30mm to 100mm, 35mm to 40mm, 35mm to 45mm, 35mm to 50mm, 35mm to 55mm, 35mm to 60mm, 35mm to 70mm mm, 35mm to 80mm, 35mm to 90mm, 35mm to 100mm, 40mm to 45mm, 40mm to 50mm, 40mm to 55mm, 40mm to 60mm, 40mm to 70mm, 40mm to 80mm, 40mm to 90mm, 40mm to 100mm, 45mm to 50mm, 45mm to 55mm, 45mm to 60mm, 45mm to 70mm, 45mm to 80mm, 45mm to 90mm, 45mm to 100mm, 50mm to 55mm, 50mm to 60mm, 50mm to 70mm It provides a focal output pressure from an ultrasonic emission element that enables ultrasonic stimulation at tissue depths of 50mm to 80mm, 50mm to 90mm, 50mm to 100mm, 55mm to 60mm, 55mm to 70mm, 55mm to 80mm, 55mm to 90mm, 55mm to 100mm, 60mm to 70mm, 60mm to 80mm, 60mm to 90mm, 60mm to 100mm, 70mm to 80mm, 70mm to 90mm, 70mm to 100mm, 80mm to 90mm, or 90mm to 100mm.

[0036] The ultrasonic transducer array disclosed in this specification provides appropriate timing intensities from ultrasonic radiation elements designed to provide an optimal beam shape, steering range, and power output to effectively stimulate a specific brain region both spatially and temporally. In aspects of this embodiment, the ultrasonic transducer array disclosed in this specification provides, for example, a spatial peak pulse average intensity at the spatial focus from ultrasonic radiation elements of about 1 mW / cm 2 , about 2.5 mW / cm 2 , about 5 mW / cm 2 , about 7.5 mW / cm 2 , about 10 mW / cm 2 , about 15 mW / cm 2 , about 20 mW / cm 2 , about 30 mW / cm 2 , about 40 mW / cm 2 , about 50 mW / cm 2 , about 60 mW / cm 2 , about 70 mW / cm 2 , about 80 mW / cm 2 , about 90 mW / cm 2 , about 100 mW / cm 2 , about 110 mW / cm 2 , about 120 mW / cm 2 , about 130 mW / cm 2 , about 140 mW / cm 2 , about 150 mW / cm 2 , about 160 mW / cm 2 , about 170 mW / cm 2 , about 180 mW / cm 2 , about 190 mW / cm 2 , or about 200 mW / cm 2 at the spatial focus from ultrasonic radiation elements. In other aspects of this embodiment, the ultrasonic transducer array disclosed in this specification provides from ultrasonic radiation elements, for example, at least 1 mW / cm 2 , at least 2.5 mW / cm 2 , at least 5 mW / cm 2 , at least 7.5 mW / cm 2 , at least 10 mW / cm 2 , at least 15 mW / cm 2 , at least 20 mW / cm 2at least 30 mW / cm² 2 at least 40 mW / cm² 2 at least 50 mW / cm² 2 at least 60 mW / cm² 2 at least 70 mW / cm² 2 at least 80 mW / cm² 2 at least 90 mW / cm² 2 at least 100 mW / cm² 2 at least 110 mW / cm² 2 at least 120 mW / cm² 2 at least 130 mW / cm² 2 at least 140 mW / cm² 2 at least 150 mW / cm² 2 at least 160 mW / cm² 2 at least 170 mW / cm² 2 at least 180 mW / cm² 2 at least 190 mW / cm² 2 , or at least 200 mW / cm² 2 It provides an intensity of, for example, up to 1 mW / cm² from the ultrasonic emitting element. In yet another embodiment of this embodiment, the ultrasonic transducer array disclosed herein provides, for example, up to 1 mW / cm² from the ultrasonic emitting element. 2 , max. 2.5mW / cm 2 , max. 5mW / cm 2 , up to 7.5mW / cm 2 , maximum 10mW / cm 2 , max. 15mW / cm 2 , maximum 20mW / cm 2 , maximum 30mW / cm 2 , maximum 40mW / cm 2 , maximum 50mW / cm 2 , maximum 60mW / cm 2 , maximum 70mW / cm 2 , maximum 80mW / cm 2 , maximum 90mW / cm 2 , maximum 100mW / cm 2 , maximum 110mW / cm 2 , maximum 120mW / cm 2 , maximum 130mW / cm 2 , maximum 140mW / cm 2 , maximum 150mW / cm2 , up to 160 mW / cm 2 , up to 170 mW / cm 2 , up to 180 mW / cm 2 , up to 190 mW / cm 2 , or up to 200 mW / cm 2 to provide the intensity of.

[0037] In still other aspects of this embodiment, the ultrasonic transducer array disclosed herein is, for example, from about 1 mW / cm 2 to about 5 mW / cm 2 , from about 1 mW / cm 2 to about 10 mW / cm 2 , from about 1 mW / cm 2 to about 20 mW / cm 2 , from about 1 mW / cm 2 to about 30 mW / cm 2 , from about 1 mW / cm 2 to about 40 mW / cm 2 , from about 1 mW / cm 2 to about 50 mW / cm 2 , from about 1 mW / cm 2 to about 60 mW / cm 2 , from about 1 mW / cm 2 to about 70 mW / cm 2 , from about 1 mW / cm 2 to about 80 mW / cm 2 , from about 1 mW / cm 2 to about 90 mW / cm 2 , from about 1 mW / cm 2 to about 100 mW / cm 2 , from about 1 mW / cm 2 to about 110 mW / cm 2 , from about 1 mW / cm 2 to about 120 mW / cm 2 , from about 1 mW / cm 2 to about 130 mW / cm 2 , from about 1 mW / cm 2 to about 140 mW / cm 2 , from about 1 mW / cm 2 to about 150 mW / cm 2 , from about 1 mW / cm 2 to about 160 mW / cm 2 , from about 1 mW / cm 2From approximately 170 mW / cm² 2 , about 1mW / cm 2 From approximately 180 mW / cm² 2 , about 1mW / cm 2 From approximately 190 mW / cm² 2 , about 5mW / cm 2 From approximately 10 mW / cm² 2 , about 5mW / cm 2 From approximately 20 mW / cm² 2 , about 5mW / cm 2 From approximately 30 mW / cm² 2 , about 5mW / cm 2 From approximately 40 mW / cm² 2 , about 5mW / cm 2 From approximately 50 mW / cm² 2 , about 5mW / cm 2 From approximately 60 mW / cm² 2 , about 5mW / cm 2 From approximately 70 mW / cm² 2 , about 5mW / cm 2 From approximately 80 mW / cm² 2 , about 5mW / cm 2 From approximately 90 mW / cm² 2 , about 5mW / cm 2 From approximately 100 mW / cm² 2 , about 5mW / cm 2 From approximately 110 mW / cm² 2 , about 5mW / cm 2 From approximately 120 mW / cm² 2 , about 5mW / cm 2 From approximately 130 mW / cm² 2 , about 5mW / cm 2 From approximately 140 mW / cm² 2 , about 5mW / cm 2 From approximately 150 mW / cm² 2 , about 5mW / cm 2 From approximately 160 mW / cm² 2 , about 5mW / cm 2 From approximately 170 mW / cm² 2 , about 5mW / cm 2 From approximately 180 mW / cm² 2 , about 5mW / cm 2 From approximately 190 mW / cm² 2 , about 10mW / cm 2 From approximately 20 mW / cm²2 , about 10mW / cm 2 From approximately 30 mW / cm² 2 , about 10mW / cm 2 From approximately 40 mW / cm² 2 , about 10mW / cm 2 From approximately 50 mW / cm² 2 , about 10mW / cm 2 From approximately 60 mW / cm² 2 , about 10mW / cm 2 From approximately 70 mW / cm² 2 , about 10mW / cm 2 From approximately 80 mW / cm² 2 , about 10mW / cm 2 From approximately 90 mW / cm² 2 , about 10mW / cm 2 From approximately 100 mW / cm² 2 , about 10mW / cm 2 From approximately 110 mW / cm² 2 , about 10mW / cm 2 From approximately 120 mW / cm² 2 , about 10mW / cm 2 From approximately 130 mW / cm² 2 , about 10mW / cm 2 From approximately 140 mW / cm² 2 , about 10mW / cm 2 From approximately 150 mW / cm² 2 , about 10mW / cm 2 From approximately 160 mW / cm² 2 , about 10mW / cm 2 From approximately 170 mW / cm² 2 , about 10mW / cm 2 From approximately 180 mW / cm² 2 , about 10mW / cm 2 From approximately 190 mW / cm² 2 , about 20mW / cm 2 From approximately 30 mW / cm² 2 , about 20mW / cm 2 From approximately 40 mW / cm² 2 , about 20mW / cm 2 From approximately 50 mW / cm² 2 , about 20mW / cm 2 From approximately 60 mW / cm² 2 , about 20mW / cm 2 From approximately 70 mW / cm²2 , about 20mW / cm 2 From approximately 80 mW / cm² 2 , about 20mW / cm 2 From approximately 90 mW / cm² 2 , about 20mW / cm 2 From approximately 100 mW / cm² 2 , about 20mW / cm 2 From approximately 110 mW / cm² 2 , about 20mW / cm 2 From approximately 120 mW / cm² 2 , about 20mW / cm 2 From approximately 130 mW / cm² 2 , about 20mW / cm 2 From approximately 140 mW / cm² 2 , about 20mW / cm 2 From approximately 150 mW / cm² 2 , about 20mW / cm 2 From approximately 160 mW / cm² 2 , about 20mW / cm 2 From approximately 170 mW / cm² 2 , about 20mW / cm 2 From approximately 180 mW / cm² 2 , about 20mW / cm 2 From approximately 190 mW / cm² 2 , about 30mW / cm 2 From approximately 40 mW / cm² 2 , about 30mW / cm 2 From approximately 50 mW / cm² 2 , about 30mW / cm 2 From approximately 60 mW / cm² 2 , about 30mW / cm 2 From approximately 70 mW / cm² 2 , about 30mW / cm 2 From approximately 80 mW / cm² 2 , about 30mW / cm 2 From approximately 90 mW / cm² 2 , about 30mW / cm 2 From approximately 100 mW / cm² 2 , about 30mW / cm 2 From approximately 110 mW / cm² 2 , about 30mW / cm 2 From approximately 120 mW / cm² 2 , about 30mW / cm 2From approximately 130 mW / cm² 2 , about 30mW / cm 2 From approximately 140 mW / cm² 2 , about 30mW / cm 2 From approximately 150 mW / cm² 2 , about 30mW / cm 2 From approximately 160 mW / cm² 2 , about 30mW / cm 2 From approximately 170 mW / cm² 2 , about 30mW / cm 2 From approximately 180 mW / cm² 2 , about 30mW / cm 2 From approximately 190 mW / cm² 2 , about 40mW / cm 2 From approximately 50 mW / cm² 2 , about 40mW / cm 2 From approximately 60 mW / cm² 2 , about 40mW / cm 2 From approximately 70 mW / cm² 2 , about 40mW / cm 2 From approximately 80 mW / cm² 2 , about 40mW / cm 2 From approximately 90 mW / cm² 2 , about 40mW / cm 2 From approximately 100 mW / cm² 2 , about 40mW / cm 2 From approximately 110 mW / cm² 2 , about 40mW / cm 2 From approximately 120 mW / cm² 2 , about 40mW / cm 2 From approximately 130 mW / cm² 2 , about 40mW / cm 2 From approximately 140 mW / cm² 2 , about 40mW / cm 2 From approximately 150 mW / cm² 2 , about 40mW / cm 2 From approximately 160 mW / cm² 2 , about 40mW / cm 2 From approximately 170 mW / cm² 2 , about 40mW / cm 2 From approximately 180 mW / cm² 2 , about 40mW / cm 2 From approximately 190 mW / cm² 2 , about 50mW / cm2 From approximately 60 mW / cm² 2 , about 50mW / cm 2 From approximately 70 mW / cm² 2 , about 50mW / cm 2 From approximately 80 mW / cm² 2 , about 50mW / cm 2 From approximately 90 mW / cm² 2 , about 50mW / cm 2 From approximately 100 mW / cm² 2 , about 50mW / cm 2 From approximately 110 mW / cm² 2 , about 50mW / cm 2 From approximately 120 mW / cm² 2 , about 50mW / cm 2 From approximately 130 mW / cm² 2 , about 50mW / cm 2 From approximately 140 mW / cm² 2 , about 50mW / cm 2 From approximately 150 mW / cm² 2 , about 50mW / cm 2 From approximately 160 mW / cm² 2 , about 50mW / cm 2 From approximately 170 mW / cm² 2 , about 50mW / cm 2 From approximately 180 mW / cm² 2 , about 50mW / cm 2 From approximately 190 mW / cm² 2 , about 60mW / cm 2 From approximately 70 mW / cm² 2 , about 60mW / cm 2 From approximately 80 mW / cm² 2 , about 60mW / cm 2 From approximately 90 mW / cm² 2 , about 60mW / cm 2 From approximately 100 mW / cm² 2 , about 60mW / cm 2 From approximately 110 mW / cm² 2 , about 60mW / cm 2 From approximately 120 mW / cm² 2 , about 60mW / cm 2 From approximately 130 mW / cm² 2 , about 60mW / cm 2 From approximately 140 mW / cm² 2, about 60mW / cm 2 From approximately 150 mW / cm² 2 , about 60mW / cm 2 From approximately 160 mW / cm² 2 , about 60mW / cm 2 From approximately 170 mW / cm² 2 , about 60mW / cm 2 From approximately 180 mW / cm² 2 , about 60mW / cm 2 From approximately 190 mW / cm² 2 , about 70mW / cm 2 From approximately 80 mW / cm² 2 , about 70mW / cm 2 From approximately 90 mW / cm² 2 , about 70mW / cm 2 From approximately 100 mW / cm² 2 , about 70mW / cm 2 From approximately 110 mW / cm² 2 , about 70mW / cm 2 From approximately 120 mW / cm² 2 , about 70mW / cm 2 From approximately 130 mW / cm² 2 , about 70mW / cm 2 From approximately 140 mW / cm² 2 , about 70mW / cm 2 From approximately 150 mW / cm² 2 , about 70mW / cm 2 From approximately 160 mW / cm² 2 , about 70mW / cm 2 From approximately 170 mW / cm² 2 , about 70mW / cm 2 From approximately 180 mW / cm² 2 , about 70mW / cm 2 From approximately 190 mW / cm² 2 , about 80mW / cm 2 From approximately 90 mW / cm² 2 , about 80mW / cm 2 From approximately 100 mW / cm² 2 , about 80mW / cm 2 From approximately 110 mW / cm² 2 , about 80mW / cm 2 From approximately 120 mW / cm² 2 , about 80mW / cm 2 From approximately 130 mW / cm²2 , about 80mW / cm 2 From approximately 140 mW / cm² 2 , about 80mW / cm 2 From approximately 150 mW / cm² 2 , about 80mW / cm 2 From approximately 160 mW / cm² 2 , about 80mW / cm 2 From approximately 170 mW / cm² 2 , about 80mW / cm 2 From approximately 180 mW / cm² 2 , about 80mW / cm 2 From approximately 190 mW / cm² 2 , about 90mW / cm 2 From approximately 100 mW / cm² 2 , about 90mW / cm 2 From approximately 11 0 mW / cm 2 , about 90mW / cm 2 From approximately 120 mW / cm² 2 , about 90mW / cm 2 From approximately 130 mW / cm² 2 , about 90mW / cm 2 From approximately 140 mW / cm² 2 , about 90mW / cm 2 From approximately 150 mW / cm² 2 , about 90mW / cm 2 From approximately 160 mW / cm² 2 , about 90mW / cm 2 From approximately 170 mW / cm² 2 , about 90mW / cm 2 From approximately 180 mW / cm² 2 , about 90mW / cm 2 From approximately 190 mW / cm² 2 , about 100mW / cm 2 From approximately 110 mW / cm² 2 , about 100mW / cm 2 From approximately 120 mW / cm² 2 , about 100mW / cm 2 From approximately 130 mW / cm² 2 , about 100mW / cm 2 From approximately 140 mW / cm² 2 , about 100mW / cm 2 From approximately 150 mW / cm² 2, about 100mW / cm 2 From approximately 160 mW / cm² 2 , about 100mW / cm 2 From approximately 170 mW / cm² 2 , about 100mW / cm 2 From approximately 180 mW / cm² 2 , about 100mW / cm 2 From approximately 190 mW / cm² 2 , about 110mW / cm 2 From approximately 120 mW / cm² 2 , about 110mW / cm 2 From approximately 130 mW / cm² 2 , about 110mW / cm 2 From approximately 140 mW / cm² 2 , about 110mW / cm 2 From approximately 150 mW / cm² 2 , about 110mW / cm 2 From approximately 160 mW / cm² 2 , about 110mW / cm 2 From approximately 170 mW / cm² 2 , about 110mW / cm 2 From approximately 180 mW / cm² 2 , about 110mW / cm 2 From approximately 190 mW / cm² 2 , about 120mW / cm 2 From approximately 130 mW / cm² 2 , about 120mW / cm 2 From approximately 140 mW / cm² 2 , about 120mW / cm 2 From approximately 150 mW / cm² 2 , about 120mW / cm 2 From approximately 160 mW / cm² 2 , about 120mW / cm 2 From approximately 170 mW / cm² 2 , about 120mW / cm 2 From approximately 180 mW / cm² 2 , about 120mW / cm 2 From approximately 190 mW / cm² 2 , about 130mW / cm 2 From approximately 140 mW / cm² 2 , about 130mW / cm 2 From approximately 150 mW / cm² 2 , about 130mW / cm2 From approximately 160 mW / cm² 2 , about 130mW / cm 2 From approximately 170 mW / cm² 2 , about 130mW / cm 2 From approximately 180 mW / cm² 2 , about 130mW / cm 2 From approximately 190 mW / cm² 2 , about 140mW / cm 2 From approximately 150 mW / cm² 2 , about 140mW / cm 2 From approximately 160 mW / cm² 2 , about 140mW / cm 2 From approximately 170 mW / cm² 2 , about 140mW / cm 2 From approximately 180 mW / cm² 2 , about 140mW / cm 2 From approximately 190 mW / cm² 2 , about 150mW / cm 2 From approximately 160 mW / cm² 2 , about 150mW / cm 2 From approximately 170 mW / cm² 2 , about 150mW / cm 2 From approximately 180 mW / cm² 2 , about 150mW / cm 2 From approximately 190 mW / cm² 2 , about 160mW / cm 2 From approximately 170 mW / cm² 2 , about 160mW / cm 2 From approximately 180 mW / cm² 2 , about 160mW / cm 2 From approximately 190 mW / cm² 2 , about 170mW / cm 2 From approximately 180 mW / cm² 2 , about 170mW / cm 2 From approximately 190 mW / cm² 2 , or approximately 180 mW / cm² 2 From approximately 190 mW / cm² 2 It provides intensity from the ultrasonic radiation element.

[0038] The ultrasonic transducer arrays disclosed herein provide appropriately timed ultrasonic stimulation pulses from ultrasonic emitting elements designed to provide optimal beam shape, steering range, and power output for effectively stimulating specific brain regions spatially and temporally. In aspects of this embodiment, the ultrasonic transducer arrays disclosed herein provide ultrasonic stimulation pulses from ultrasonic emitting elements for, for example, about 50 milliseconds, about 100 milliseconds, about 150 milliseconds, about 200 milliseconds, about 250 milliseconds, about 300 milliseconds, about 350 milliseconds, about 400 milliseconds, about 450 milliseconds, or about 500 milliseconds. In other aspects of this embodiment, the ultrasonic transducer arrays disclosed herein provide ultrasonic stimulation pulses from ultrasonic emitting elements for, for example, at least 50 milliseconds, at least 100 milliseconds, at least 150 milliseconds, at least 200 milliseconds, at least 250 milliseconds, at least 300 milliseconds, at least 350 milliseconds, at least 400 milliseconds, at least 450 milliseconds, or at least 500 milliseconds. In yet another embodiment of this embodiment, the ultrasonic transducer array disclosed herein provides ultrasonic stimulation pulses from ultrasonic emitting elements for, for example, up to 50 milliseconds, up to 100 milliseconds, up to 150 milliseconds, up to 200 milliseconds, up to 250 milliseconds, up to 300 milliseconds, up to 350 milliseconds, up to 400 milliseconds, up to 450 milliseconds, or up to 500 milliseconds.In yet another embodiment of this embodiment, the ultrasonic transducer array disclosed herein is, for example, about 50 milliseconds to about 100 milliseconds, about 50 milliseconds to about 150 milliseconds, about 50 milliseconds to about 200 milliseconds, about 50 milliseconds to about 250 milliseconds, about 50 milliseconds to about 300 milliseconds, about 50 milliseconds to about 350 milliseconds, about 50 milliseconds to about 400 milliseconds, about 50 milliseconds to about 450 milliseconds, about 50 milliseconds to about 500 milliseconds, about 100 milliseconds to about 150 milliseconds, about 100 milliseconds to about 200 milliseconds, about 100 milliseconds to about 250 milliseconds, about 100 milliseconds to about 300 milliseconds, about 100 milliseconds to about 350 milliseconds, about 100 milliseconds to about 4 Provides ultrasonic stimulation pulses from an ultrasonic emitting element for 00 milliseconds, approximately 100 to 450 milliseconds, approximately 100 to 500 milliseconds, approximately 150 to 200 milliseconds, approximately 150 to 250 milliseconds, approximately 150 to 300 milliseconds, approximately 150 to 350 milliseconds, approximately 150 to 400 milliseconds, approximately 150 to 450 milliseconds, approximately 150 to 500 milliseconds, approximately 200 to 250 milliseconds, approximately 200 to 300 milliseconds, approximately 200 to 350 milliseconds, approximately 200 to 400 milliseconds, approximately 200 to 450 milliseconds, or approximately 200 to 500 milliseconds.

[0039] The ultrasonic transducer arrays disclosed herein are configured such that each ultrasonic emitting element within the ultrasonic transducer array is spaced apart in a manner designed to provide optimal beam shape, spatial focus, and power output for effectively stimulating specific brain regions spatially and temporally. In aspects of this embodiment, the ultrasonic transducer array has a length of 20 mm, 25 mm, 30 mm, 35 mm, 40 mm, 45 mm, 50 mm, 55 mm, 60 mm, 65 mm, 70 mm, 75 mm, or 80 mm, and the ultrasonic emitting elements contained therein are arranged at equal intervals from one another.

[0040] Aspects of this specification disclose neuromodulation devices comprising electroencephalogram (EEG) electrodes. The neuromodulation devices disclosed herein comprise a plurality of EEG electrodes designed to provide optimal measurement of EEG activity, including but not limited to wave frequency, wave amplitude, and waveform, for effectively segmenting one or more characteristics, phases, or states of brain activity. The EEG electrodes may be dry or wet electrodes. The neuromodulation devices disclosed herein may further include one or more programmable gain amplifiers for amplifying the signals obtained from the plurality of EEG electrodes. Thus, the arrangement of the EEG electrodes, the number of EEG electrodes, the sensitivity of the EEG electrodes, the spacing between each EEG electrode, the location of signal amplification, and the capacitance of the one or more gain amplifiers are each related to the optimal functionality of the neuromodulation devices disclosed herein. In some embodiments, the EEG electrodes disclosed herein are interchangeable snaps on a conductive material with preamplifiers integrated into a headband. EEG cabling is also located within the housing unit. Both the ultrasonic and EEG cabling exit the wearable through ports near the back of the head and enter ports on the control unit.

[0041] As shown in Figures 1A-B, the neuromodulation device 110 includes electroencephalogram (EEG) electrodes positioned on the inner surface of the main band 122 and configured to interface with the user's skull. In some embodiments, the neuromodulation device 110 includes a single EEG electrode positioned on the anterior part of the main band 122, which is positioned on the user's forehead above the eyebrows. In some embodiments, the neuromodulation device 110 includes multiple EEG electrodes, each positioned on the anterior part of the main band 122, which is positioned on the user's forehead above the eyebrows. In aspects of these embodiments, as shown in Figures 1A-B, the neuromodulation device disclosed herein includes two EEG electrodes 150, each positioned on the anterior part of the main band 122, one positioned above the user's left eyebrow and the other above the user's right eyebrow.

[0042] A single or multiple electroencephalogram (EEG) electrodes including the neuromodulation devices disclosed herein provide sufficient sensitivity to provide optimal measurement of EEG activity, including but not limited to wave frequency, wave amplitude, and waveform type, in order to effectively identify one or more characteristics, phases, or states of brain activity. In aspects of this embodiment, the neuromodulation devices disclosed herein comprise multiple EEG electrodes having sufficient sensitivity to detect and measure alpha waves, theta waves, delta waves, sleep spindles, K-complex waves, or any combination thereof.

[0043] The neuromodulation device 110 may comprise a planar, open-curve arc, or closed-curve arc configuration of electroencephalogram (EEG) electrodes. The planar, open-curve arc, or closed-curve arc configuration of EEG electrodes is designed to provide optimal measurement of EEG activity, including but not limited to wave frequency, wave amplitude, and waveform type, in order to effectively identify one or more characteristics, phases, or states of brain activity. In some embodiments, the neuromodulation devices disclosed herein are one-dimensional planar, curved, or closed-curve arc configurations of EEG electrodes. In some embodiments, each EEG electrode may be controlled separately or in a cluster to reduce cabling.

[0044] In some embodiments, the neuromodulation devices disclosed herein include a two-dimensional plane, an open curve, or a closed curve configuration of electroencephalogram (EEG) electrodes. In aspects of these embodiments, the two-dimensional plane, an open curve, or a closed curve configuration of the neuromodulation device may comprise, for example, two, three, four, or five rows of EEG electrodes. In other embodiments of these embodiments, the two-dimensional plane, a curve, or a closed curve configuration of the neuromodulation device may comprise, for example, at least two, at least three, at least four, or at least five rows of EEG electrodes. In yet another embodiment of these embodiments, the two-dimensional plane, a curve, or a closed curve configuration of the neuromodulation device may include, for example, up to two, up to three, up to four, or up to five rows of EEG electrodes. In yet another embodiment of these embodiments, the two-dimensional planar, curved, or closed curve arc configuration of the neuromodulatory device may include, for example, two-three rows of electroencephalogram electrodes, two-four rows of electroencephalogram electrodes, two-five rows of electroencephalogram electrodes, three-four rows of electroencephalogram electrodes, three-five rows of electroencephalogram electrodes, or four-five rows of electroencephalogram electrodes.

[0045] The number of electroencephalogram (EEG) electrodes in a neuromodulation device disclosed herein is a number designed to provide an optimal measurement of EEG activity, including but not limited to wave frequency, wave amplitude, and waveform type, in order to effectively identify one or more characteristics, phases, or states of brain activity. In aspects of this embodiment, the neuromodulation device comprises, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 EEG electrodes. In aspects of this embodiment, the neuromodulation device includes, for example, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 EEG electrodes. In further aspects of this embodiment, the neuromodulation device includes, for example, up to 1, up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, or up to 10 EEG electrodes. In a further embodiment of this embodiment, the neuromodulation device is, for example, 2-3 electroencephalogram electrodes, 2-4 electroencephalogram electrodes, 2-5 electroencephalogram electrodes, 2-6 electroencephalogram electrodes, 2-7 electroencephalogram electrodes, 2-8 electroencephalogram electrodes, 2-9 electroencephalogram electrodes, 2-10 electroencephalogram electrodes, 3-4 electroencephalogram electrodes, 3-5 electroencephalogram electrodes, 3-6 electroencephalogram electrodes, 3-7 electroencephalogram electrodes, 3-8 electroencephalogram electrodes, 3-9 electroencephalogram electrodes, 3-10 electroencephalogram electrodes, 4-5 electroencephalogram electrodes, 4-6 electroencephalogram electrodes, Includes 4-7 EEG electrodes, 4-8 EEG electrodes, 4-9 EEG electrodes, 4-10 EEG electrodes, 5-6 EEG electrodes, 5-7 EEG electrodes, 5-8 EEG electrodes, 5-9 EEG electrodes, 5-10 EEG electrodes, 6-7 EEG electrodes, 6-8 EEG electrodes, 6-9 EEG electrodes, 6-10 EEG electrodes, 7-8 EEG electrodes, 7-9 EEG electrodes, 7-10 EEG electrodes, 8-9 EEG electrodes, 8-10 EEG electrodes, or 9-10 EEG electrodes.

[0046] In some embodiments, the neuromodulation device disclosed herein includes a main band positioned on the user's forehead above the eyebrows and a plurality of electroencephalogram (EEG) electrodes positioned on either side of the main band in the left and right temple regions above the user's ears. In aspects of these embodiments, the neuromodulation device disclosed herein includes four EEG electrodes, two of which are positioned on the main band above the user's left eyebrow and the other above the user's right eyebrow, one EEG electrode positioned on the left side of the main band in the left temple region above the user's ear, and one EEG electrode positioned on the left side of the main band in the right temple region above the user's ear. In other embodiments of these embodiments, the neuromodulation device disclosed herein comprises six electroencephalogram (EEG) electrodes, with two EEG electrodes positioned in front of the main band, one above the user's left eyebrow and the other above the user's right eyebrow; two EEG electrodes positioned to the left of the main band, located in the left temple area above the user's ear; and two EEG electrodes positioned to the left of the main band, located in the right temple area above the user's ear. In yet another embodiment of these embodiments, the neuromodulation device disclosed herein comprises eight EEG electrodes, with four EEG electrodes positioned in front of the main band, two arrays positioned above the user's left eyebrow and two arrays positioned above the user's right eyebrow; two EEG electrodes positioned to the left of the main band, located in the left temple area above the user's ear; and two EEG electrodes positioned to the left of the main band, located in the right temple area above the user's ear.

[0047] In some embodiments, the neuromodulation device disclosed herein includes a single electroencephalogram (EEG) electrode positioned in front of a main band located on the user's forehead above the eyebrows, and a single ultrasonic transducer array positioned on either side of the main band located in the left and right temple regions above the user's ears. In some embodiments, the neuromodulation device disclosed herein includes a plurality of EEG electrodes positioned in front of a main band located on the user's forehead above the eyebrows, and a plurality of ultrasonic transducer arrays positioned on either side of the main band located in the left and right temple regions above the user's ears. In aspects of these embodiments, the neuromodulation device disclosed herein comprises two EEG electrodes positioned in front of a main band, one positioned above the user's left eyebrow and the other above the user's right eyebrow, and a single ultrasonic transducer array positioned on either side of the main band located in the left and right temple regions above the user's ears. In other embodiments of these embodiments, the neuromodulation devices disclosed herein include two electroencephalogram electrodes positioned in front of the main band, one positioned above the user's left eyebrow and the other above the user's right eyebrow, and two ultrasonic transducer arrays positioned to the left of the main band, and two ultrasonic transducer arrays positioned to the right of the main band.

[0048] In aspects of this embodiment, the neuromodulation device disclosed herein comprises a plurality of electroencephalogram (EEG) electrodes having sufficient sensitivity to detect and measure EEG frequencies of, for example, at least 0.1 Hz, at least 0.2 Hz, at least 0.25 Hz, at least 0.3 Hz, at least 0.4 Hz, or at least 0.5 Hz. In other aspects of this embodiment, the neuromodulation device disclosed herein comprises a plurality of EEG electrodes having sufficient sensitivity to detect and measure EEG frequencies of, for example, 0.1 Hz to 50 Hz, 0.1 Hz to 60 Hz, 0.1 Hz to 75 Hz, 0.25 Hz to 50 Hz, 0.25 Hz to 60 Hz, 0.25 Hz to 75 Hz, 0.5 Hz to 50 Hz, 0.5 Hz to 60 Hz, or 0.5 Hz to 75 Hz.

[0049] In aspects of this embodiment, the electroencephalogram monitoring array disclosed herein provides sufficient sensitivity to detect and measure electroencephalogram amplitudes from electroencephalogram electrodes of, for example, at least 5 μV, at least 25 μV, or at least 50 μV. In other aspects of this embodiment, the electroencephalogram monitoring array disclosed herein provides sufficient sensitivity to detect and measure electroencephalogram amplitudes from electroencephalogram electrodes of, for example, 5 μV to 500 μV, 5 μV to 750 μV, or 5 μV to 1,000 mV, 25 μV to 500 μV, 25 μV to 750 μV, 25 μV to 1,000 mV, 50 μV to 500 μV, 50 μV to 750 μV, or 50 μV to 1,000 mV.

[0050] The neuromodulation devices disclosed herein are configured to provide spacing between each electroencephalogram (EEG) electrode to provide optimal measurement of EEG activity, including but not limited to wave frequency, wave amplitude, and waveform type, in order to effectively identify one or more characteristics, phases, or states of brain activity. In aspects of this embodiment, the neuromodulation device comprises a plurality of EEG electrodes, each equally spaced apart from the others. In aspects of this embodiment, the neuromodulation device comprises a plurality of EEG electrodes, each spaced apart from the others by, for example, about 20 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, about 45 mm, about 50 mm, about 55 mm, or about 60 mm. In other aspects of this embodiment, the neuromodulation device comprises a plurality of EEG electrodes, each spaced apart from the others by, for example, at least 20 mm, at least 25 mm, at least 30 mm, at least 35 mm, at least 40 mm, at least 45 mm, at least 50 mm, at least 55 mm, or at least 60 mm. In yet another aspect of this embodiment, the neuromodulation device comprises a plurality of electroencephalogram (EEG) electrodes, each EEG electrode spaced apart by, for example, up to 20 mm, up to 25 mm, up to 30 mm, up to 35 mm, up to 40 mm, up to 45 mm, up to 50 mm, up to 55 mm, or up to 60 mm. In yet another aspect of this embodiment, the neuromodulation device comprises a plurality of EEG electrodes, each EEG electrode spaced apart by, for example, about 20 mm to about 30 mm, about 20 mm to about 40 mm, about 20 mm to about 50 mm, about 20 mm to about 60 mm, about 30 mm to about 40 mm, about 30 mm to about 50 mm, about 30 mm to about 60 mm, about 40 mm to about 50 mm, about 40 mm to about 60 mm, or about 50 mm to about 60 mm.

[0051] The neuromodulation devices disclosed herein further include conductive wiring. Such conductive wiring may be located outside the device housing or embedded within the wearable device housing 120, for example, within a channel, and exit the housing through a port on the rear. In some embodiments, the conductive wiring exits from a cable port 160 in an anterior-posterior direction parallel to the skull, allowing the user to lie on their back with their back to the flush wire. The conductive wiring disclosed herein can power and bundle together the electroencephalogram amplification stages for each electroencephalogram electrode 150, each ultrasonic transducer array 140, the stimulation control unit 200 and its associated processing elements and functions, as well as other components of the neuromodulation device 110. In some embodiments, the conductive wiring runs through a channel in the main band 122 and connects each electroencephalogram electrode 150 to one or more amplifiers, digital-to-analog converters, and the stimulation control unit 200 before exiting through a cable port 160 located at the rear of the main band 122. In some embodiments, with respect to each ultrasonic transducer array 140, conductive wiring runs through channels in the main band 122 and connects each ultrasonic transducer array 140 to the stimulation control unit 200 before exiting through a cable port 160 located at the rear of the main band 122.

[0052] Aspects of this specification disclose a neuromodulation system comprising a stimulus control computing environment including a stimulus control unit and an offline computing device. Referring to Figure 1A, the neuromodulation system 100 further includes a stimulus control unit 200 located on or connected to the main band 122 via a cable port 160 and conductive wiring 210. The stimulus control unit 200 comprises a central control ASIC processor, printed circuit board (PCB) components including an ultrasonic phase control component, one or more signal amplifiers, an ultrasonic matching network, and a power supply and other processors. The ASIC chip processes electroencephalogram data, ultrasonic state data, target phase data of ultrasonic emission elements, power usage, and data storage. The ASIC processor transmits information about element phases that trigger the ultrasonic phase control component and one or more signal amplifiers of the PCB components. The PCB components then transmit signals to the ultrasonic matching network to reduce reflections from acoustic impedance mismatches, and then to each ultrasonic emission element 142 of the ultrasonic transducer array 140 to enable beam steering in the neuromodulation device 110. The battery unit included in the stimulation control unit 200 has a current and voltage appropriately rated for the requirements of the neuromodulation device 110. The stimulation control unit 200 uses an input file relating to the phase delay of each target structure, which may be a single target and a subdivision of the stimulation protocol for each target. This file is loaded via a bus interface such as a LIGHTNING connector, micro-USB connector, or USB-C connector and derived through acoustic simulations performed on a set of brain images of a user wearing the neuromodulation device 110. The simulation maps the patient's target brain region to the ultrasound emission elements 142 of each ultrasound transducer array 140 and appropriately phase-corrects the timing of each element so that the beam focuses on the target. An exemplary algorithmic framework of the stimulation control unit disclosed herein is shown in Figure 2.

[0053] Referring to Figure 1A, the stimulus-controlled computing environment disclosed herein also includes an offline computing device 250 and comprises an algorithmic framework including one or more processors and multiple software and hardware components (including digital-to-analog converters, function generators, and hard drives) configured to perform data processing and performance functions that execute program instructions or routines to control the operability of the neuromodulator disclosed herein.

[0054] The algorithmic framework of the stimulus control unit 200 and software elements disclosed herein is part of one or more systems and methods that apply mathematical functions, models, or other analytical and data processing techniques in real time, ensuring that the neuromodulation device disclosed herein applies ultrasound stimulation to one or more specific regions of the brain individually and differentially in an appropriate spatial and temporal manner in response to brain activity data obtained by electroencephalogram electrodes. The software elements include offline elements referred to herein as offline algorithmic mapping elements 300 and online elements referred to herein as online algorithmic stimulus application elements 310.

[0055] The systems and methods for modulating the operation of neural modulation devices disclosed herein can be implemented in many different computing environments. For example, the permitted distributed ledger can be implemented in combination with a dedicated computer, a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, an electronic or logic circuit such as discrete element circuits, a programmable logic device or gate array such as a PLD, PLA, FPGA, PAL, and equivalent means. In general, various aspects of the invention can be implemented using any means that implement the methodology shown herein. Exemplary hardware that can be used in the invention includes computers, handheld devices, telephones (e.g., cellular, internet-enabled, digital-analog, hybrid, etc.), and other such hardware. Some of these devices include processors (e.g., one or more microprocessors), memory, non-volatile storage, input devices, and output devices. Furthermore, alternative software implementations, including but not limited to distributed processing, parallel processing, or virtual machine processing, can also be configured to perform the methods described herein.

[0056] The systems and methods for modulating the operation of neuromodulatory devices disclosed herein can also be partially implemented in software that can be stored in a storage medium and run on a general-purpose computer, dedicated computer, microprocessor, etc., programmed in cooperation with a controller and memory. In these examples, the systems and methods of the present invention can be implemented as programs embedded in a personal computer, such as applets, JAVA(R) or CGI scripts, as resources residing on a server or computer workstation, or as routines incorporated into a dedicated measurement system, system component, etc. The system can also be implemented by physically incorporating the system and / or method into a software and / or hardware system.

[0057] Furthermore, the data processing functions disclosed herein may be performed by one or more program instructions stored in or executed by such memory, or by one or more modules configured to execute those program instructions. Modules are intended to refer to known or future hardware, software, firmware, artificial intelligence, fuzzy logic, expert systems, or combinations of hardware and software capable of performing the data processing functions described herein. The device includes three main software modules: an electroencephalogram (EEG) controller module with sleep stage classification and slow-wave analysis capabilities; an ultrasound module for controlling ultrasound beamforming and stimulation patterns; and a data logging module for capturing all interaction data for optimization (Figure 3A-B).

[0058] The stimulation control unit disclosed herein has capacity for temporary and long-term data storage to available hard drive space. Depending on the memory capacity of the stimulation control unit, data can be downsampled using time-window averaging before being written to each file. Stored data includes, but is not limited to, timestamps, real-time information received by the electroencephalogram electrodes disclosed herein, information generated by the stimulation control unit disclosed herein, and information received by the ultrasound transducer array disclosed herein.

[0059] The electroencephalogram (EEG) electrode information preferably includes EEG data from each electrode, stored as a microvolt time series sampled at 256 Hz. The ultrasound transducer array information preferably includes ultrasound stimulation information, stored as a 256 Hz binary time series with a stimulation delivery status, where, for example, 1 reflects that the ultrasound delivery waveform is on and 0 reflects that the ultrasound delivery waveform is off. The voltage waveform pattern delivered to each ultrasound transducer element during the stimulation period is unique to each user and can be stored in the stimulation control unit in a standard format. This file may include, for example, the phase delay, fundamental frequency, burst modulation frequency, and burst interval of the ultrasound transducer element. In some cases, several of these files may exist that create specific foci in brain space. For example, one file may be created to focus on the medial thalamic center, and another file may be created to focus on the reticular nucleus of the thalamus.

[0060] The stimulation control unit may further include one or more systems and methods for applying mathematical functions, real-time models or other analytical and data processing techniques for battery management, data offloading / onloading, or other operations designed for the neuromodulatory devices disclosed herein. Regarding user-specific data offloading / onloading, the stimulation control unit may be connected to the internet, and the data storage of each neuromodulatory device may be automatically scanned for new data files and offloaded as needed.

[0061] An exemplary system and method for modulating the operation of the neuromodulation device disclosed herein is shown in Figure 3. Brain activity data, including frequency, amplitude, and waveform, is continuously collected by electroencephalogram electrodes disclosed herein, and the collected data is amplified. This amplified brain activity data is passed through a digital-to-analog converter (DAC) board and then relayed to one or more processors of a stimulation control unit. The stimulation control unit then runs brain activity software to analyze this EEG information to classify brain activity and, if specified criteria are met, initiates a protocol for administering ultrasonic stimulation. The stimulation control unit then performs phase-corrected beam steering based on offline phase commands generated by an offline algorithm mapping element 300. These commands are generated offline using brain imaging data and subbrain structure mapping software that analyzes the anatomical structure of the user's skull to identify one or more specific regions of the brain and precisely identifies the target location designated for ultrasonic stimulation. The phase correction command for each element is determined by performing acoustic simulations on the brain imaging data to predict the optimal focusing parameters. Once these brain regions are mapped, the stimulation control unit disclosed herein instructs the ultrasonic transducer array disclosed herein to deliver ultrasonic signals to these mapped brain regions for a specified period of time, with or without signal amplification. A constant input from the electroencephalogram electrodes disclosed herein results in a continuous initiation and / or modulation from the stimulation control unit disclosed herein, which coordinates the application of ultrasound by the ultrasonic transducer array disclosed herein. This provides a feedback mechanism that enables timely, effective, and precise ultrasonic stimulation to one or more specific brain regions requiring such stimulation.

[0062] Aspects of this specification partially disclose a system and method for processing real-time information acquired by electroencephalogram electrodes disclosed herein in an online algorithmic stimulation application element 310. The disclosed system and method for processing this real-time electroencephalogram information includes brain activity analysis software that evaluates the electroencephalogram information to classify brain activity and determine whether the measured brain activity meets specified criteria, and if such criteria are met, initiates a protocol for administering ultrasound stimulation, including subbrain structure mapping and ultrasound administration.

[0063] In some embodiments, brain activity analysis software evaluates brain activity to classify sleep stages in the sleep cycle in order to determine slow wave signals indicating non-REM sleep stage N2 or non-REM sleep stage N3. The device identifies the current phase of slow waves for appropriate application of ultrasonic stimulation using an algorithm partially shown in Figure 2. In this application, the phase with respect to the EEG measurement refers to a point along the cycle or oscillation of the recorded brain region between positive and negative measured voltages. To identify the current slow wave phase during device operation, spectral analysis is performed on the most recent segment of the acquired EEG signal; this is typically the last 4 seconds of the acquired signal, but is not limited to this. Spectral analysis is performed on this segment to determine the contribution of all EEG frequencies known as frequency domains. In one aspect of this embodiment, spectral analysis is performed using Fast Fourier Transform (FFT) analysis to determine the frequency domains. The system then determines the dominant slow wave frequency by determining the maximum contributing frequency from 0.5 Hz to 2 Hz. This dominant slow wave frequency is then used to determine the current slow wave phase. The signal can be bandpass filtered with respect to the dominant frequency to remove irrelevant signals that are larger or smaller than the dominant signal. Several methods can be used to determine the phase of the filtered or unfiltered signal. In one embodiment, a sinusoidal function with a dominant frequency is fitted to the filtered electroencephalogram (EEG) signal. The current slow wave phase is determined by the termination phase of the sinusoidal function fitted to the EEG signal. If signal acquisition is delayed, the sinusoidal function will extend beyond the data, and the future phase can be predicted. Alternatively, a phase-locked loop can be used. A phase-locked loop is a type of control system that detects the phase difference between a reference signal and an input signal, allowing the system to effectively identify the occurrence of peaks and troughs in the waveform signal over time. Once the phase is determined by one of these methods, or any other suitable method, the system delivers an ultrasonic stimulus if the phase meets one or more specific criteria as shown in Figures 4A-E.This means the phase may match, or be within the range of, a given number of degrees or radians of the “up state” where the measured voltage is most positive. This “up state” is generated by thalamic bursting activity involving the corticothalamic loop. Alternatively, the criterion may be that the phase matches the “down state” where the corticothalamic loop is inactive.

[0064] Depending on the ultrasound response of the target cell type, the relative phase of administration may vary. In aspects of this embodiment, the protocol for delivering ultrasound stimulation is initiated when the phase of the slow wave frequency is, for example, about 50°, about 55°, about 60°, about 65°, about 70°, about 75°, about 80°, about 85°, about 90°, or about 95° from the peak slow wave frequency. In other aspects of this embodiment, the protocol for delivering ultrasound stimulation is initiated when the phase of the slow wave frequency is, for example, at least 50°, at least 55°, at least 60°, at least 65°, at least 70°, at least 75°, at least 80°, at least 85°, at least 90°, or at least 95° from the peak slow wave frequency. In yet another embodiment of this embodiment, the protocol for administering ultrasonic stimulation is initiated when the phase of the slow wave frequency is, for example, up to 50°, up to 55°, up to 60°, up to 65°, up to 70°, up to 75°, up to 80°, up to 85°, up to 90°, and up to 95° from the peak slow wave frequency. In yet another embodiment of this embodiment, a protocol for administering ultrasonic stimulation is initiated, and the phase of the slow wave frequency is, for example, about 50° to about 60°, about 50° to about 70°, about 50° to about 80°, about 50° to about 90°, about 50° to about 100°, about 60° to about 70°, about 60° to about 80°, about 60° to about 90°, about 60° to about 100°, about 70° to about 80°, about 70° to about 90°, about 70° to about 100°, about 80° to about 90°, about 80° to about 100°, or about 90° to about 100°.

[0065] Aspects of this specification partially disclose systems and methods for mapping one or more specific regions of the brain. The disclosed systems and methods for mapping one or more specific regions of the brain include subbrain structure mapping software that identifies one or more specific regions of the brain and precisely plots one or more target locations designated for ultrasound stimulation, as shown in Figures 5A–J. The ultrasound focusing of the neuromodulation device disclosed herein relies on the convergence of coordinated interference of acoustic waves from different pressure sources along an ultrasound transducer array, which are affected by the nonlinearity of the user's skull thickness and brain morphology, and the nonlinearity of the angle of incidence to the skull. Furthermore, such heterogeneous skull and brain morphology may vary considerably among users. The subbrain structure mapping software disclosed herein models optimal ultrasound focusing parameters tailored to each user's unique cranial morphology by identifying and taking into account these nonlinearities and variations when plotting solutions for appropriately steering ultrasound stimulation of specific regions of the brain.

[0066] The subbrain mapping software disclosed herein identifies one or more specific regions to be targeted by ultrasound stimulation. In some embodiments, one or more specific regions of the brain are identified by comparing brain image scans with publicly available or internally annotated brain atlases to identify a common coordinate space. Brain image scans include scans generated by computed tomography (CT) and magnetic resonance imaging (MRI). Non-limited sources of such brain image scans include scans obtained from users of the neuromodulation devices disclosed herein (personalized models customized for specific users), scans obtained from anonymized individuals through healthcare facilities, or scans obtained from anonymized individuals through registries such as Human Connectome. Brain image scans are registered in a common brain region atlas, and image segmentation is performed to identify the centroid of the voxel space of one or more specific regions to be targeted by ultrasound stimulation. In some embodiments, the target brain region is the thalamus. In some embodiments, one or more specific regions targeted by ultrasound stimulation are subregions of the thalamus, including but not limited to the central nucleus of the thalamus, the reticular thalamus, or the lateral thalamus, or several combinations thereof.

[0067] In some embodiments, one or more specific regions of the brain are identified using biometric parameters that coordinate with brain regions. The target site may be estimated as a point in 3D space relative to biometrics that have some predictive value about the location of the target site. This biometrics may include, but are not limited to, the locations of a person's eyes, ears, eyebrows, nose, mouth, jawline, or other appendages relative to cranial landmarks. This biometrics may also include midpoints defined in small portions along the forehead, relative points along cranial feature axes such as between the ears and eyes, from the corners of the mouth to the base of the ears, or other combinations of cranial features or appendages.

[0068] Once one or more specific brain regions are identified, the subbrain mapping software disclosed herein identifies the coordinate space of each transducer element in the image data. Next, the software accurately calculates the time-phase offset of the ultrasonic emitting elements based on estimating the acoustic time-path length between the element and a target at one or more identified locations, or by performing a full-wave simulation. First, the software determines the acoustic impedance by employing an algorithm that converts pixels of the brain image scan from a brain modeling database into measured acoustic impedance (Houndsfield units). Next, the subbrain mapping software disclosed herein determines the beam steering required to effectively apply ultrasonic stimulation to a given region of the brain. In some embodiments, the required beam steering is determined by modeling a simulation of the wave equation by estimating the time-wavelength path length to the target focal point, addressing the difference in sound velocity across the skull and tissue, as well as wave refraction. The simulation then adjusts the excitation phase delay of each ultrasonic transducer element until the wavefronts constructively interfere at the focal point.

[0069] In some embodiments, a model using acoustic simulation software provides a three-dimensional matrix of beam characteristics given specific phase and power inputs applied to an ultrasonic transducer element. This may include the maximum possible power distribution ratio between on-target and off-target structures within the x, y, and z domains, beam deformation characteristics at large steering angles, and / or the minimum achievable focal size. In some embodiments, the target brain region can be determined by repeated stimulation at different locations in the region that should contain the target region. Using this method, a general target space can be estimated from a focal distribution based on a brain-skull model database or user data, as shown in Figures 5A–J. This space may have a probabilistic feature where certain layers of the 3D coordinate space are more likely to contain the target region than other layers. The transducer can be programmed to scan this 3D space incrementally while measuring biological readings. The scanning coordinate space may be equally spaced, giving each point an equal inspection weight, or it may be unequal to reflect the probability that the target region is located at a given coordinate. The coordinate space can be static throughout the test, or it can be dynamic to reflect positive or negative biological readings at each coordinate during the test. Biological readings can be electroencephalogram features such as slow-wave amplitude, or subjective measures described by the user, such as descriptions of sensory experiences or mental states. Readings can be made once, multiple times, or a weighted number of times based on the probability space at each coordinate.

[0070] In some embodiments, the subbrain structure mapping software disclosed herein determines the required beam steering by plotting the maximum lateral steering angle from a fixed reference point on the user's skull to the edge of the target brain region. In aspects of these embodiments, the subbrain structure mapping software disclosed herein determines the required beam steering by plotting the maximum angle from the ultrasound transducer array to the maximum and minimum lateral steering angles of the target brain region.

[0071] In some embodiments, the hypobrain mapping software disclosed herein maps the location of the thalamus. For example, as shown in Figures 5A–B, the location of the thalamus 610 is mapped using a brain modeling database, the maximum angles relative to the most posterior and anterior parts of the thalamus are calculated on a transformed MRI scan by marking the anterior commissure as a control point (Figure 5A, crosshairs) and the placement of the ultrasound transducer array 140 of the neuromodulation device 110 (Figure 5B) as a setpoint, and the dashed lines indicate the focal steering angle. As shown in Figures 5C–D, the projection of ultrasound stimulation from the neuromodulation device disclosed herein established a pressure field involving only a portion of the thalamus with the highest intensity portion of the field. In another example, as shown in Figures 5E and 5H, the hypobrain mapping software disclosed herein maps the location of the thalamus (purple) using a brain modeling database and then precisely plots the application of ultrasound stimulation to the thalamus, as shown in Figures 5F and 5I.

[0072] In some embodiments, the subbrain mapping software disclosed herein creates a file format that includes user-specific information about the target brain region and the phase delay required for each ultrasound transducer element.

[0073] The stimulation control units of neuromodulation devices disclosed herein include the implementation of artificial intelligence in systems and methods that apply one or more machine learning techniques for determining sleep stages, analyzing electroencephalogram spectra, and controlling ultrasonic emission elements accordingly. The present invention intends that many different types of artificial intelligence, more specifically machine learning, may be used, and they are within its scope. Applications of artificial intelligence and machine learning may include one or more such types of artificial intelligence in addition to, or instead of, neural networks. These include, but are not limited to, instantiations of one or more other types of machine learning paradigms, such as k-nearest neighbors (KNN), logistic regression, support vector machines or networks (SVM), supervised learning, unsupervised learning, deep learning, and reinforcement learning. In any case, the use of artificial intelligence and machine learning in the algorithmic framework of the present invention enhances the usefulness of the data processing functions performed therein by automatically and heuristically constructing appropriate relationships mathematically or otherwise in relation to the complex interactions between data obtained from multiple sensors and other input data used by the stimulation control unit, thereby arriving at the most appropriate response to specific vehicular operating conditions.

[0074] Aspects of this specification also disclose stimulus control units that include machine learning elements such as deep learning models for sleep stage prediction. Such applications of the artificial intelligence system in the present invention include automatically monitoring, classifying, and quantifying electroencephalogram (EEG) information to predict a user's sleep stage in real time. The deep learning model for sleep stage prediction first uses representation learning to extract useful features from raw EEG data using a convolutional neural network (CNN), detecting features in the raw data, for example, by using 1D convolution on the raw EEG or 2D convolution on the spectrogram. The deep learning model for sleep stage prediction then employs sequence residual learning using recurrent and fully connected cell layers to classify the features extracted from the first part into sleep stages. Using recurrent cells allows for the consideration of the time dimension in the problem. The deep learning model for sleep stage prediction also includes an error correction layer that handles motion artifacts and other external noise using an encoder / decoder approach.

[0075] In some embodiments, as shown in Figures 7A-B, a deep learning model for sleep stage prediction includes one multi-branch using a four-signal (EEG, EOG-R, EOG-L, and EMG) input architecture. The multi-branch deep learning model for sleep stage prediction consists of four branches of a convolutional neural network and an LSTM. Tensors are concatenated at the ends of the branches and fed into fully connected layers. This final fully connected layer can be fine-tuned to enable personalization through transfer learning. A bidirectional LSTM is used as a baseline for evaluating a single EEG signal architecture.

[0076] In some embodiments, as shown in Figures 8A-C, the deep learning model for sleep stage prediction includes one or two branches using a single-signal (EEG) input architecture. Each branch of the one or two-branch deep learning model for sleep stage prediction is a convolutional neural network with various filter configurations to capture various features from the signal. These tensors are concatenated and then fed to two other branches, one with bidirectional long-short-term memory and the other with fully connected layers. The results are again concatenated and fed to a final fully connected layer.

[0077] In some embodiments, a deep learning model for sleep stage prediction is based on a bidirectional recurrent network with a large amount of short-term memory and trained on thousands of labeled polysomnographs. Such a deep learning model for sleep stage prediction can classify polysomnographs and electroencephalograms in real time and personalize the classification for a given user. In aspects of these embodiments, the deep learning model for sleep stage prediction is based on a bidirectional recurrent neural network (RNN) with or without long-term short-term memory (LSTM).

[0078] In some embodiments, a deep learning model for sleep stage prediction is developed by creating a database of overnight polysomnography based on publicly available information. Such polysomnography can be obtained from healthy individuals as well as from individuals suffering from associated sleep conditions, such as drug-resistant insomnia, REM sleep behavior disorder, or narcolepsy.

[0079] In some embodiments, a deep learning model for sleep stage prediction is developed by creating a personalized database of overnight polysomnography from the same individual. Such polysomnography can be obtained by having the user sleep under controlled conditions and manually labeling them, thereby improving the weights of the final layer by retraining them over several epochs. The personalized database helps to retrain the final layer of the deep learning model to adapt to the differences of each individual, allowing the neuromodulation devices disclosed herein to be personalized to the user.

[0080] Aspects of this specification also disclose a stimulus control unit comprising a deep learning model for tuning ultrasonic stimulation parameters. Such applications of the artificial intelligence system include automatically determining and tuning ultrasonic stimulation parameters in real time that are necessary for modulating brain activity. The deep learning model for tuning ultrasonic stimulation parameters can read information from deep within the brain at an individualized level and, in real time, can instruct the neuromodulation device disclosed herein to deliver the ultrasonic stimulation necessary to achieve a desired outcome. In some embodiments, the deep learning model automatically determines and tunes ultrasonic stimulation parameters in real time that are necessary for modulating brain activity to improve the quality of sleep. In some embodiments, the deep learning model for tuning ultrasonic stimulation parameters includes: 1) reinforcement learning to adapt in real time to changes in device position and other external factors; 2) subsystem routines to control electroencephalogram electrodes; and 3) a data logging module used for training long-term personalization and improving other modules.

[0081] In some embodiments, the neuromodulation devices disclosed herein target the thalamus with focused ultrasound stimulation, as shown in Figures 5A-I. Although there is no direct interaction with the cortex, focused ultrasound stimulation of the thalamus ultimately involves more cortex through the corticothalamic loop. The corticothalamic loop is a circular network of neurons that includes connections between the cortex, basal ganglia, and thalamus, returning to the cortex. The two main pathways in the loop are the striatum and the subthalamic nucleus (STN). The striatum receives excitatory input from the cortex and modulating input from the substantia nigra pars compacta (SNc), while the subthalamic nucleus receives excitatory input only from the cortex. Two pathways emerge from the striatum. One pathway is called the indirect (or NoGo) pathway and projects to and inhibits the lateral globus pallidus (GPe), resulting in disinhibition of the endoglobus pallidus (GPi), which leads to inhibition of the thalamus. This pathway also disinhibits the subthalamic nucleus as a result of inhibiting GPe, which in turn excites GPi and thus inhibits the thalamus. The second pathway is called the direct (or Go) pathway, which projects to and inhibits GPi, resulting in thalamic disinhibition. Thalamic disinhibition leads to stimulation of cortical neurons, while thalamic inhibition prevents such stimulation. The corticothalamic loop receives input not only from peripheral sensory information such as touch and sound, but also from the hypothalamic region that encodes bodily homeostasis. Its projections extend widely into the cortex, as do other sleep- and wakefulness-promoting regions where information oscillates between structures, creating a spatially wide network of information flows.

[0082] The neuromodulation devices disclosed herein are positioned by placing the device housing on the user's head and adjusting the main and secondary bands to position the electroencephalogram electrodes in appropriate locations, such as the forehead and left and right temple regions, and the left and right temporal window regions, for example, an ultrasound transducer array located in appropriate locations. After the device housing is properly adjusted, the position of the neuromodulation device disclosed herein is firmly fixed to ensure proper operation.

[0083] During operation, as shown in Figure 6, the neuromodulation device disclosed herein continuously measures and processes real-time electroencephalogram (EEG) signals from the user's brain to instantaneously identify the user's current sleep stage. In some embodiments, the sleep stage is determined using ratios of distinct spectral components such as delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) powers. When the device classifies the sleep stage as non-REM N2 or non-REM stage N3, the brain activity software of the device disclosed herein then determines the dominant frequency of the FFT within a slow wave frequency range used as an oscillator for determining the slow wave phase using a phase-locked loop. If the phase-locked loop detects a slow wave within a given phase range relative to the peak slow wave frequency, a transducer beam steering parameter targeting the thalamus applies focused ultrasonic stimulation to the thalamus, increasing thalamic activity and thereby enhancing SWS.

[0084] Depending on the device's predetermined operation, the ultrasound waveform can be delivered specifically during the non-REM phase of sleep or during the waking state. When stimulation is assigned to a sleep phase, the device finds the peak spectral component of the FFT. A phase-locked loop algorithm is applied to generate a phase-locked signal relative to the real signal (reference signal) and determine the current phase of the slow wave. If the current phase is within the stimulation range, the ultrasound stimulation is triggered for the inter-packet interval. Once the inter-packet interval has elapsed, the routine is continuously restarted as long as the device is configured to operate.

[0085] Apart from the implementation described above, the neuromodulation device disclosed herein can be used to investigate the optimal spatial parameters of ultrasound stimulation. This device examines the baseline delta power and compares it to the changes in delta power achieved at various focal points in space and at various slow-wave phases of ultrasound delivery. The spatial point can be limited to the space surrounding the thalamus or to the maximum steering capability of the transducer.

[0086] While worn, the neuromodulation device is powered on via a single button on the device. Electroencephalograms (EEGs) are acquired and analyzed at 120 Hz. A central processing control unit uses a gradient boost decision tree algorithm to determine the sleep stage and identify slow waves in real time. If the user is in non-REM stage N2 or N3 and experiencing slow waves, the neuromodulation device sends a single, continuous 100-millisecond pulse of focused ultrasound energy to the thalamus to enhance thalamic activity, then enhances the amplitude of the slow waves and subsequent slow waves. Depending on the width of the thalamus, the device can raster scan the beam over the structure over the entire duration of the stimulation. A timeout period is then set that must elapse before the next stimulation; this ensures that tissue heating is limited to less than 1°C. While worn, EEG time-series data and ultrasound stimulation state time-series data are stored in the central processing unit and offloaded to an external computing device using a bus interface such as a LIGHTNING connector, micro-USB connector, or USB-C connector.

[0087] This specification also discloses methods and uses for preventing and / or treating brain disorders using the neuromodulatory devices disclosed herein. Non-limiting aspects of brain disorders include sleep disorders, psychiatric disorders, metabolic disorders, epilepsy or other seizure disorders, anxiety, depression, and / or sleep disorders of brain disorders associated with neuropathic pain.

[0088] The neuromodulatory devices disclosed herein may be used to prevent or treat sleep disorders or sleep disorder-related disorders that originate from or can be treated by originating from deep brain regions. This includes, but is not limited to, modulation of the activity of the thalamus or thalamic subregions to enhance slow-wave sleep, or generation of sleep spindles to enhance memory, immune function, cognitive function, and general restorative sleep function.

[0089] The neuromodulatory devices disclosed herein may be used to prevent or treat mental disorders that originate from or can be treated by originating from deep brain regions. This includes, but is not limited to, modulation of the activity of the thalamus or thalamic subregions to enhance slow-wave sleep, or generation of sleep spindles to enhance memory, immune function, cognitive function, and general restorative sleep function.

[0090] The neuromodulatory devices disclosed herein may be used to prevent or treat metabolic disorders originating from or that can be treated by originating from deep brain regions. This may include modulation of locus coeruleus activity to increase or decrease arousal. This may also include modulation of hypocretin / orexin neuronal activity in the lateral hypothalamus to modulate arousal, emotional state, or appetite. This may also include modulation of hypothalamic or hypothalamic subregion activity for the treatment of metabolic disorders, increasing or decreasing metabolism, modifying appetite, or regulating body temperature.

[0091] The neuromodulatory devices disclosed herein may be used to prevent or treat epilepsy or seizure disorders that originate from or can be treated by originating from deep brain regions. This may include modulation of the activity of the thalamus or thalamic subregions for the treatment of focal and non-focal seizures or temporal lobe epilepsy.

[0092] The neuromodulatory devices disclosed herein may be used to prevent or treat depression or anxiety that originates from or can be treated by a deep brain region. This may include modulation of amygdala activity to treat and / or modify emotional states such as depression and anxiety.

[0093] This specification also discloses methods and uses of the neuromodulatory devices disclosed herein for promoting healthy brain aging and preventing age-related brain diseases. Such age-related brain diseases may be caused by the accumulation of toxic debris, metabolic disorders, stroke, and neurodegenerative diseases.

[0094] This specification also discloses methods and uses for improving cognitive performance for specific requirements, such as preventing and treating jet lag, inducing hibernation to prevent physical injury (e.g., post-surgery or post-traumatic), space travel, and mental requirements such as those of pilots, soldiers, officers, and students taking exams.

[0095] example The following non-limiting examples are provided for illustrative purposes only to facilitate a more complete understanding of the representative embodiments currently being considered. These examples should not be construed as limiting any of the embodiments described herein, including those relating to the devices, methods, and systems disclosed herein.

[0096] Example 1 Deep learning models for sleep stage prediction A database containing overnight polysomnography data was created from three publicly available datasets representing over 6,700 individual polysomnography entries. From this compiled database, 153 polysomnography entries were extracted from healthy individuals. The Pz-OZ channels of the EEG data were preprocessed with a fourth-order Butterworth bandpass filter centered on the delta, theta, alpha, and beta spectra (1–4Hz, 4–8Hz, 8–13Hz, and 13–30Hz, respectively). From the four resulting signals, four temporal features were extracted per epoch: median amplitude, variance, skewness, and kurtosis; and four spectral features were extracted: spectral edge frequency difference, spectral attenuation, spectral gradient, and spectral spread. The resulting 32 features were concatenated from the previous epoch to include causal relationships within the system, creating 64 feature vectors representing each epoch. Spectral domains were obtained by fast Fourier transform, and moving epochs were removed from the dataset. Finally, the features were normalized. Table 1 shows the various sleep stages in this dataset, which is divided into 70% for training and 30% for testing. [Table 1]

[0097] Classification accuracy was tested using several deep learning architectures, including CNNs, RNNs, and nonlinear machine learning algorithms such as Random Forests or Gradient Boosting Decision Trees (GBDTs). Using a weighted GBDT model (100 trees), 88% accuracy was achieved for all classes, but it suffered from a decrease in accuracy for less common events (not so much for N1 and N3; see Table 2). Nonlinear dimensionality reduction of UMAP in two virtual dimensions can be used to visualize the quality of predictions for different classes in Figure 9A and in Figure 9B. [Table 2]

[0098] Example 2 sleep research In this study, participants wear the neuromodulatory device disclosed herein during two consecutive nights of sleep at a clinic. The neuromodulatory device is powered on and individual file steering data is set. During the night, one optimal spatial peak pulse mean intensity ultrasound power is assessed. The neuromodulatory device emits continuous ultrasound for 200 milliseconds during non-REM sleep stages N2 and / or N3, phase-locked to the participant's slow wave. A minimum inter-stimulus delay is set to 5 seconds to allow assessment of slow wave power after stimulation. The following intensity is delivered randomly for each event: 5 W / cm with a 300-360° phase. 2 , 10W / cm 2 , 20W / cm 2 , and 40W / cm 2The delta power ratio at 4.7 seconds post-stimulation is evaluated in comparison to the average delta power of all events. The optimal power is investigated during the 7 days prior to the second night of testing. The second night of the study is used to investigate the optimal stimulus slow wave phase at a given power. The stimulus phases to be tested are 300-60°, 0-60°, 60-120°, 120-180°, 180-240°, and 240-300° relative to the down state or minimum voltage. Slow wave enhancement is determined by the relative slow wave amplitude following each stimulus parameter set and averaged across events. The phase with the minimum power dose and maximum enhancement is used to achieve slow wave enhancement that is statistically indistinguishable from higher power levels.

[0099] Example 3 Sleep research in patients with PTSD associated with sleep disorders In this study, participants diagnosed with PTSD will be presented with a 7-day randomized, double-blind, sham-controlled trial designed using an adaptive design. Participants will be randomly assigned in a 2:1 ratio to receive either treatment or sham stimulation. Participants will be asked not to change their sleep medication in the week prior to the study or during the study. In the sham intervention, sleep patterns will be 0 mW / cm². 2 A responsive neuromodulatory device configured to provide ultrasound intensity is fitted. The baseline visit includes two nights of sleep in a sleep laboratory for adaptation to define the participant's sleep architecture and for PSG recording. All participants are asked to wear the device for seven nights. The neuromodulatory device is configured to be responsive by a technician so that the physician cannot recognize the treatment group. Treatment is performed from 9 p.m. to 7 a.m., either with optimal spatial peak pulse mean intensity power and phase ultrasound stimulation as defined in Example 2, or with a sham intervention. Daily, from 9 a.m. to 5 p.m., participants are evaluated on a performance test including the impact of using the neuromodulatory device on sleep quality and well-being in PTSD. In addition, vital signs, physical examination, and sleep habits are recorded.

[0100] Example 4 Sleep research on individuals with sleep disorders In this study, participants will be randomized to receive either pre-sleep disturbance, post-sleep disturbance, or sham stimulation. Participants will be instructed to maintain their chronic sleep schedule, as reported during a screening over seven nights of a seven-day "at-home" sleep monitoring phase. They will be encouraged to get an optimal eight-hour night of sleep, entering between 9 p.m. and 8 a.m., seven days prior to the laboratory phase of the study. Participants' sleep-wake activity will be assessed using actigraphy to ensure compliance with this requirement. Participants will be required to refrain from napping and ingesting any study-prohibited substances during this period. Participants will arrive at the laboratory around 7 p.m. the day after the final night of the at-home sleep monitoring phase. Participants will spend 10 hours in bed with the lights off and leave the laboratory shortly after 7 a.m. Participants will be instructed not to nap during the day and will be monitored with actigraphy throughout this phase. Participants will be monitored with polysomnography every night. Participants will remain in the laboratory the night after the final sleep satiation phase, before the night of therapeutic sleep restriction. Baseline daytime performance assessments are conducted every four hours daily. These assessments include PVT, a mathematical test, and a sleep tendency test. Before bedtime on nights of sleep restriction, both the stimulation and sham groups wear neuromodulatory devices. By 11 p.m. (+ / - 10 minutes), participants turn off their lights and go to bed. The stimulation group receives the power identified in Example 2 using a neuromodulatory device operating as described in Example 3. Participants in the sham group sleep for approximately four hours without stimulation. Participants who experience a total of less than approximately 90 minutes of sleep during those four hours are excluded from further participation in the study. Additionally, participants who do not fall asleep within approximately 75 minutes after lights out (bedtime) are excluded from further participation in the study. If a subject experiences discomfort, stimulation is immediately terminated, and their participation in the study ends at that point; those individuals are allowed to sleep for the rest of the night and are released from the study the following morning. A 44-hour sleep deprivation period follows the four-hour sleep restriction, during which performance, mood, and sleep tendency are regularly assessed.

[0101] During the first sleep recovery night, participants will be asked to wear a neuromodulatory device throughout the night. The neuromodulatory device will deliver ultrasound to the post-sleep disorder group. All participants will have two nights of recovery sleep consisting of eight hours in bed from 11 p.m. to 7 a.m. Performance assessments will be conducted regularly daily following the recovery nights. During the recovery nights, sleep will be objectively monitored using actigraphy and polysomnography. Participants will be dismissed by the research medical investigator by 7 p.m. or earlier if all planned research procedures and medical clearances have been completed on the day following the second recovery night.

[0102] Finally, the foregoing description of embodiments of the present invention is presented for illustrative and explanatory purposes. While aspects of the present invention are emphasized by reference to specific embodiments, it should be understood that those skilled in the art will readily recognize that these described embodiments are merely illustrative of the principles constituting the present invention. Accordingly, specific embodiments are not intended to be exhaustive or to limit the present invention to the exact forms disclosed. Therefore, it should be understood that embodiments of the disclosed subject matter are not limited to specific elements, compounds, compositions, components, articles, apparatus, methodologies, uses, protocols, steps, and / or limitations described herein unless expressly stated so.

[0103] Furthermore, groupings of alternative embodiments, elements, steps, and / or limitations of the present invention should not be construed as limitations. Each such group may be referenced and claimed individually or in any combination with other groupings disclosed herein. For convenience and / or patentability reasons, it is anticipated that one or more alternative embodiments, elements, steps, and / or limitations of a grouping may be included in or removed from a grouping. When such inclusion or removal occurs, the specification shall be deemed to include the modified group and therefore all descriptions of the Markush groups used in the appended claims shall be satisfied.

[0104] Furthermore, those skilled in the art will recognize that, without departing from the spirit of the present invention, specific changes, modifications, rearrangements, alterations, additions, deletions, and partial combinations thereof can be made in accordance with the teachings of this specification. Further, the following appended claims and the claims introduced hereinafter are intended to be construed to include all such changes, modifications, rearrangements, alterations, additions, deletions, and partial combinations that are within their true spirit and scope. Accordingly, the scope of the present invention should not be limited to that precisely shown and described herein.

[0105] Specific embodiments of the present invention, including the best mode known to the inventors for carrying out the invention, are described herein. Of course, variations of these described embodiments will be apparent to those skilled in the art upon reading the foregoing description. The inventors expect those skilled in the art to appropriately employ such variations, and the inventors intend for the invention to be practiced in ways other than those specifically described herein. Accordingly, the present invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Further, any combination of all possible variations of the above-described embodiments is included in the present invention unless otherwise indicated herein or clearly contradicted by context.

[0106] The words, languages, and technical terms used in this specification are for the purpose of describing only specific embodiments, elements, steps, and / or limitations, and are not intended to limit the scope of the present invention, which is defined only by the claims. Further, such words, languages, and technical terms are to be understood not only in the sense of their generally defined meanings, but it is also to be understood that structures, materials, or acts that go beyond the scope of their generally defined meanings are included herein by special definition. Thus, where an element, step, or limitation can be understood as having multiple meanings in the context of this specification, its use in the claims must be understood to be general to all possible meanings supported by the specification and the word itself.

[0107] The definitions and meanings of elements, steps, or limitations described in the claims below are therefore defined herein to include not only combinations of elements, steps, or limitations described literally, but also all equivalent structures, materials, or actions that perform substantially the same function in substantially the same manner and obtain substantially the same results. In this sense, it is intended that an equivalent substitution of two or more elements, steps, or limitations may be made for any one of the elements, steps, or limitations of the claims below, or that a single element, step, or limitation may be replaced by two or more elements, steps, or limitations of such claims. Elements, steps, or limitations may be described above as functioning in a particular combination, and even if initially claimed as such, one or more elements, steps, or limitations from the claimed combination may, in some cases, be removed from the combination, and the claimed combination may cover subcombinations or variations of subcombinations. Therefore, despite the fact that elements, steps, and / or limitations of the claims are described below in specific combinations, it should be clearly understood that the present invention includes other combinations of fewer, more, or different elements, steps, and / or limitations, even if they were not initially claimed in such combinations as disclosed above. Furthermore, minor modifications from the claimed subject matter, whether now known or later devised, as seen by those skilled in the art, are clearly intended to be equivalently within the scope of the claims. Thus, obvious substitutions, whether now or in the future known to those skilled in the art, are defined as being within the scope of the defined elements. Accordingly, the claims should be understood to include those specifically illustrated and described above, conceptually equivalents, obviously replaceables, and also those essentially incorporating the essential ideas of the present invention.

[0108] Unless otherwise indicated, all numerical terms used herein and in the claims to represent properties, items, quantities, parameters, characteristics, terms, etc., should be understood in all cases to be modified by the term “approximately.” As used herein, the term “approximately” means that the thus limited property, item, quantity, parameter, characteristic, or term encompasses a range of plus or minus 10 percent of the stated value of the property, item, quantity, parameter, characteristic, or term. Thus, unless otherwise indicated, numerical parameters described in the specification and the appended claims are approximations and may vary. For example, they may vary slightly when determining the mass of a given sample as a mass spectrometer, and the term “approximately” in the context of ion mass or ion mass / charge ratio refers to + / - 0.50 atomic mass units. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the claims, each numerical indication should be interpreted by applying ordinary rounding techniques, at least in light of the reported number of significant digits.

[0109] Although the numerical ranges and values ​​describing the broad scope of this invention are approximations, the numerical ranges and values ​​described in specific examples are reported as accurately as possible. However, any numerical range or value inherently contains certain errors that inevitably arise from the standard deviation found in each test measurement. The enumeration of numerical ranges of values ​​herein is merely intended to serve as a simple way to individually refer to the individual numerical values ​​that fall within the range. Unless otherwise indicated herein, the individual values ​​of a numerical range are incorporated herein as if they were individually enumerated herein.

[0110] The use of the terms “may” or “may” with respect to an embodiment or aspect of an embodiment also carries the alternative meaning of “may not” or “cannot.” Therefore, where this specification discloses that an embodiment or aspect of an embodiment may or could be included as part of the subject matter of the invention, a negative limitation or exclusive proviso is also expressly implied, meaning that the embodiment or aspect of an embodiment may or may not be included as part of the subject matter of the invention. Similarly, the use of the term “optionally” with respect to an embodiment or aspect of an embodiment means that such an embodiment or aspect of an embodiment may or may not be included as part of the subject matter of the invention. Whether such a negative limitation or exclusive proviso applies depends on whether the negative limitation or exclusive proviso is stated in the claimed subject matter.

[0111] In the context of describing the present invention (particularly in the context of the following claims), the terms “a,” “an,” “the,” and similar references should be construed to cover both singular and plural forms unless otherwise indicated herein or unless the context clearly contradicts this. Furthermore, ordinal indicators such as “first,” “second,” “third,” etc., used for identified elements, are used to distinguish elements and do not indicate or imply any required or limited number of such elements, nor do they indicate any particular position or order of such elements unless specifically stated. All methods described herein can be performed in any suitable order unless otherwise indicated herein or unless the context clearly contradicts this. The use of any examples or illustrative language provided herein (e.g., “etc.”) is intended solely to better illustrate the present invention and does not limit the scope of the present invention as otherwise claimed. The language herein should not be construed to indicate unclaimed elements essential to the practice of the present invention.

[0112] When used in the claims, the open-ended transitional term “comprising,” its variations such as “comprise” and “comprises,” and their equivalent open-ended transitional phrases such as “including,” “containing,” and “having,” whether added at the time of filing or by amendment, encompass all explicitly enumerated elements, restrictions, steps, integers, and / or features, either alone or in combination with unenumerated subject matter; named elements, restrictions, steps, integers, and / or features are mandatory, but other unnamed elements, restrictions, steps, integers, and / or features may be added to form the composition of the claims. Certain embodiments disclosed herein may be further limited in the claims by using closed-ended transitional clauses such as "consisting of" or "consisting essentially of" (or variations such as "consist of," "consists of," "consist essentially of," and "consists essentially of") instead of, or as a modification of, "comprising of." When used in the claims, whether added at the time of filing or by amendment, the closed-ended transitional clause "consisting of" excludes elements, limitations, steps, integers, or features not expressly described in the claims. The closed-ended transitional clause "consisting essentially of" limits the claims to the expressly enumerated elements, limitations, steps, integers, and / or features and other elements, limitations, steps, integers, and / or features that do not substantially affect the basic and novel characteristics of the claimed subject matter.Therefore, the meaning of the unrestricted transitional clause “comprising” is defined as encompassing all elements, limitations, steps, and / or features specifically enumerated, as well as any optional additional unspecified ones. The meaning of the restricted transitional clause “consisting of” is defined as encompassing only the elements, limitations, steps, integers, and / or features specifically described in the claims, while the meaning of the restricted transitional clause “consisting essentially of” is defined as encompassing only the elements, limitations, steps, integers, and / or features specifically described in the claims, and elements, limitations, steps, integers, and / or features that do not substantially affect the basic and novel features of the claimed subject matter. Thus, the unrestricted transitional clause “comprising” (and its equivalent unrestricted transitional clauses) includes, in the limited case, the claimed subject matter specified by the restricted transitional clauses “consisting of” or “consisting essentially of.” Therefore, embodiments described herein or claimed in the phrase "comprising" explicitly and clearly provide an explanation, activation, and support for the phrases "consisting essentially of" and "consist of."

[0113] Finally, all patents, patent publications, and other references cited and identified herein are incorporated herein individually and expressly by reference in whole for the purpose of describing and disclosing, for example, compositions and methodologies described in such publications that may be used in connection with the present invention. These publications are provided solely for disclosure prior to the filing date of this application. In this regard, it is not, and should not be, construed as the inventor acknowledging, or being entitled to, prior art or otherwise prior to such disclosures. All statements regarding dates or expressions regarding the contents of these documents are based on information available to the applicant and do not constitute an endorsement of the accuracy of the dates or contents of these documents.

Claims

1. A wearable device housing, one or more electroencephalogram (EEG) electrodes for real-time analysis of brain function, one or more EEG signal amplifiers coupled to one or more EEG electrodes, and one or more ultrasonic transducer arrays, each of which comprises one or more ultrasonic transducer arrays including one or more ultrasonic emitting elements, comprising a neuromodulation device including one or more ultrasonic transducer arrays, A stimulation control computing environment including a stimulation control unit and an offline computing device, wherein the stimulation control unit includes at least one processor coupled to one or more ultrasonic transducer arrays and configured with one or more data processing functions to deliver ultrasonic radiation to one or more brain regions including at least a portion of the thalamus, and A neuromodulation system including one or more data processing functions, Using brain imaging data, identify one or more parameters that represent one or more brain regions. By using ultrasound-generated data to determine beam steering parameters, one or more target regions in one or more brain regions can be identified. Position one or more ultrasonic emission elements for one or more target regions. An offline algorithmic mapping element configured to perform one or more acoustic simulations of ultrasonic radiation for at least a subset of one or more ultrasonic radiation elements to determine the phase offset of the subset of one or more ultrasonic radiation elements, thereby focusing the ultrasonic radiation onto one or more target regions, Using phase offset information generated by offline algorithm mapping elements, ultrasonic radiation is dynamically administered to one or more target regions for a specified period. To detect the phase of slow-wave spectral components, real-time data acquired by one or more electroencephalogram electrodes is processed. An online algorithmic stimulation application element configured to deliver ultrasonic radiation to one or more target regions within a specific slow-wave phase range, and A neuromodulatory system that includes this.

2. The neuromodulation system according to claim 1, wherein one or more electroencephalogram electrodes detect and measure alpha waves, theta waves, delta waves, sleep spindles, K complex waves, or any combination thereof.

3. The neuromodulation system according to claim 1 or 2, wherein one or more electroencephalogram electrodes have a sensitivity to detect and measure at least 0.1 Hz.

4. The neuromodulation system according to any one of claims 1 to 3, wherein each of the one or more ultrasonic emitting elements of one or more ultrasonic transducer arrays comprises at least 64 ultrasonic emitting elements.

5. A neuromodulation system according to any one of claims 1 to 4, wherein one or more ultrasonic transducer arrays generate ultrasonic frequencies between 500 kHz and 1 MHz from ultrasonic emitting elements.

6. The neuromodulation system according to any one of claims 1 to 5, wherein real-time information processed by a stimulus control unit includes an electroencephalogram power spectral distribution and an electroencephalogram spectral amplitude for identifying sleep stages.

7. The neuromodulation system according to any one of claims 1 to 6, wherein a stimulation control unit adjusts the application of ultrasound by one or more ultrasound transducer arrays based on a constant input from one or more electroencephalogram electrodes.

8. A neuromodulation system according to any one of claims 1 to 7, wherein ultrasonic stimulation targeting by a stimulation control unit includes determining acoustic impedance and determining beam steering parameters using ultrasonic generation data.

9. The neuromodulation system according to claim 8, wherein the determination of beam steering parameters optimizes the power distribution ratio between a point and one or more target regions and off-target regions over different steering angles of the ultrasonic emission element.

10. The neuromodulation system according to claim 8, wherein the determination of beam steering parameters is performed by using a modeling simulation of the maximum lateral steering angle, which is obtained by estimating the maximum angle from each of one or more ultrasonic transducer arrays to the maximum and minimum lateral steering angles of one or more specific regions of the brain.

11. The neuromodulation system according to any one of claims 1 to 10, wherein a stimulation control unit delivers ultrasonic radiation during one or more specific sleep stages.

12. The neuromodulation system according to claim 11, wherein one or more specific sleep stages include non-REM sleep stage N2, non-REM sleep stage N3, or both non-REM sleep stage N2 and non-REM sleep stage N3.

13. The neuromodulation system according to claim 11 or 12, wherein the stimulus control unit classifies one or more specific sleep stages using a gradient boost decision tree machine learning algorithm.

14. The neuromodulation system according to any one of claims 1 to 13, wherein the stimulus control unit further includes a deep learning model for predicting sleep stages and a deep learning model for adjusting ultrasonic radiation.

15. The neuromodulation system according to any one of claims 1 to 14, wherein a stimulation control unit optimizes the spatial, temporal, and / or intensity of ultrasonic radiation based on the current slow wave amplitude reading relative to a baseline slow wave amplitude reading.

16. A method for operating a neuromodulation system according to any one of claims 1 to 15, The process involves a transmitting device provided in a stimulus control unit transmitting an operation signal to a nerve modulation device, A process in which a receiving device provided on a nerve modulation device receives an operation signal from a stimulus control unit, A process in which one or more ultrasonic emission elements of a neuromodulatory device are activated based on a received control signal, Having, method.

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