49 results about "Stimulus frequency" patented technology

The invention provides a transcranial stimulation magnetic therapy closed-loop control method and system, and the method comprises the steps: determining a multi-modal physiological signal of a user, the multi-modal physiological signal at least comprising an electroencephalogram signal, an autonomic nerve physiological signal and a subjective feedback signal; constructing an individual dynamic baseline spectrum based on user multi-state data collected before treatment; the method comprises the following steps: preprocessing a multi-modal physiological signal collected in a treatment process, and extracting a real-time feature vector; calculating a comprehensive deviation index of the real-time feature vector and the individual dynamic baseline spectrum, and determining a deviation type based on the comprehensive deviation index; the adjustment amount of transcranial magnetic stimulation parameters is dynamically calculated according to the deviation type, the individual response characteristics and the historical treatment data, stimulation operation is conducted according to the adjustment amount of the transcranial magnetic stimulation parameters, and the transcranial magnetic stimulation parameters at least comprise the stimulation frequency, the intensity, the pulse width and the stimulation duration. Parameters of transcranial magnetic stimulation are adaptively changed according to changes of neuron oscillation of a patient in the treatment process.

Methods and systems for spinal cord stimulation (SCS) are disclosed. The methods and systems involve the use of electrode leads implanted within the spine of a patient to record nerve responses induced by a stimulus. The disclosed neural responses differ in various aspects from the electrical responses previously measured in the SCS context, such as stimulation artifacts and evoked compound action potentials (ECAPs). The disclosed neural responses typically occur at a later time after inducing the stimulation pulse. Another significant feature is that when the frequency of evoked stimulation is ultra-low, such as about 10 Hz or less, the disclosed neural response is most significantly observed at a consistent, relatively constant amplitude as a whole. The disclosed methods and systems may use these neural responses as indications of pain, treatment, and / or another clinically relevant dimension to guide / confirm stimulation placement, as well as feedback control for stimulation parameters.

The invention belongs to the technical field of nerve regulation and control engineering, and provides a cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system, which is characterized in that first frequency band range data and second frequency band range data in brain wave signals are coupled to obtain cerebrospinal fluid rhythm data; then, calculating the phase difference between the first frequency band range data and the cerebrospinal fluid rhythm data; finally, the stimulation frequency is dynamically controlled according to the phase difference, and the stimulation current is dynamically controlled according to the brain tissue water volume fraction. The problem that specific metabolism requirements of non-fast eye movement sleep and fast eye movement sleep cycles cannot be matched by adopting single-frequency-band stimulation is solved, dynamic changes of post-traumatic encephaledema and requirements and influences of cerebrospinal fluid rhythm on current control are considered, the real-time adaptive capacity to the dynamic changes of the post-traumatic encephaledema is improved, and the application prospect is wide. And the treatment effect of the Alzheimer's disease is ensured.

The invention relates to the technical field of crossing of medical instruments and artificial intelligence, and provides a hearing threshold detection method based on multi-mode hearing electrophysiological signal fusion. Comprising stimulation frequency otoacoustic emission and auditory brainstem response induced by sweep frequency stimulation sound, distortion product otoacoustic emission induced by dual-tone stimulation and middle ear sound immittance / reflection signals, and a multi-task deep learning model is constructed to perform fusion analysis on the signals. According to the method, accurate quantitative regression prediction of hearing thresholds with any frequency resolution and automatic classification and discrimination of hearing loss properties are synchronously realized, the problems of single evaluation dimension and insufficient complex hearing loss analysis ability in the prior art are effectively solved, the comprehensiveness, accuracy and clinical operation efficiency of objective hearing evaluation are remarkably improved, and the method is suitable for popularization and application. And an innovative system solution is provided for fine diagnosis of the auditory function.

The invention relates to a hearing threshold prediction system and method based on a sweep frequency SFOAE fine structure, and the system comprises a collection and transmission module which is used for transmitting a stimulation signal and collecting an ear canal signal. The hearing threshold value analysis and prediction mechanism comprises a hearing threshold value detection module used for detecting sweep frequency SFOAE data under each stimulation intensity based on the input stimulation intensity of a set range, constructing an SFOAE signal fine structure, extracting the SFOAE signal fine structure formed by the SFOAE signals of all the stimulation intensities under each stimulation intensity, and outputting the SFOAE signal fine structure; and calling the pre-trained network model to predict the hearing threshold. And the hearing screening module is used for detecting sweep frequency SFOAE data under the selected stimulation intensity based on the adaptively selected stimulation intensity, constructing an SFOAE signal fine structure, extracting the SFOAE signal fine structure formed by all the SFOAE signals under the selected stimulation frequency, and calling a pre-trained network model to carry out hearing screening.

The application provides a SSVEP-based brain-computer interface stimulation modulation method, system, device and medium, comprising the following steps: acquiring a preset code sequence set, a stimulation frequency set and a stimulation paradigm; the code sequence set comprises a plurality of code sequences; the code sequence comprises a plurality of code elements, and the code element corresponds to a brightness amplitude depth coefficient; the stimulation frequency set comprises a plurality of stimulation frequencies; the stimulation paradigm is used for modulating the brightness amplitude depth and the flicker frequency of visual stimulation based on the code sequence and the stimulation frequency; based on the code sequence and the stimulation frequency, a code stimulation combination of the code sequence and the stimulation frequency is generated; and based on the code stimulation combination, the visual stimulation corresponding to each stimulation block is modulated by using the stimulation paradigm. The SSVEP-based brain-computer interface stimulation modulation method, system, device and medium provided by the application not only expand the number of stimulation targets, but also improve the comfort of users when gazing at the visual stimulation.

A method of determining brain stimulation parameters includes applying Low Frequency Stimulation (LFS) to a contact of a multi-contact electrode implanted in a target structure in an individual's brain. Evoked Compound Activity (ECA) evoked in the target structure by the LFS is measured. A range of frequencies for delivering brain stimulation within a predetermined range based on a phase space extracted from the ECA is determined. Stimulation frequencies are applied to the contact of the multi-contact electrode within the determined range of frequencies. High Frequency Oscillations (HFO) evoked in the target structure by the applied stimulation frequencies within the determined range are measured. A frequency evoking HFO above a predetermined threshold is determined. The determined frequency is selected as a treatment frequency for the target structure.

The invention discloses a double-domain double-scale cross-subject SSVEP (Steady-State Visual Evoked Potential) decoding method and device based on transfer learning, and belongs to the technical field of brain-computer interfaces. The method comprises the following steps: acquiring SSVEP electroencephalogram data of a source domain and SSVEP electroencephalogram data to be decoded of a target domain; sub-band filtering and sine and cosine reference matrix template generation; aiming at source domain data of each sub-band in the source domain multi-sub-band data, constructing a source domain dual-scale template pair of each sub-band for each candidate stimulation frequency, and extracting a single-cycle cross-tested spatial filter; applying time local constraint to target domain data of each sub-band, and extracting an adaptive spatial filter; and calculating multi-path evidence scores, carrying out weighted fusion based on the sub-band weights, and judging and outputting a decoding result. Target subject training data is not needed, high-precision and strong-robustness cross-subject decoding is achieved through double-domain double-scale feature fusion and self-adaptive adaptation, the calculation amount is low, real-time operation is supported, and the practicability and generalization ability of a brain-computer interface are remarkably improved.

The application discloses a kind of individualized electric stimulation signal generation system and modulation gamma oscillation's space-frequency domain adaptive otDCS intervention method, including the EEG signal data of individual, the gamma-ASSR data collected are re-referenced, filtering, artifact removal, intercept data section, extract the resonance frequency of individual gamma oscillation as otDCS's stimulation frequency using spectrum analysis, extract the instantaneous phase of individual gamma oscillation as otDCS's stimulation phase using phase estimation, and extract the key node in the brain network of individual gamma oscillation as otDCS's stimulation site using network analysis method;And according to the resonance frequency, instantaneous phase and key node of individual gamma oscillation obtained by individualized stimulation parameter extraction module, lock stimulation frequency, stimulation phase and stimulation site, generate otDCS waveform, for subsequent individual electric stimulation output.

A diagnostic system is disclosed that is configured to direct an acoustic stimulation generator to apply acoustic stimulation having a stimulus frequency to a recipient of a cochlear implant during an insertion procedure in which an electrode lead communicatively coupled to the cochlear implant is inserted into a cochlea of the recipient, direct the cochlear implant to use an electrode disposed on the electrode lead to record an evoked response signal during the insertion procedure, the evoked response signal representing amplitudes of a plurality of evoked responses that occur within the recipient in response to the acoustic stimulation applied to the recipient, and incrementally step, as the electrode lead is inserted into the values starting with an initial value and ending with a final value lower than the initial value.

The invention discloses a safety helmet intelligent control method and system based on electroencephalogram characteristics, and the method comprises the steps: decomposing a real-time electroencephalogram signal frequency spectrum to generate cognitive load frequency domain characteristics, and building an electroencephalogram control space; an electroencephalogram control space is scanned to identify a frequency band coupling unbalance point, a cooperative intervention opportunity is reversely derived, and a multi-channel adjustment mode is formed; mapping the stimulation frequency range to generate a frequency matching domain, and performing resonance analysis to determine a resonance intervention window; monitoring a frequency band in a resonance window to obtain an energy migration track, constructing a risk propagation chain to identify a cascade failure node and forming a key blocking anchor point; analyzing risk acceleration of the key blocking anchor points to form a state deterioration rate, fusing frequency domain features to generate dynamic delay tolerance, and generating variable speed control parameters in a fast-slow grading manner; the variable speed control parameter is compared with a threshold value to generate a control deviation signal, reverse phase modulation is carried out to generate a hedging stimulation waveform, a phase compensation execution instruction is formed, and accurate monitoring and safe intervention of the cognitive state are achieved.

An exemplary system comprises memory that stores instructions and a processor communicatively coupled to the memory and configured to execute the instructions to perform a process. The process may comprise obtaining a pre-operative audiogram of a recipient, the pre-operative audiogram generated prior to a lead insertion procedure during which an electrode lead having a plurality of electrodes is inserted into a cochlea of the recipient, obtaining one or more attributes associated with the lead insertion procedure, and determining, based on the pre-operative audiogram and the one or more attributes, one or more stimulation frequencies for stimulation that is to be used during the lead insertion procedure to elicit evoked responses within the recipient, the evoked responses usable to monitor an insertion condition associated with the electrode lead.

The invention belongs to the field of auxiliary medical treatment, and provides a personalized electrical stimulation treatment scheme generation system and method, and the method comprises the steps: obtaining a voice signal of a patient, and extracting voice dynamic change information; acquiring neuromyoelectric signals, heart rate signals and motion information of the patient, and extracting physiological change information; performing association modeling on the voice change information and the physiological change information to generate fusion features for representing a multi-part tremor mode of the patient; performing dynamic generation of personalized electrical stimulation parameters based on the fused features; constraining the dynamic change of the current amplitude, the stimulation frequency and the pulse width within an individually set safety threshold range, and performing protective adjustment in combination with real-time feedback of a patient; and the electrical stimulation module is used for implementing electrical stimulation on a target nerve part in the radial nerve, the median nerve or the ulnar nerve according to the parameters issued by the decision module.

This invention discloses a device for monitoring nerve conduction function, comprising: a mounting plate, a fastening assembly, and a monitoring assembly, both of which are fixed to the surface of the mounting plate; the fastening assembly includes a connecting strap and a connecting block fixed to the side of the mounting plate, with a matching locking block fixed to one end of the connecting strap; the monitoring assembly includes an ultrasonic detection module, a detection electrode, a stimulation electrode, and a processing module, with a plurality of detection electrodes and stimulation electrodes of equal quantity. This device and method for monitoring nerve conduction function, by classifying the monitored nerves into different degrees of damage, allows the stimulation electrodes to provide precise stimulation treatment, avoiding further nerve damage due to stimulation time, intensity, and frequency exceeding tolerance limits. Simultaneously, the electrical stimulation emitted by the stimulation electrodes can promote nerve cell regeneration and growth, accelerating the repair speed of damaged nerves.

The invention discloses a space-frequency fusion visual stimulation method and system. The method comprises the following steps: S1, selecting a central stimulation target and four adjacent stimulation targets around the central stimulation target; s2, setting an arrangement mode of the stimulation blocks; s3, the two arrangement modes are stimulated in sequence, and the stimulation frequencies of the five stimulation blocks and the corresponding electroencephalogram signal amplitudes are obtained; s4, selecting an arrangement mode in which the number of the stimulation frequencies of the central stimulation target including the stimulation frequencies of the surrounding four adjacent stimulation targets is maximum and the amplitude of the electroencephalogram signals induced by the stimulation frequencies is maximum as an optimal arrangement mode; compared with the prior art, the characteristic of partition backlight space crosstalk is converted into the multi-frequency characteristic advantage, a traditional single signal loudness analysis mode is abandoned, surrounding target interference is converted into the multi-frequency and space characteristics of the center target, and the target recognition accuracy in the multi-target scene is greatly improved.

The application provides a neural internal medicine rehabilitation training system and regulation method based on artificial intelligence, extracts the rhythm power of a stimulation pulse of a rehabilitation training device to a patient and the blood oxygen concentration of a target nerve point of the patient, classifies the efficiency of the remodeling level of the neural function in the rehabilitation training through the blood oxygen concentration and the rhythm power, and obtains a graded remodeling index of the neural function regulation in the rehabilitation training; determines the cortex excitability index of the patient in the neural internal medicine rehabilitation training through the motor evoked potential of the target patient muscle cortex, the stimulation frequency and the stimulation intensity of the stimulation pulse in the rehabilitation training device; multi-modal synergistically integrates the cortex excitability index and the graded remodeling index of the neural function regulation, obtains the neural regulation target parameter of the remodeling index in the rehabilitation training device, and then dynamically regulates the stimulation parameter in the rehabilitation training device based on the neural regulation target parameter. Based on the above scheme, multi-modal coupling regulation of the stimulation parameter in the rehabilitation training process can be realized.

The present invention discloses a high-frequency visual evoked potential brain-computer interface encoding and decoding method, device and robotic arm control system. The high-frequency visual evoked potential brain-computer interface encoding method comprises: displaying a plurality of instruction blocks distributed in rows and columns on a visual stimulation interface, each instruction block being jointly encoded by two frequency-phase combination pairs, and the row and column positions of each instruction block corresponding to the two frequency-phase combination pairs of the instruction block; wherein the frequency value in the frequency-phase combination pair is greater than or equal to 30Hz. When a subject gazes at an instruction block, the brain induces visual evoked potentials corresponding to the rows and columns of the instruction block, respectively, so that the row number and column number of the instruction block can be decoded instead of decoding the visual evoked potential itself to be identified, thereby solving the problem of high training cost caused by low signal-to-noise ratio of high-frequency signals in the prior art, thereby consuming lower training costs while adopting high-frequency visual stimulation frequency, achieving the effect of improving the subject's eye comfort.

The present application relates to the technical field of electric needle nerve electric stimulation, in particular to a low-stimulation electric needle nerve electric stimulation parameter optimization method and system, which comprises the following steps: obtaining first data; inserting an electric needle into an acupuncture point of a stimulation object, setting the actual stimulation frequency of the electric needle as a reference stimulation frequency, the actual pulse width as a reference pulse width, and the actual current intensity as a reference current intensity; reading a reference blood pressure value, a reference heart rate value, and a reference electromyogram amplitude value; setting the actual stimulation frequency, the actual pulse width, and the actual current intensity of the electric needle dynamically at a preset adjustment interval time, obtaining a blood pressure value three-dimensional function, a heart rate value three-dimensional function, and an electromyogram amplitude value three-dimensional function; and calculating electric needle nerve electric stimulation optimization parameters through a parameter optimization algorithm. The present application calculates electric needle nerve electric stimulation optimization parameters through a parameter optimization algorithm, thereby ensuring that a stimulation object maintains optimal physiological indicators during nerve electric stimulation.

The application discloses a bilateral transaural vagus nerve regulation device, an electronic device and a storage medium. The nerve regulation device comprises an auditory stimulation module configured to apply multiple novel stimuli to a target object within an auditory stimulation period; a processing module configured to obtain an electroencephalogram signal corresponding to each electroencephalogram channel when each novel stimulus is applied to the target object; determine a target frequency when fitting the electroencephalogram signal with a target lag order based on the electroencephalogram signal corresponding to each electroencephalogram channel; determine a first proportion based on the information exchange intensity between different brain regions of the target object at each time point and the target frequency; determine a second stimulation frequency within a next electrical stimulation period adjacent to the auditory stimulation period based on the first proportion, the first stimulation frequency and the target frequency; and an electrical stimulation module configured to electrically stimulate the auricular vagus nerve of the binaural of the target object at the second stimulation frequency within the next electrical stimulation period.

To determine the presence or absence of induction of brain waves with relatively high accuracy when a determination result of the presence or absence of induction of brain waves by stimulation is used for selection of a selection target.SOLUTION: A frequency determination apparatus includes induction determination means for determining whether or not a brain wave is induced by a stimulus in a brain wave measured in a state in which the stimulus of a designated frequency is output, frequency range search means for searching for a maximum value and a minimum value of a frequency at which the induction occurs, and frequency determination means for determining a plurality of frequencies as frequencies of the stimulus within a range between the maximum value and the minimum value.SELECTED DRAWING: Figure 1

The application discloses a deep learning-based electroencephalogram slow wave real-time feedback transcranial electrical stimulation method and system, relates to the cross technical field of neural regulation and deep learning, and comprises the following steps: collecting electroencephalogram signals of a target object and pre-processing the electroencephalogram signals to obtain pre-processed electroencephalogram signals; inputting the pre-processed electroencephalogram signals into a pre-constructed deep learning model for analysis to obtain a latent representation representing the dynamic state of electroencephalogram slow waves; wherein the deep learning model is obtained through the collaborative training of three types of self-supervised targets, namely self-predictive representation, unsupervised target conditional reinforcement learning and inverse dynamics modeling; transcranial electrical stimulation parameters are decided based on the latent representation, wherein the stimulation parameters at least include a stimulation type, a stimulation intensity and a stimulation frequency; and corresponding transcranial direct current stimulation or transcranial alternating current stimulation is output to specific brain regions of the target object according to the transcranial electrical stimulation parameters.

The application discloses a kind of nuclear magnetic compatible tactile stimulation devices, comprising: drive control system and multichannel tactile stimulation system, the drive control system with the multichannel tactile stimulation system electrically connected;The drive control system is used to control the reciprocating tactile stimulation module of corresponding position on the multichannel tactile stimulation system generates tactile stimulation output, adjusts the frequency of output, output displacement and output force size, control stimulation time length;The multichannel tactile stimulation system is used to apply contact mechanics tactile stimulation to the hand of subject, tactile stimulation area covers whole hand, stimulates position, stimulation output displacement, force and regulates and controls stimulation duration.The present application tactile stimulation has high time and space resolution, output displacement, stimulation frequency is stable and adjustable, tactile coverage area is wide, and it is beneficial to tactile brain topological function diagram research.

This invention provides a method for enhancing attention and executive function based on the synergy of transcranial alternating current stimulation (TCD) and white noise. The method includes: acquiring clinical and functional phenotypic vectors, behavioral vectors of attention and executive function, and neural oscillation and brain network vectors to obtain a comprehensive representation; defining target cognitive function, target brain region, target stimulation frequency, and stimulation parameters to obtain an individualized target brain network and oscillation frequency band scheme; designing a TCD stimulation scheme, a cognitive task training scheme, a white noise scheme, and an overall timetable to obtain a synergistic intervention scheme; recording behavioral process data, neural process data, and subjective experience data of the research subjects to obtain multimodal feedback data; and analyzing the multimodal feedback data to obtain an evaluation result of the intervention effect. This invention achieves synergistic intervention of individualized brain networks, frequency bands, tasks, noise, and time, which can not only address the symptoms and self-care abilities of individual patients but also promote the development of medicine and neuroscience.

The application discloses an individualized cognitive function adjusting device based on cross-frequency coupling, which comprises a peak frequency automatic extraction module, a spectrum analysis and automatic frequency extraction are used to obtain the peak frequency of the frontal lobe theta oscillation and the parietal lobe gamma oscillation of an individual baseline as the stimulation frequency of transcranial alternating current stimulation; a target site determination module is used to automatically output the stimulation site of tACS by determining the positive and negative power values corresponding to the peak frequency between the left and right brain regions, and the stimulation equipment outputs current to the corresponding brain region according to the output stimulation site; a signal-to-noise ratio calculation module is used to adaptively output the stimulation current intensity of tACS according to the signal-to-noise ratio value at the peak frequency; and a multi-mode regulation model is used to set the peak nesting and valley nesting modes according to the cross-frequency tACS, so that the targeted regulation of cognitive function disorders such as memory, attention and emotion processing is realized.

According to the neurology rehabilitation training system based on artificial intelligence and the regulation and control method, the rhythm power of stimulation pulses of rehabilitation training equipment on a patient and the blood oxygen concentration of a target nerve target of the patient are extracted, and the remodeling level of the neural function in rehabilitation training is subjected to efficiency grading through the blood oxygen concentration and the rhythm power; obtaining graded remodeling indexes of nerve function regulation in rehabilitation training; determining the cortex excitability index of the patient in neurology rehabilitation training through the motor evoked potential of the muscle cortex of the target patient and the stimulation frequency and the stimulation intensity of the stimulation pulse in the rehabilitation training equipment; the method comprises the following steps: carrying out multi-modal collaborative integration on a cortex excitability index and a neural function regulation hierarchical remodeling index to obtain a neural regulation target parameter of a remodeling index in rehabilitation training equipment, and then carrying out dynamic regulation on a stimulation parameter in the rehabilitation training equipment based on the neural regulation target parameter. Based on the scheme, multi-mode coupling regulation and control of stimulation parameters in the rehabilitation training process can be achieved.

This invention relates to the field of cognitive data processing technology, and more particularly to a method and system for physical stimulation-driven lymphatic circulation and cognitive enhancement. The system includes a physical stimulation frequency impact assessment module, a stimulation mechanical vibration analysis module, a lymphatic circulation vibration calibration module, and a stimulation frequency drive adjustment module. It utilizes pressure and temperature sensors to monitor the corresponding physical stimulation pressure and the surface temperature of the stimulated skin in real time, performing stimulation frequency impact assessment and stimulation mechanical vibration analysis to generate a physical stimulation mechanical vibration effect. It also acquires lymphatic fluid flow velocity and EEG neural activity signals, performing neurocognitive level analysis and vibration response calibration. Simultaneously, it performs lymphatic circulation cognitive regression analysis and stimulation frequency drive adjustment to generate a physical stimulation frequency drive adjustment scheme corresponding to lymphatic circulation cognitive enhancement. This invention can accurately analyze the correlation between physical stimulation and lymphatic circulation and cognitive enhancement.

The application belongs to the technical field of nerve regulation and control engineering, and proposes an Alzheimer's disease electric stimulation system based on cerebrospinal fluid rhythm. First, the first frequency range data and the second frequency range data in the brain wave signal are coupled to obtain cerebrospinal fluid rhythm data. Then, the phase difference between the first frequency range data and the cerebrospinal fluid rhythm data is calculated. Finally, the stimulation frequency is dynamically controlled according to the phase difference, and the stimulation current is dynamically controlled according to the brain tissue water volume fraction. The problem that single frequency stimulation cannot match the specific metabolic needs of non-rapid eye movement sleep and rapid eye movement sleep cycles is solved. The dynamic changes of post-traumatic brain edema, as well as the demand and influence of cerebrospinal fluid rhythm on current control, are considered, the real-time adaptive ability to the dynamic changes of post-traumatic brain edema is improved, and the treatment effect of Alzheimer's disease is guaranteed.

This invention discloses a method for decoding EEG features for steady-state visual evoked potentials, including: the subject's fixation frequency being f n The original steady-state visual evoked potentials collected during visual stimulation are X. n Using N fb A filter bank for X n Preprocessing is performed to obtain N fb After preprocessing, the steady-state visual evoked potential pairs are filtered to obtain useful signal components and noise signal components, and then a spatial filter is obtained to obtain the steady-state visual evoked potential template signal. This is used to acquire the unknown steady-state visual evoked potential K and obtain N. fb K is a preprocessed unknown steady-state visual evoked potential. (m) By improving and K (m) The signal-to-noise ratio, calculate N fb The filtered sum K (m) The correlation coefficient of N fb The weighted summation yields ρ n ; We obtained K and steady-state visual evoked potentials [X1,…X i ,…,X n ,...,X Nf The correlation coefficient between ], ρ i If the maximum value is reached, then the visual stimulus frequency of K is f. i The technical solution in this embodiment achieves the effect of improving the accuracy of recognizing unknown visual stimuli using a small amount of training data.

Computing systems and computer-implemented methods for removing brain stimulation artifacts in neural signals are disclosed. The method makes use of a matching pursuit algorithm to accurately extract the stimulation artifact. The disclosed method removes the stimulation artifact associated with individual stimulation pulses without needing additional information from previous stimulation pulses or other electrodes. The disclosed method is compatible for use with various stimulation frequencies, does not need any filtering, and can recover neural signals almost immediately after stimulation. The disclosed method is compatible for use in has great potential in closed-loop systems used in various neurological diagnostic and therapeutic procedures.

Methods and systems for spinal cord stimulation (SCS) are disclosed. The methods and systems involve using electrode leads implanted within the patient's spinal column to record neural responses evoked by the stimulation. The disclosed neural responses are different in several respects from electrical responses that have previously been measured in the context of SCS, such as stimulation artifacts and evoked compound action potentials (ECAPs). The disclosed neural responses typically occur later in time following the evoking stimulation pulse. Another distinguishing feature is that disclosed neural responses are generally most prominently observed with consistent, relatively unchanging amplitudes when the evoking stimulation frequency is ultra-low, for example, about 10 Hz or less. The disclosed methods and systems may use these neural responses as indications of pain, therapy, and / or another clinically relevant dimension, to direct / confirm stimulation placement, and for feedback control of stimulation parameters.