391 results about "Neural activity" patented technology

The invention discloses a rehabilitation training evaluation system for dealing with schizophrenia patients, and relates to the technical field of data processing, the evaluation system comprises a multi-modal sensing module, an edge intelligent processing module and a dynamic graph network evaluation module; according to the technical key points, neurophysiology, behavior tracks, cognitive functions and environmental parameters are fused to form a'microscopic neural activity-mesoscopic behavior performance-macroscopic environment interaction 'full-dimension evaluation network, for example, the recessive decoupling phenomenon of'reduced brain oxygen metabolism but normal autonomic nerve function' of a negative symptom patient can be synchronously captured, and the accuracy of the evaluation network is improved. The method comprises the following steps of: firstly, quantifying the coordination and causality of data of different dimensions through a dynamic graph network, disclosing a dynamic association path of insufficient activation of a forehead cortex, social attention distraction and cognitive task error rate increase, and providing a visual basis for mechanism research and intervention target selection.

Disclosed herein are systems, devices, and methods for implanting electrodes within a cerebral vessel of a subject. For example, disclosed are various types of implantable electrode arrays, stents carrying electrode arrays, delivery devices for implantable electrode arrays, electrical interfaces for an implantable electrode array, connector lead cable assemblies for an implantable electrode array, and methods of delivering and deploying implantable electrode arrays.

The invention discloses an early judgment system for cognitive impairment of Parkinson's disease based on transcranial magnetic-electroencephalogram multi-dimensional features. The early judgment system comprises a transcranial magnetic-electroencephalogram combined acquisition module, a synchronous signal preprocessing module, a multi-dimensional feature extraction module, a feature intelligent fusion module and a storage and display module, the transcranial magnetic-electroencephalogram combined acquisition module is used for disturbing the neural activity of a patient through monopulse transcranial magnetic stimulation coding and acquiring an evoked state electroencephalogram signal; the synchronizing signal preprocessing module is used for denoising and filtering the evoked state electroencephalogram signals; a multi-dimensional feature extraction module performs feature extraction on the processed evoked state electroencephalogram signals by utilizing multi-dimensional electroencephalogram feature analysis to obtain multi-parameter electroencephalogram neural activity features; the feature intelligent fusion module is used for carrying out weight distribution and feature fusion on the multi-parameter electroencephalogram neural activity features to obtain cognitive disorder multi-modal neural indexes; and the storage and display module is used for storing the cognitive disorder multi-modal nerve indexes and visualizing the cognitive disorder multi-modal nerve indexes.

The invention discloses a Parkinson's dyskinesia individualized SCAN network positioning method based on a multi-modal image and deep learning. The method comprises the steps of obtaining multi-modal medical image data, preprocessing the multi-modal medical image data, obtaining a multi-modal structure image and functional connection data, and calculating a spontaneous neural activity index of a whole-brain voxel level; taking a priori brain region related to the spontaneous neural activity index and dyskinesia as a seed point, constructing a seed point voxel function connection graph representing individual brain function connection, and performing nonlinear feature fusion and extraction through the deep learning network model; the bilinear attention network is adopted to capture the interaction information of the feature data and the individual dyskinesia symptom which is significantly related, an individualized SCAN network positioning result is obtained, the structure-function coupling characteristics of the individual brain are comprehensively described, the cross-modal pathological features related to the dyskinesia can be more sensitively recognized, and the accuracy and accuracy of the diagnosis and treatment of the dyskinesia can be improved. And the accuracy and robustness of abnormal brain region detection are obviously improved.

The invention discloses a brain deep nerve stimulation method and system based on adaptive adjustment, and relates to the technical field of brain deep nerve regulation, and the method comprises the steps: collecting a local field potential signal of a brain deep target region of a target patient, and extracting a beta frequency band power spectrum density and a gamma frequency band phase synchronization index as neural activity characteristic parameters; determining an individual baseline value and a preset threshold value based on historical data, and outputting a stimulation adjustment trigger signal when the beta frequency band power spectral density exceeds the individual baseline value and the gamma frequency band phase synchronization index is lower than the preset threshold value; in response to the trigger signal, calculating an optimal stimulation parameter combination through a gradient descent optimization algorithm and executing nerve regulation; and monitoring the signal change after regulation and control, calculating a relative change rate and updating a threshold value. Through a two-parameter joint judgment mechanism and a threshold updating strategy, individualized adaptive adjustment of stimulation parameters is realized, and the stimulation accuracy and the treatment effect are improved.

The invention provides a calcium imaging neural signal extraction method and product based on self-supervised pre-training, and relates to the technical field of artificial intelligence. Based on a pre-training data set containing a large amount of unlabeled two-photon calcium fluorescence imaging data, a pre-training decoding network and a corresponding pre-training task are self-supervised, and a pre-trained space-time coding network is trained by taking minimization of a pre-training loss function as a target; the method comprises the following steps of: firstly, establishing an encoder and decoder network, establishing fine-tuning data with accurate space-time marked neuron calcium signal positions through simulation, simultaneously performing fine-tuning on the encoder and decoder network by taking minimization of a fine-tuning loss function as a target, and checking the algorithm extraction precision based on various actual imaging data to obtain a calcium imaging signal extraction algorithm network with strong generalization performance. Therefore, a calcium signal extraction model which can be generalized in various imaging conditions and model biological brain regions can be obtained through training without manual labeling, a high-accuracy neural signal extraction result is obtained, the application difficulty that a deep learning model lacks data generalization ability is overcome, and the accuracy of a neural signal extraction result is improved. Calcium imaging neural activity analysis based on self-supervised pre-training and wide in application is achieved.

Devices, systems, software, and methods are provided for treating sleep dysfunction in a subject using nighttime deep brain stimulation. Deep brain stimulation is performed with a neural recording device that records brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep features or sleep stages. An adaptive deep brain stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep features or sleep stages to target sleep dysfunction. Methods and systems are also provided for performing closed-loop therapy with a deep brain stimulator that records brain electrical signals from subcortical or cortical neural activity associated with selected sleep features or stages and automatically adjusts deep brain stimulator settings and / or delivers deep brain electrical stimulation when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected.

The invention is applicable to the technical field of neuroscience, and provides a depression patient brain nerve activity difference acquisition method, which comprises the following steps: constructing an experimental normal form containing two emotion stimulations including physiological and social pain stimulations, collecting first EEG signals of a plurality of depression patients in a first test group and a second test group in an experiment of the experimental normal form, and calculating the depression patient brain nerve activity difference. Preprocessing each first EEG signal to obtain a second EEG signal of each subject depression patient in an emotional stimulation stage of an experiment, and processing the second EEG signals in combination with a micro-state analysis strategy and an electroencephalogram traceability analysis strategy to obtain a first EEG signal; brain nerve activity differences of the depression patients in the first test group and the second test group are obtained when the depression patients face physiological and social pain stimulation, so that a brain function network of the depression patients is comprehensively understood, a bottom layer mechanism of brain functions and activity modes is disclosed, and the method has obvious advantages in research of abnormal nerve mechanisms of the depression patients and has good application prospects. And a new way is provided for early diagnosis and treatment of NSSI behaviors of depression patients.

Methods and systems are provided for treating chronic pain using spinal cord stimulation in combination with a neural recording device that records electrical signals from neural activity associated with chronic pain. Machine learning computational models are used to classify patterns of neural activity associated with various pain states and pain relief. Neural signatures of “chronic pain” and “pain relief” are used to assist with spinal cord stimulation programming to determine therapeutic stimulation parameters that achieve analgesia without paresthesia, the minimum stimulation amplitude needed for effective therapy, and to predict whether spinal cord stimulation is likely to be effective in relieving pain. A closed-loop therapy system is provided comprising a spinal cord stimulator that records brain electrical signals from neural activity associated with pain and automatically adjusts spinal cord stimulator settings to deliver electrical stimulation to the spinal cord when pre-specified patterns of neural activity associated with pain are detected.

The invention relates to a device for risk assessment of nervous system diseases, and a processor of the device is configured to receive eye movement data and electroencephalogram data which are associated in a time sequence, and fuse a double-branch deep neural network in a model through multiple modes to assess the risk of the nervous system diseases, performing feature extraction and attention mechanism-based cross-modal feature fusion on the eye movement data and the electroencephalogram data to obtain cross fusion features; and outputting risk assessment results and risk assessment parameters of at least one type of nervous system diseases through an output layer of the multi-modal fusion assessment model. According to the method and the device, the problem that diagnosis sensitivity and universality are insufficient due to the fact that diagnosis only depends on a single information source in related technologies is solved, disease features can be more comprehensively constructed from two levels of neural activity and behavior response by fusing data of two modes of eye movement and electroencephalogram, parallel diagnosis is further performed for multiple types of diseases, and the diagnosis efficiency is improved. The sensitivity and applicability of neuropsychiatric diagnosis are improved, and particularly, the method has more remarkable accuracy in early stages such as mild cognitive impairment and the like.

Systems and methods for cortical mapping to optimize placement of a neural interface in a brain of a patient are provided. A method for cortical mapping of a brain of a patient includes implanting an electrode array proximate to the brain of the patient at a position. The electrode array may include a flexible substrate and a plurality of electrodes arranged on the flexible substrate. The method may further include monitoring and electrophysiological mapping of the brain by causing the patient to perform, attempt to perform, or imagine performing an action over a period of time, recording, via the electrode array, neural activity exhibited by the patient in response to the action or imagined action performed by the patient, decoding the neural activity to determine a correspondence between the neural activity and the action, and determining a level of confidence, wherein the level of confidence is based on the correspondence.

The invention discloses a transcranial alternating current stimulation system and a transcranial alternating current stimulation method based on event-related potential double-component alignment. The system comprises an individualized feature extraction module, a dynamic phase mapping module, a real-time synchronous triggering module and a closed-loop feedback module, wherein the individualized feature extraction module extracts an ERP component latency period and a dominant oscillation frequency based on task state electroencephalogram data; the dynamic phase mapping module establishes phase alignment of an ERP component time window and a tACS waveform to realize a brain oscillation control process; the real-time synchronous triggering module calculates an evoked potential incubation period and a visual presentation incubation period of a corresponding task item, accurately matches a peak value of the evoked potential with a corresponding peak value of a tACS waveform, reads a current phase of the tACS waveform in advance, performs prospective prediction, adjusts the starting time of each stimulation, and realizes millisecond-level stimulation synchronization; the closed-loop feedback module monitors the brain state on line and dynamically corrects alignment parameters to ensure that the stimulation effect is stable; according to the method, accurate phase alignment of tACS and brain endogenous ERP components is realized, neural activities in a specific cognitive process can be effectively enhanced or inhibited, and the accuracy and individualization level of neural regulation and control are improved.

The invention discloses an electroencephalogram data processing method for electroencephalogram glasses, which comprises the following steps: acquiring original electroencephalogram signals acquired by the electroencephalogram glasses, synchronously acquiring multi-mode auxiliary signals such as eye movement, acceleration and myoelectricity, and monitoring and acquiring quality parameters in real time; forming an initial data set containing the original electroencephalogram signal, the multi-mode auxiliary signal and a quality mark; taking the initial data set as input, and outputting a processed data set containing pure electroencephalogram signals and neural activity features through preprocessing, multi-modal artifact separation and personalized feature extraction in sequence; and packaging the processed data set and processing process meta-information thereof into a standard format file, executing local structured storage and cloud synchronous storage, recording a data operation log through a block chain technology, ensuring data security through an encryption technology, and completing whole-process data management.

Embodiments can take brain activity measurement and decode into continuous language. Embodiment can use non-invasive brain recordings, such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) to detect changes in blood oxygen level that are coupled to neural activity. These brain recordings can be used by a language reconstruction model that involves a neural language model to predict a next word in a sequence and an encoding model to decode the recordings into continuous language, or sequences of words.

The invention discloses a three-dimensional convolution method for decoding an imaginary language electroencephalogram topographic map. The method solves the problem that for a multi-rhythm electroencephalogram topographic map, an existing convolutional network method is insufficient in modeling capability, so that the decoding precision of the multi-rhythm electroencephalogram topographic map in an imaginary language is remarkably reduced. In the aspect of rectangular BEAM reconstruction, a frequency band specificity self-adaptive variation Kriging interpolation model is built, an optimal variation function is automatically selected according to spatial variation characteristics of electroencephalogram power of each frequency band, a high-resolution BEAM graph sequence under multiple frequency bands and multiple time steps is built, and spatial continuity and CNN structure adaptability are considered. A double-branch three-dimensional convolutional neural network is designed, high-dimensional feature extraction is performed on space-time and space-frequency band tensors, a multi-scale structure and dynamic expression ability of neural activity are mined, discrimination of two feature vector branches is dynamically weighted, and the recognition ability of a classifier to a language imagination electroencephalogram mode is improved.

The invention relates to the technical field of cognitive data processing, in particular to a physical stimulation driven lymphatic circulation and cognitive improvement method and system. The system comprises a physical stimulation frequency influence evaluation module, a stimulation mechanical vibration analysis module, a lymph circulation vibration calibration module and a stimulation frequency drive adjustment module. A pressure sensor and a temperature sensor can be used for monitoring corresponding physical stimulation pressure and stimulation skin surface temperature in real time and performing stimulation frequency influence evaluation and stimulation mechanical vibration analysis so as to generate a physical stimulation mechanical vibration effect; and acquiring a lymph circulation velocity and an electroencephalogram neural activity signal, performing neurocognitive level analysis and vibration response calibration, and performing lymphatic circulation cognitive regression analysis and stimulation frequency driving adjustment at the same time, so as to generate a physical stimulation frequency driving adjustment scheme corresponding to lymphatic circulation cognitive improvement. According to the method, the incidence relation among physical stimulation, lymphatic circulation and cognitive promotion can be accurately analyzed.

The invention relates to the technical field of sleep state monitoring, and particularly discloses an intelligent sleep state monitoring method and device based on brain waves, brain wave signals of a user are collected through EEG equipment, and a signal processing and analysis algorithm based on artificial intelligence and deep learning is introduced at the rear end to analyze the brain wave signals, so that the sleep state monitoring accuracy is improved. And the sleep state of the user is monitored based on full-frequency aggregation of the brain wave signals. In this way, the sleep state of the user can be automatically identified and displayed in real time based on the brain waves of the user, and improvement suggestions are provided when the sleep state is identified to be poor. For example, a brain balance regulator is used for sleep regulation, and a non-invasive and non-invasive weak stimulation technology is adopted for regulation. According to the method, the prior data table between the sleep state and the current mode is constructed by using an experiment, so that after the sleep state is obtained, the current mode can be adaptively adjusted based on prior knowledge to adjust cerebral cortex neural activity, and the sleep improvement of a user can be better assisted.

A system for personalized neuromodulation includes a neuroimaging recording device including sensors for detecting neural activity in a patient. An optimization module determines a personalized neuromodulation treatment protocol for the patient. The optimization module includes a neuromodulation target optimization module for determining neural targets in the patient for the personalized neuromodulation treatment protocol. The optimization module includes a neuromodulation parameters optimization module for determining optimized neural stimulation parameters for the personalized neuromodulation treatment protocol. A personalized neural stimulation device receives the neural targets from the neuromodulation target optimization module and receives the optimized neural stimulation parameters from the neuromodulation parameters optimization module. The personalized neural stimulation device delivers the personalized neuromodulation treatment protocol to the patient. The personalized neural stimulation device includes at least one neural stimulation device for stimulating the targets in the patient. A neuroimaging navigation system determines a placement of the neural stimulation device(s) about the patient.

The invention discloses a method for detecting interpersonal nerve synchronization under audio-visual stimulation, which comprises the following steps of: designing a double-person super-scanning experiment normal form, collecting double-person electroencephalogram signals, and constructing a database of visual stimulation and auditory stimulation; preprocessing the data in the database to obtain electroencephalogram signals of four different frequency bands delta, theta, alpha and beta, extracting the electroencephalogram signals of the alpha frequency band, segmenting the electroencephalogram signals, and calculating a correlation ISC value between subjects of each segment; an intra-brain network and an inter-brain network are constructed by constructing a functional connection matrix, and the similarity of the intra-brain network and the global efficiency of the inter-brain network are calculated, so that the cooperation and synchronization degree between neural activities of subjects under visual stimulation and auditory stimulation is effectively evaluated. The invention further discloses a system for detecting interpersonal nerve synchronization under audiovisual stimulation. According to the method, the difference of subjects is reduced by standardizing experimental conditions, and the influence of single sensory stimulation on an intracerebral network activation mode is studied.

The invention provides a transcranial magnetoacoustic stimulation closed-loop feedback method and system based on electroencephalogram guidance. The method comprises the steps that original electroencephalogram signals of a target brain area are collected and preprocessed; target brain region activity features are extracted based on the preprocessed electroencephalogram signals, and the neural activity state of the target brain region is judged according to the target brain region activity features; according to the state judgment result, initial stimulation parameters are generated and optimized in combination with a preset stimulation response model; the generated initial stimulation parameters are converted into actual stimulation signals, the transcranial magnetoacoustic stimulation device is controlled to execute ultrasonic and magnetic field stimulation on the target brain area, and closed-loop feedback is conducted according to the activity state of the target brain area. According to the method, the limitation of a traditional transcranial stimulation method in the aspects of real-time feedback, individualized adaptation, artifact interference and safety control is effectively solved, accurate, controllable and individualized intervention of the neural activity of the target brain region is achieved, and a repeatable, safe and efficient technical means is provided for nerve regulation and control research and clinical application.

Devices, systems, software, and methods are provided for treating bradykinesia with deep brain stimulation (DBS). In particular, DBS is performed with a neural recording device that records brain electrical signals from neural activity associated with intended movement of the subject and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain when pre-specified patterns of neural activity associated with the intended movement are detected. Machine learning computational models are used to detect and classify patterns of neural activity associated with intended movement. The neural signatures of "intended movement" are used to assist with DBS programming to determine therapeutic stimulation parameters that boost movement when a patient starts to prepare for movement and during movements but stop when the patient is resting.

The invention relates to the field of ultrasonic sleep aiding, and provides an ultrasonic-infrasound coupled sound wave whole-brain sleep regulation and control method and device. The whole-brain sleep regulation and control method based on the ultrasound-infrasound coupling sound waves comprises the steps that electroencephalogram signals of a user are collected through electroencephalogram collection equipment; physiological parameters of the user are obtained through physiological state monitoring equipment; a control device is adopted to control and adjust the fundamental frequency, pulse width, pulse repetition frequency, difference frequency, intensity and other parameters of two columns of ultrasonic waves generated by a sound wave emission device according to the electroencephalogram signals and the physiological parameters; a sound wave emitting device is adopted to generate and emit two columns of ultrasonic waves which are close in frequency and face to face, the two columns of ultrasonic waves are fed into the brain face to face, and the two columns of ultrasonic signals inhibit the cerebral cortex activity when passing through the cerebral cortex; meanwhile, interference occurs in the deep brain target nuclear region, low-frequency infrasound beat frequency waves are formed, electroencephalogram slow wave rhythm resonance is induced, sleep-related neural activities of the deep brain target nuclear region are synchronized, and coordinated regulation and control of the whole brain region are achieved.

The embodiment of the invention discloses an ultrasonic nerve regulation and control system. The system comprises an ultrasonic transducer array, a signal generation module and a power amplification module, wherein the signal generation module is used for determining a target nerve rhythm in response to a nerve regulation instruction for a target brain region, and determining a target ultrasonic parameter matched with the target nerve rhythm from each candidate ultrasonic parameter; the signal generation module is also used for generating an ultrasonic signal according to the target ultrasonic parameter; the power amplification module is used for amplifying the ultrasonic signal to obtain an amplified ultrasonic signal; and the ultrasonic transducer array is used for outputting ultrasonic waves to the target brain region according to the amplified ultrasonic signals so as to induce neural activity matched with the target neural rhythm in the target brain region. According to the technical scheme provided by the embodiment of the invention, the fineness of nerve regulation and control can be improved.

The invention provides a memory enhancement system and method based on a closed-loop regulation brain-computer interface, and relates to the technical field of medical equipment, and the system comprises a multi-channel neural activity detection unit which is used for collecting the field potential waveforms of a plurality of brain regions, related to memory cognition, of the brain of a target object; the electroencephalogram signal analysis unit is used for sending a corresponding control signal to the electrical stimulation output unit under the condition that the field potential waveform of any target brain region meets a preset triggering condition; and the electrical stimulation output unit is used for applying electrical stimulation to the target brain area under the condition of receiving the control signal sent by the electroencephalogram signal analysis unit. According to the memory enhancement system and method based on the closed-loop regulation and control brain-computer interface provided by the invention, by synchronously implanting the microelectrodes of the hippocampus multi-subregion and the temporal lobe cortex of the brain in a cross-scale manner, the waveform information of the memory coding spike ripple is effectively identified, and the accurate stimulation output moment is fed back; and a customized closed-loop brain-computer interface system memory regulation strategy can be provided for different individuals.

The invention provides a tactile perception full cortex nerve imaging system and method. The tactile perception full cortex nerve imaging system comprises a central control module, a tactile stimulation module and a cortex imaging module. During use, the tactile stimulation module is arranged on the skin of the to-be-tested living body, it is ensured that the tactile stimulation module and the to-be-tested living body move at the same time without displacement when the to-be-tested living body is in a waking state, and tactile fixed-point stimulation of the to-be-tested living body in the waking state is achieved. The tactile stimulation module comprises a plurality of tactile stimulation channels and a plurality of tactile stimulation sites; the central control module performs gating control on the plurality of tactile stimulation channels based on a preset tactile stimulation position to obtain a selected tactile stimulation site, and transmits tactile stimulation current / voltage generated based on a preset tactile stimulation current / voltage parameter to the tactile stimulation site corresponding to the selected tactile stimulation channel; the cortex imaging module collects activity information of single-cell neurons in a cortex multi-subbrain region of a living body to be detected during tactile stimulation, and obtains high-resolution full-cortex neural activity in real time.

The invention relates to an acupuncture brain machine interface system based on brain-like recursive network model decoding. The system comprises an electroencephalogram acquisition module; an electroencephalogram preprocessing module; the needling parameter fitting module is used for accurately fitting needle body motion parameters through a polynomial regression method; the state space model is used for accurately simulating the nonlinear change characteristic of the neuron membrane potential according to the needle body motion parameters output by the acupuncture parameter fitting module; the brain-like recursive network model is used for depicting time dependence of neural activities of a single brain region under the acupuncture action and correlation of neural activities among different brain regions; a Kalman filtering generator; and the neural decoder is used for performing behavior decoding on the optimal internal state output by the brain-like recursive network model updated by the Kalman filtering generator. The system integrates electroencephalogram signal acquisition, acupuncture parameter acquisition, brain nerve activity modeling and acupuncture manipulation decoding, can perform real-time online operation, and is high in time resolution and high in response speed.

The invention discloses an MEG data bad segment detection method based on a variational auto-encoder and clustering, and belongs to the technical field of magnetoencephalogram data processing. The method comprises the following steps: collecting cranial nerve activity original data, extracting time domain features and frequency domain features, inputting the time domain features and the frequency domain features into a variational auto-encoder, and converting the time domain features and the frequency domain features into low-dimensional potential variables; taking the sum of the reconstruction loss and the KL divergence as a total loss function in the variational auto-encoder, and minimizing the total loss function through a back propagation algorithm; extracting low-dimensional potential variables in the variational auto-encoder, and performing clustering analysis on the low-dimensional potential variables by using a trained clustering algorithm to distinguish bad segments from good segments; and decoding the MEG bad segment data identified by the trained clustering algorithm back to the original feature space. According to the method, the robust performance is shown in the low-signal-to-noise-ratio, large-source and multi-source environments, and the strong robustness and the excellent imaging performance are shown.

The invention provides a brain wave detection data processing method and system based on AI, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly, obtaining an original electroencephalogram signal set composed of a plurality of segments of electroencephalogram signal sequences which are continuously collected through multiple channels and have time stamps, and then carrying out the quality optimization processing of the original electroencephalogram signal set; the method comprises the following steps: acquiring an effective electroencephalogram signal set according with a detection standard, then performing feature extraction processing on the effective electroencephalogram signal set to obtain an electroencephalogram feature combination reflecting a neural activity mode, and then calling a pre-trained electroencephalogram analysis model to perform mode recognition processing on the electroencephalogram feature combination to obtain a neural activity model. An electroencephalogram detection result containing the abnormal activity time period identifier and the corresponding brain region positioning information is generated, finally, an electroencephalogram processing instruction containing space-time information is generated based on the electroencephalogram detection result and sent to the target device to trigger response operation, and the accuracy and efficiency of electroencephalogram detection are improved.

A dichoptic visual stimulus is presented to a user's eyes by presenting a first visual stimulus to a first eye of the user, the first visual stimulus being presented with a first modulation, and presenting a second visual stimulus to a second eye of the user, the second visual stimulus being presented without the first modulation. The first visual stimulus and second visual stimulus are configured to cause the user to perceive the first visual stimulus and the second visual stimulus as a single visual element, and to perceive the visual element as being unmodulated by the first modulation. Neural signals are obtained from a neural signal capture device configured to detect neural activity of the user. In response to determining, based on the neural signals, that the first eye is focused on the first visual stimulus, a computing system executes a command associated with the dichoptic visual stimulus.

Described herein is a microphysiological system for models of disease. Specifically, induced pluripotent stem cells (iPSCs) and iPSC-derived cells, including those obtained from disease patients, are seeded onto microfluidic “chip” devices to study cellular development and disease pathogenesis. Herein, neurodegenerative disease modeling, including Parkinson's Disease (PD) is shown to reproduce key PD pathology in a vascularized human model that contains neurons relating to PD pathology. Such compositions and methods are used for research for PD biomarkers, patient screening for PD risk assessment, and therapeutic discovery and testing. A panel of biomarkers are generated through analysis of living PD-chips by neural activity, whole transcriptomic, proteomic, and metabolomic analysis, and functional enzyme tests of media and tissue. Introducing therapeutics through a vasculature channel, coupled with blood brain barrier penetration studies can be assessed for efficacy in the human neural cells present in the PD-Chip.