39 results about "Event-related potential" patented technology

The invention discloses a multi-modal electroencephalogram classification method under multi-source interference based on a multi-head attention mechanism, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: synchronously acquiring three types of electroencephalogram modal signals of motor imagery, steady-state visual evoked and event-related potentials, and synchronously acquiring multi-source interference data; performing data preprocessing on the multi-mode signal data; time domain, frequency domain and spatial domain three-dimensional features of each electroencephalogram mode are extracted, and meanwhile, physical features of interference data are quantified; the multi-domain features of the three types of electroencephalogram modes serve as parallel query vectors, the multi-source interference features serve as key value vectors, interference suppression weights are dynamically distributed through a layered attention mechanism, and anti-interference enhanced multi-mode fusion features are generated; and the multi-modal fusion features are processed through a gating circulation unit and a time domain attention module, a multi-modal classification result is output, and online learning and weight updating are carried out. According to the method, a robust decoding scheme can be provided for a brain control interaction system in a complex environment.

The invention discloses a cognitive improvement method and system based on multi-modal data. The cognition improvement method comprises the following steps: acquiring first multi-modal data of a patient before treatment; carrying out amyloid protein load grouping on the patients on the basis of amyloid protein loads in the brains of the patients so as to obtain corresponding initial cognitive training schemes; performing cognitive training based on the initial cognitive training scheme during the treatment period of the patient, and collecting second multi-modal data during the treatment period; based on the multi-modal data before and after the treatment of the patient, acquiring multi-modal change data of the patient in combination with the brain network group atlas; and inputting the multi-modal change data of the patient into a preset model to output a cognition improvement scheme for improving the cognition index of the patient to the greatest extent, and pushing the cognition improvement scheme to the patient for cognition training. According to the method, by integrating A beta-PET data, functional nuclear magnetic resonance imaging data and event-related potential data, the neuroplasticity change of a patient is evaluated in real time, so that a personalized cognition improvement scheme is dynamically generated.

For use in transcranial photobiomodulation (PBM) or brain wave or heart rate variability (HRV) biofeedback and using event-related potential (ERP) and induced and induced EEG oscillation (ERO) measurements, devices and methods for analyzing and monitoring electroencephalogram (EEG) electrical activity evoked during, prior to, and / or after a neurocognitive test of a human user to stimulate and modulate brain activity. An electroencephalogram (EEG) and photo-biometric modulation (PBM) apparatus in the form of a wearable device with headphones is described for stimulating the brain with PBM or monitoring and evaluating electrical activity generated by the brain of a person during stimulation using biofeedback training and simultaneous occurrence of EEG, also described are methods for testing the cognitive and physiological state of a person during a cognitive test using the provided devices. The PBM light is pulsed to stimulate an increase in target brain activity by providing additional energy to the mitochondria and activating other photosensitive brain processes. PBM stimulation is combined with EEG sensors for detecting EEG responses during cognitive testing using event related potentials (ERPs) and induced and induced EEG oscillations (EROs) for evaluating cognitive status and therapeutic function outcomes of a user.

The invention discloses a device for awareness level evaluation and a storage medium. The apparatus comprises: a processor; the device realizes the following operations: collecting clinical information of a person to be assessed and a task state electroencephalogram signal under a target stimulation normal form; extracting frequency domain characteristics of a specific frequency band and spatial-temporal characteristics of a target event related potential based on the task state electroencephalogram signal, and combining the frequency domain characteristics and the spatial-temporal characteristics into a corresponding electroencephalogram topographic map; inputting the corresponding electroencephalogram topographic map into a multi-modal large language model, and performing image feature extraction by using an image encoder to obtain electroencephalogram features; inputting clinical information into the multi-modal large language model, and performing text feature extraction by using a text encoder to obtain text features; and performing cross-modal attention calculation fusion on the electroencephalogram features and the text features by using a cross-modal fusion module to realize consciousness evaluation so as to output a consciousness evaluation result. By means of the scheme, the consciousness level of the patient can be automatically and accurately evaluated.

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 obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

A cognitive function evaluation device includes a brain signal detection unit, a response motion detection unit, and a calculation unit. The brain signal detection unit detects a brain signal generated by the subject with respect to the stimulus. The response motion detection unit detects response motion of the subject with respect to the stimulus and outputs a response signal. The calculation unit detects an event-related potential and a motion preparation potential from the brain signal, calculates a response speed from the response signal, and evaluates the cognitive function on the basis of the event-related potential, the motion preparation potential, and the response signal.

Provides is a method for detecting a complex target in pedestrian re-identification video streams based on an event-related potential (ERP). According to the method, video streams of a complex target in different scenarios are collected, and are made into experimental paradigms, and electroencephalogram (EEG) data of a subject during watching of a video content is collected. Then brain electrical activity mapping (BEAM) is analyzed, and ERP features are marked, including P300 and P300-D, which correspond to target emergence and target disappearance respectively. Positive and negative sample pairs are constructed, and essential features between classes are obtained based on a method of contrastive representation learning and a method of spatial-temporal feature attention extraction, so as to solve the extreme class imbalance problem of samples and the problem of how to distinguish between two similar classes in a video paradigm.

The application discloses a kind of cognitive promotion method and system based on multi-modal data.The cognitive promotion method includes: obtaining the first multi-modal data before treatment of patient;Amyloid load grouping is carried out to patient based on the amyloid load in the brain of patient, to obtain the initial cognitive training scheme corresponding;Cognitive training is carried out based on initial cognitive training scheme during the treatment of patient, and the second multi-modal data during treatment is collected;Based on multi-modal data before and after treatment of patient, in combination with brain network group atlas, obtain the multi-modal change data of patient;The multi-modal change data of patient is input into preset model, to output the cognitive promotion scheme that cognitive index of patient is most improved, to push to patient and carry out cognitive training.The method will be integrated by A beta-PET data, functional nuclear magnetic resonance imaging data and event-related potential data, real-time evaluation of the neuroplasticity change of patient, to dynamically generate personalized cognitive promotion scheme.

The application discloses a semantic context-related auditory brain-computer interface consciousness detection system, uses a brain-computer interface and a deep learning technology to carry out consciousness detection, designs a speech context-related auditory brain-computer interface paradigm, through using animal sounds as auditory stimulation, adds a semantic context related to the stimulation to improve the attention of a subject to the paradigm, thereby improving the quality of an event-related potential induced by the paradigm and improving the performance of the brain-computer interface. Through improvement of an EEG-Inception model for feature extraction and classification, the EEG-Inception model can extract more effective electroencephalogram signal features and also has a better classification effect under a small sample size. Through innovation and improvement of the brain-computer interface paradigm and a detection and classification algorithm, the application improves the performance of the auditory brain-computer interface and solves the problem that some patients with consciousness disorders cannot use a visual brain-computer interface for consciousness detection because they cannot control eye movement.

The application discloses a kind of multidimensional timbre perception space model modeling method based on electroencephalogram characteristics, which comprises the following steps: collecting a variety of musical instrument single timbre samples and pretreating;To timbre sample, extract acoustic time-frequency domain feature, and extract psychological perception feature by behavior psychology experiment;Multiple musical instrument timbre samples are used as auditory stimulus to carry out electroencephalogram experiment, and corresponding event-related potential ERP signal is extracted as electroencephalogram feature;The dissimilarity of different timbre characteristics is represented by the Euclidean distance of different timbre points;The distance between different timbre points is calculated;According to the Euclidean distance matrix between sample timbre points, a low-dimensional space with mutually orthogonal dimensions is fitted, the dissimilarity of timbre feature vector is transformed into low-dimensional space, the timbre feature vector is directly mapped into low-dimensional space, a point set is formed, and the similarity and dissimilarity of each musical instrument timbre are directly displayed.The application represents the mapping relationship between acoustic characteristics, psychological perception characteristics and electroencephalogram characteristics.

The application discloses a brain-computer interface method and system based on real-time closed-loop vibration enhancement, relates to the technical field of brain-computer interface, and comprises the following steps: processing EEG signals through a filtering and feature extraction algorithm, generating a regression vector and identifying an event-related potential as an expected signal; constructing a distributed EEG signal decoding network, optimizing node cooperation through task clustering; adopting an adaptive algorithm update node estimation of a diffusion strategy, optimizing signal decoding through dynamic adjustment of combination coefficients and inter-node cooperation; generating a virtual feedback signal according to a decoding result, fusing the virtual feedback signal with the EEG signal and feeding back to the system, adopting machine learning to optimize classification output, and adjusting model parameters to enhance brain rhythm. The method disclosed by the application can accurately capture the change characteristics of the electroencephalogram signal, dynamically adjust the cooperation mode between nodes, and improve the signal decoding accuracy. The decoder parameters can be adjusted in real time, the electroencephalogram rhythm synchronization is enhanced, and the recognition effect of the motor imagination task is improved.

The invention discloses an electroencephalogram signal processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal collected under intermittent task stimulation, and based on an event mark corresponding to each task stimulation in the electroencephalogram signal, extracting an event related potential corresponding to each event mark; for each event-related potential, performing multi-level feature extraction on the event-related potential in a time dimension to obtain a target time sequence feature; feature extraction is carried out on the target time sequence features based on the channel dimension, and target spatial-temporal features are obtained; and determining a brain activity mode when the target object executes the cognitive task based on the target spatial-temporal characteristics of the multiple event-related potentials. The electroencephalogram signals in the task state enrich the amount of information related to the specific task, multilevel feature extraction is carried out on the event related potential extracted from the electroencephalogram signals in the time dimension, the decoding capacity for the cognitive task is improved, and then the accuracy of electroencephalogram information processing is improved.

The application discloses a kind of flying disc shooting training mental fatigue intelligent identification and evaluation method, it is related to flying disc shooting training monitoring technical field, the specific steps of this method are as follows: with firearm firing electric signal as time zero anchoring time sequence window, as unified reference;Synchronous acquisition event-related potential signal in corresponding window, extract nerve potential feature and bind;Action and performance characteristics are synchronously collected throughout, and are homologous with nerve characteristics Binding;Based on individualized threshold, identify mental fatigue using three-level linkage rule;Finally, with continuous effective shooting as unit, fatigue classification evaluation is completed;The application builds standardized time reference, realizes the homologous synchronization of multiple devices, constructs linkage feature system, improves the accuracy and stability of mental fatigue identification;Establish marksman individualized reference threshold system, complete mental fatigue classification evaluation, combined with shooting decision link, locate deviation link, provide quantitative guidance for training adjustment, optimize control mode, improve training scientific nature and efficiency.

Devices and methods for analyzing and monitoring electroencephalographic (EEG) electrical activity evoked during neurocognitive tests using event-related potential (ERP) and evoked and induced EEG oscillations (ERO) measures in human users. Electroencephalography (EEG) devices in the form of wearable apparatus with headphones and cognitive test interface with concurrent EEG monitoring and evaluation of electrical activity generated by a person's brain during stimulation are described, along with description of methods for testing person's cognitive and physiological state during cognitive tests using the provided devices. EEG sensors for detecting EEG responses during cognitive tests using event-related potentials (ERP) and evoked and induced EEG oscillations (ERO) for evaluation of user's cognitive status and functional outcomes of treatment. Additionally, devices and sensors for monitoring heart rate, heart rate variability (HRV), electrocardiogram (EKG), and photoplethysmography (PPG), and analysis of evoked heart rate, HRV, and pulse volume responses during cognitive tests. The devices may be used to assess psychophysiological responses and assist users with monitoring mental performance, cognitive function, stress, anxiety, fatigue, mood, behavioral performance and mental focus and acuity.

The application discloses a kind of consciousness disorder evaluation method and system based on electroencephalogram and language processing response, visual face image and hierarchical auditory sentence of self-reference are presented to subject simultaneously, construct multimodal audio-visual mixed stimulation paradigm, and P300 event-related potential and hierarchical language tracking features are simultaneously induced.Electroencephalogram signal is collected and preprocessed, time-domain P300 features and frequency-domain hierarchical language tracking features are extracted and multi-domain feature fusion is carried out;Cross-subject meta-learning classification model is constructed, and the feature is adaptively calibrated using hypernetwork, and the instruction following ability and language processing ability grading are output;Finally, combined with online statistical test and offline hierarchical analysis, cross-validation output consciousness state comprehensive evaluation result is carried out.The application realizes the cross-subject precision discrimination under small sample condition, and provides an objective, bed-side deployable evaluation tool for patients with consciousness disorder.

Devices and methods for stimulation and modulation of brain activity using transcranial photobiomodulation (PBM) or electroencephalogram or heart rate variability (HRV) biofeedback, and devices and methods for analyzing and monitoring evoked electroencephalogram (EEG) electrical activity before and / or after neurocognitive testing using event-related potentials (ERP) and evoked and stimulated EEG oscillations (ERO) measurements in a human user. Electroencephalography (EEG) and photobiomodulation (PBM) devices in the form of wearable devices with headphones for stimulating the brain with PBM or using biofeedback training with simultaneous EEG monitoring, as well as a description of a method for testing a person's cognitive and physiological state during cognitive testing using the provided devices, are described. PBM light is pulsed to stimulate an increase in target brain activity by providing additional energy to mitochondria and activating other photosensitive brain processes. PBM stimulation is combined with an EEG sensor to detect EEG responses during cognitive testing using event-related potentials (ERP) and evoked and stimulated EEG oscillations (ERO) for the assessment of the user's cognitive state and treatment of functional outcomes.

The invention relates to an auditory event related potential system of a self-adaptive hearing threshold, and the system comprises an acoustic stimulation module which is used for generating an adjustable sound pressure stimulation signal with the carrier frequency of 500-4000 Hz and the modulation frequency of 35-110 Hz; the electroencephalogram acquisition module is used for acquiring electroencephalogram signals of a subject in real time; the neural response detection module is used for performing spectral analysis on the electroencephalogram signals and calculating the signal-to-noise ratio SNR at the modulation frequency, namely SNRgt; when a threshold value is set, judging that neural response exists; the self-adaptive threshold value adjusting module is used for iteratively adjusting the sound pressure level based on the neural response result and determining the sound pressure level of the lowest extraction response as a hearing threshold value; and the ERP parameter calibration module is used for automatically setting an ERP stimulation sound pressure level according to the hearing threshold value. The system is a system which does not need subjective cooperation of a subject, can automatically measure a hearing threshold value and is used for ERP stimulation parameter calibration, so that the problems of subjective stimulation intensity setting, neglect of individual differences, poor data quality, difficulty in being suitable for special crowds and the like in an existing ERP system are solved.

The invention discloses an integrated non-intrusive, safe and user-friendly electroencephalography (EEG) system capable of classifying signals generated from both Event Related Potential (ERP) based steady-state visually evoked potential (SSVEP) and pure cognition, leveraging Riemannian Geometry-based signal classification algorithms for precise command generation. The system seamlessly combines SSVEP-based visual stimuli with cognition-based EEG signals to provide a comprehensive interface for brain-computer interaction (BCI) applications. Riemannian Geometry techniques are employed for robust signal classification and efficient command generation, enhancing the system's accuracy and reliability.

The invention discloses a brain-computer interface method and system based on real-time closed-loop vibration enhancement, and relates to the technical field of brain-computer interfaces, and the method comprises the steps: processing an EEG signal through a filtering and feature extraction algorithm, generating a regression vector, and recognizing an event-related potential as an expected signal; a distributed EEG signal decoding network is constructed, and node cooperation is optimized through task clustering; updating node estimation by adopting a self-adaptive algorithm of a diffusion strategy, and optimizing signal decoding by dynamically adjusting a combination coefficient and cooperation between nodes; and generating a virtual feedback signal according to a decoding result, fusing the virtual feedback signal with an EEG signal, feeding back the fused signal to a system, optimizing classified output by adopting machine learning, and adjusting model parameters to enhance the EEG rhythm. According to the method, the change characteristics of the electroencephalogram signals can be accurately captured, the cooperation mode between the nodes is dynamically adjusted, and the signal decoding precision is improved. Decoder parameters can be adjusted in real time, electroencephalogram rhythm synchronism is enhanced, and then the recognition effect of motor imagery tasks is improved.

The present invention provides an event-related potential identification method based on an adversarial convolutional neural network, comprising: collecting electroencephalogram (EEG) signals to be identified; constructing a feature extraction network comprising multiple convolutional layers; after adversarial learning, the feature extraction network extracts a feature vector group of the EEG signal in terms of time, space, and frequency dimensions related to the event-related potential layer by layer; constructing a classifier comprising two softmax layers; identifying the feature vector group of the event-related potential by the classifier to obtain an event-related potential identification result; wherein, during adversarial learning of the feature extraction network, the feature vector group extracted in each iteration is clustered, the proportion of misclassified samples after clustering is calculated, and this loss is back-propagated to the feature extraction network with a certain weight in an adversarial manner for learning. This method improves the accuracy of ERP identification and has good stability.

The invention provides an anesthesia depth monitoring method, system and device based on auditory response and a medium, and the method comprises the following steps: constructing and outputting a composite auditory stimulation sequence formed by alternate circulation of a steady state evoked segment and a cognitive probing segment, the cognitive probing segment being a rapid presentation with a stimulation interval smaller than evoked potential duration; the continuous scalp electroencephalogram of the subject is acquired. Intercepting steady-state evoked section data, extracting steady-state auditory evoked response features and calculating a first anesthesia depth index; intercepting cognitive exploration section data, extracting event-related potential by adopting an overlapped signal separation algorithm, and calculating a second anesthesia depth index. And determining two confidence coefficients weights based on the signal quality of the steady-state section and the residual noise of the cognitive section, carrying out weighted fusion on the two indexes according to the weights, and generating and outputting a comprehensive anesthesia depth index for real-time and robust anesthesia depth evaluation and prompt. By implementing the technical scheme provided by the invention, the problem of low updating rate of traditional cognitive potential monitoring data can be solved.

Systems and methods for assessing cognitive function are described that use tracked electrical activity of the brain of the individuals in response to a specific sequence of stimuli in generating data sets, which, for example, can be encapsulated as a data structure. The data sets can include tracked specific response types, at different times and amplitudes, including, but not limited to, event related potential signal components. Brainwave features including, event related potentials, are tracked in relation to both pre-attentive brain responses and consciously controlled attention responses.

This invention discloses a rapid field brain injury diagnostic system based on electroencephalogram (EEG) data, belonging to the field of EEG data analysis and diagnostic technology. It includes an external stimulation module that utilizes sound waves and electrical impulses to stimulate limb muscles to induce specific event-related potentials and motor cortical activity rhythms for detection; an EEG data analysis module that performs multi-scale time window preprocessing and deep learning algorithm analysis; and an injury assessment module that determines the type and quantifies the degree of injury. This system can be implemented using a portable EEG acquisition helmet, a portable pulse stimulation device, and a chip-level handheld terminal to achieve a method from stimulation to detection to diagnosis. The overall device is lightweight, suitable for deployment in field scenarios, and can operate stably in complex environments.

The present application discloses an EEG and event-related potential test method, device, electronic device and storage medium based on extended reality, the method comprising: constructing a virtual environment of the real world based on a preset game engine, wherein the virtual environment comprises at least one environmental scene and at least one perceptual scene; determining a target environmental scene, and determining a target perceptual scene based on the target environmental scene and configuration parameters; dynamically adjusting the configuration parameters of the target environmental scene under the target environmental scene and the target perceptual scene, and collecting event-related potential ERP data of the test subject under different configuration parameters; analyzing the ERP data to determine the ERP response of the test subject under different configuration parameters. The method of the present application can improve the ecological validity of ERP research, enrich the types of stimulation, accurately simulate cognitive processes in the real world, better understand how the brain works in complex cognitive tasks, and provide new tools and perspectives for neuroscience research.

The invention discloses a visual identification method and system based on electroencephalogram signal enhancement, and the method comprises the steps: S1, carrying out the grouping of a multi-class image data set, and generating a sequence image through employing a dual randomization strategy; s2, presenting an image sequence to a subject by adopting an ERP normal form, and randomly inserting a task target irrelevant to the main task into the sequence; s3, collecting EEG (electroencephalogram) signals with high time resolution of the whole brain and preprocessing the EEG signals; s4, respectively extracting features by using an image encoder and an electroencephalogram encoder, and carrying out joint training by adopting a supervised contrast learning target containing a hard negative sample weighting mechanism to generate feature representations aligned in a unified semantic space; and S5, inputting the aligned feature representation into a Transform-based fusion model, performing depth information interaction through a cross attention mechanism to generate a final fusion feature representation, and outputting an identification result by a classifier. According to the invention, the mixed granularity visual identification performance is improved.

The invention discloses a visual stimulation pattern display normal form, equipment, a medium, a product and an adaptive system, and belongs to the field of brain-computer interfaces. The normal form comprises the steps that at least two stimulation areas are displayed in a display interface, each stimulation area in the at least two stimulation areas at least comprises a peripheral view pattern displayed at a preset frequency, the peripheral view patterns are used for inducing steady-state visual evoked potentials, and different stimulation areas correspond to different stimulation time windows; a silence time window is arranged between every two adjacent stimulation time windows; for each stimulation time window under the current stimulation round, determining a display time period distributed in the current stimulation time window, when the display time period arrives, displaying a central view pattern in the center of the corresponding stimulation area, the display time period being randomly distributed in the current stimulation time window, and displaying the central view pattern in the center of the corresponding stimulation area; the duration of the display period is determined based on the response time of the predetermined component of the subject event-related potential. According to the embodiment of the invention, the number of targets corresponding to the classifiable central view pattern can be increased.

An intention decoding apparatus and a decoding method that quickly and precisely analyze brain waves for decoding an intention decision without requiring an advance brain wave measurement for, for example, a calibration step, and an intention conveyance assist apparatus, an intention conveyance assist system, and a program using a decoding result of the intention decision in the brain. The intention decoding apparatus that analyzes brain waves to decode an intention performs a process to examine a dispersion by classifying brain wave data of event-related potentials corresponding to a plurality of stimulus events into any one stimulus event and the other stimulus events among the plurality of stimulus events, on all the stimulus events, and identifies a classification where the dispersion becomes maximum to identify the one stimulus event in the classification as the intention. The intention conveyance assist apparatus includes a presentation unit that presents a decoding result.

The invention relates to an early detection method and device for motion intention of an MRI (Magnetic Resonance Imaging) patient based on EEG (electroencephalogram) signal characteristics. The method comprises the following steps: acquiring EEG signals of the head, limbs and trunk of the patient by using a multi-modal sensor, and performing baseline drift correction; performing event-related potential analysis on the EEG signal, and performing feature extraction on a slow-wave potential of a central region in an incubation period from about 2 seconds before the motion to the start of the motion to obtain a cortex potential feature; calculating the power spectrums of the mu wave band and the beta wave band of the cortex potential characteristics, and calculating the ERD of the mu wave band and the ERS of the beta wave band before the motion intention occurs; and inputting the power spectrum, the ERD and the ERS into a trained motion intention prediction and decision-making model, and outputting the probability that the motion intention occurs in the current time window. According to the method, the spatial resolution of the electroencephalogram signals is enhanced, the electrical activity changes of different regions of the brain in the motion intention generation process are more comprehensively reflected, and the motion intention prediction accuracy is improved.

The invention discloses a field brain injury rapid diagnosis system based on electroencephalogram data, and relates to the technical field of electroencephalogram data analysis and diagnosis, and the field brain injury rapid diagnosis system comprises an external stimulation module which utilizes sound waves and limb muscle electric pulse stimulation to induce specific event-related potential and motor cortex activity rhythm and utilizes a data acquisition module to perform detection; the electroencephalogram data analysis module is used for carrying out multi-scale time window preprocessing and deep learning algorithm analysis; and the damage judgment module is used for judging the damage type and quantifying the damage degree. The system can depend on a portable electroencephalogram acquisition helmet, portable pulse stimulation equipment and a chip-level handheld terminal to realize a method from stimulation to detection to diagnosis, and the whole equipment is light and convenient, is suitable for being deployed in a field operation scene and can stably run in a complex environment.