656 results about "Brain waves" patented technology

The invention discloses an abnormity early warning method and system based on artificial intelligence, and relates to the technical field of abnormity early warning. The method comprises the following components: S1, brain wave data acquisition and preprocessing, S2, emotional state recognition and feature extraction, S3, specific business data acquisition and arrangement, S4, data fusion and abnormal feature construction, and S5, abnormal early warning decision and notification. According to the method, the brain wave emotion features and the specific business data are fused, abnormal feature construction is carried out by adopting a fusion algorithm of multi-scale feature matching and a support vector machine algorithm based on quantum annealing optimization, the abnormal feature rule can be mined more accurately through the comprehensive data processing and analysis mode, and the accuracy of abnormal feature extraction is improved. Therefore, in the real-time data monitoring and analysis process, the accuracy of abnormal early warning is improved, meanwhile, the system can dynamically adjust the monitoring frequency according to the fluctuation characteristics of the data, the timeliness of early warning information is ensured, and related personnel can take countermeasures in time.

The invention discloses a neurosurgery patient postoperative care risk early warning system, and relates to the technical field of postoperative care risk early warning. According to the postoperative care risk early warning system for the neurosurgical patient, multi-source postoperative data such as brain waves, physiological indexes, behaviors and inflammation are integrated through the data acquisition module, single-index anomaly recognition is achieved through the sub-item risk assessment module, and then overall risk judgment is conducted by further fusing data when single indexes are normal through the comprehensive risk assessment module; finally, the early warning module performs graded early warning, so that multi-dimensional data fusion analysis is realized, abnormity of a single index can be found in time, potential overall risks can be mined through a comprehensive model, the comprehensiveness, accuracy and timeliness of early warning are improved, scientific and accurate risk judgment basis is provided for postoperative nursing, early intervention of medical staff is assisted, and the medical staff is prevented from suffering from early warning. Problems of single postoperative care risk assessment and lack of comprehensive judgment of existing neurosurgery patients are solved.

The invention discloses an anesthesia depth monitoring system and method based on multiple physiological parameters, and the system comprises a data collection unit which is used for obtaining a perfusion index value, an electrocardiogram signal, a brain wave signal, a blood pressure signal of a patient and the individual feature information of the patient; the signal processing unit is electrically connected to the data acquisition unit and used for processing the signals acquired by the data acquisition unit and calculating the perfusion index change rate, the heart rate variability, the anesthesia consciousness index and the blood pressure fluctuation rate; the parameter fusion unit is electrically connected to the signal processing unit and used for combining the parameters calculated by the signal processing unit with the individual feature information of the patient; the individualized correction unit is electrically connected to the parameter fusion unit and is used for adjusting the comprehensive anesthesia depth score generated by the parameter fusion unit; the method has the advantages that multi-dimensional physiological data and individual characteristics can be integrated, precise evaluation and individualized regulation and control of the anesthesia depth are realized, and the anesthesia safety is improved.

The invention discloses a post-stroke cognitive impairment prediction method based on multi-modal feature fusion. The method comprises the steps that firstly, multi-modal information of a stroke patient is collected, wherein the multi-modal information comprises a three-dimensional brain MRI image, an EEG electroencephalogram signal and clinical medical record information; secondly, converting MRI into a tensor, and inputting the tensor into a multi-scale spatial-temporal feature extraction backbone network to obtain MRI modal features; the EEG electroencephalogram signals are subjected to electroneurographic signals and are combined with Transform, and EEG modal features are obtained; the clinical medical record information of the stroke patient is converted into semantic sentences, the semantic sentences are input into a two-channel semantic encoder for encoding extraction, and clinical medical record information features are obtained. And finally, inputting the MRI modal features, the EEG modal features and the clinical medical record information features into a three-modal fusion device to obtain fusion features, and outputting probability prediction through a classifier. The post-stroke cognitive impairment prediction method achieves accurate prediction of post-stroke cognitive impairment, and significantly improves robustness and medical interpretation.

The invention relates to the technical field of electroencephalogram signal processing, in particular to an ontology brain wave audio auditory perception synchronous feedback method, device and system and electronic equipment. The method comprises the following steps: receiving electroencephalogram signals of a collected user on line from electroencephalogram collection equipment through an upper computer, obtaining electroencephalogram signals of a specified frequency band from the electroencephalogram signals, and extracting corresponding electroencephalogram characteristics; according to the electroencephalogram features or preset rhythm parameters, the electroencephalogram signals of the specified frequency band are segmented into a plurality of electroencephalogram segments, a plurality of audio expressions corresponding to the electroencephalogram segments are generated, and feature parameters of the audio expressions are determined according to the electroencephalogram features of the corresponding electroencephalogram segments; generating brain wave audio representation data according to the audio representation corresponding to the electroencephalogram signals of the one or more designated frequency bands, and obtaining brain wave audio according to the brain wave audio representation data. Therefore, the physiological suitability of nerve regulation and control and the artistic expressivity of audio generation are met at the same time, and organic unification of nerve regulation and control and audio generation is achieved.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention relates to the technical field of feature recognition, in particular to an authentication method and system for recognizing user identity based on brain wave features. The method comprises the following steps that EEG equipment is used for collecting electroencephalogram signals of a user under a preset cognitive task, and sample encryption is conducted on the electroencephalogram signals through encrypted data; noise baseline calibration is conducted on the electroencephalogram signals, standard electroencephalogram signals are generated, and electroencephalogram sample AES encryption processing is conducted; performing corresponding adversarial sample generation on the standard electroencephalogram signals through an adversarial generative network; performing electroencephalogram confusion feature injection on the standard electroencephalogram signals by applying a space-time dynamic masking strategy to obtain electroencephalogram confusion injection features; and performing adversarial dynamic feature enhancement training on the electroencephalogram confusion injection features by using the generated corresponding adversarial samples, and generating an adversarial test result. According to the invention, through noise calibration, adversarial sample generation, feature enhancement training and block chain technologies, the accuracy, anti-interference capability and security of the identity authentication system based on brain waves are improved.

The invention discloses a psychological health monitoring and self-adaptive nursing decision-making system and method based on electroencephalogram signals, and relates to the field, and the method comprises the following steps: S1, collecting electroencephalogram signals and eye movement characteristics, and generating a multi-modal original signal data set; s2, electroencephalogram alpha wave asymmetry and eye movement characteristics are extracted, and delta-alpha spectrum overlapping pathological interference is eliminated; s3, inputting an evaluation model and a rule base, matching a DLB pathological marker and a symptom association rule, and marking a mental health risk level; and S4, matching an intervention strategy, and dynamically updating the evaluation model and the symptom association rule. The multi-modal fusion strategy effectively covers an association path of a DLB patient from molecular pathology to clinical symptoms, further provides a high-reliability data basis for subsequent risk level marking, realizes extraction of pure alpha wave energy and correction of prefrontal lobe alpha wave asymmetry aiming at frequency spectrum overlapping interference of delta waves and alpha waves in electroencephalogram signals, and improves the accuracy of risk level marking. Emotion abnormal degree misjudgment caused by false increase of the FAA value is avoided.

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The invention relates to a child attention evaluation and training method and system, and the method comprises the steps: obtaining a brain wave mode, an eyeball movement track, an emotion activation level and autonomic nervous system function data of a child in real time, and generating multi-dimensional biological signal data; based on the multi-dimensional biological signal data, generating evaluation task data; generating dynamic task parameters according to biological signal data and behavior performance data in the evaluation task data; generating an attention feedback signal based on the dynamic task parameters; according to the attention feedback signal, a training task integrating work memory, suppression control and cognitive flexibility is constructed, and cognitive function training data is generated; and based on the cognitive function training data, synchronously sharing the attention performance data of the children to the parent terminal and the teacher terminal, generating life situation intervention suggestions, and iteratively optimizing evaluation parameters according to a long-term effect evaluation result. Therefore, the accuracy of children attention evaluation and training can be improved.

The invention discloses a brain wave monitoring visualization method and system, and the method comprises the following steps: obtaining original brain electrical signals of a user at different time points and task scenes, and carrying out filtering, denoising and segmented cleaning to obtain a preprocessing data set; performing frequency domain analysis on the data set, and extracting waveband power distribution; generating thermodynamic diagram data based on the power value, dynamically superposing the thermodynamic diagram data to the visual field of the user through an augmented reality technology, and displaying the activity intensity of the brain area; and accumulating data for a long time to construct a personalized baseline database, comparing the current power with a scene threshold value in real time, generating warning feedback by associating the thermodynamic diagram when the power is abnormal, and optimizing the baseline database. According to the method, the abstract electroencephalogram signals are converted into the visual thermodynamic diagrams, personalized baselines and dynamic feedback are combined, the problems that traditional monitoring data is difficult to understand, depends on group threshold misjudgment and is lack of dynamic optimization are solved, and the intuition, accuracy and practicability of electroencephalogram monitoring are improved.

The invention provides a transcranial alternating current stimulation method and system with an automatic adjustment function, and relates to the technical field of transcranial alternating current stimulation, and the method comprises the steps: obtaining, compressing and sampling a multi-channel electroencephalogram signal, carrying out the multi-channel reconstruction through the physiological priori and the related information of adjacent brain regions, extracting the feature parameters of target brain waves, and carrying out the reconstruction of the target brain waves. Based on the parameters, dual-stage transcranial alternating current signals of prior stimulation and post stimulation are output; by introducing an adjacent matrix or a graph Laplacian operator in a compressed sensing acquisition and reconstruction link, high-fidelity recovery of cross-brain region oscillation can be maintained under the conditions of low sampling rate and noise; performing layered screening and dynamic fine tuning on characteristic frequency bands of different brain regions by combining general and individualized prior; the target brain wave phase is locked in advance in the first stimulation stage, and the stimulation intensity of each brain region is independently or cooperatively adjusted in the later stimulation stage, so that the requirement of coupling regulation and control of multiple brain regions is met, and the closed-loop intervention effect on nerve states such as insomnia and anxiety is improved.

The invention relates to the technical field of physiological signal processing and sleep monitoring, in particular to a sleep staging method and a sleep staging system for collecting multiple physiological signals, and the sleep staging method and the sleep staging system for collecting the multiple physiological signals synchronously collect auditory meatus photoelectric volume pulse waves, temperature and head micro-motion signals through an in-ear sensor array. According to the method, the signal quality index is calculated, time domain, frequency domain and nonlinear features are extracted, a dynamic weighted fusion mechanism is adopted, feature weights are adjusted according to the signal quality index, a hierarchical depth time sequence learning model is input for sleep staging, and the sleep staging accuracy is improved to 89% or above and is improved by 15-20% compared with a single brain wave method. A sleep state evaluation report and a personalized feedback intervention strategy generated by the system are beneficial for improving sleep quality, a dynamic weighted fusion mechanism enhances system robustness, adapts to different signal qualities and ensures stable performance, and the invention provides an efficient and accurate new method for the field of sleep monitoring.

The invention discloses an auditory cognitive impairment evaluation and screening system, which belongs to the technical field of data analysis, and specifically comprises the following steps: setting basic test parameters and initial stimulation parameters, and synchronously acquiring voice, eye movement trajectory data and brain wave signals by combining space-time anchoring marks to form a multi-modal data set with a timestamp; a multi-stage cognitive test is carried out in an adaptive test engine, and stimulation parameters are dynamically adjusted by using a forgetting curve prediction algorithm according to real-time accuracy and response time; based on the adjusted stimulation parameters, performing time domain alignment on the multi-modal data set by adopting a dynamic time warping algorithm and taking a space-time anchoring mark as a benchmark, extracting features and combining the features into a cross-modal feature vector; and the cross-modal feature vectors are input into a pre-trained LSTM-decision tree fusion diagnosis model, and a quantitative evaluation report of obstacle type and degree grading is output, so that the accuracy of cognitive disorder diagnosis is improved.

The invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention relates to the technical field of integrated sleep management, in particular to a sleep data analysis management system based on artificial intelligence. The method comprises the following steps: segmenting a sleep cycle of a user based on brain wave data to obtain N sleep stages of the user; based on the body movement monitoring data, the pulse monitoring data and the respiration monitoring data, calculating to obtain a first sleep quality characteristic value of the sleep stage of the user, and based on the sleep posture category and a sleep quality weight parameter corresponding to the sleep posture category, determining a second sleep quality characteristic value of the sleep stage of the user; the sleep quality analysis index of the sleep cycle of the user is generated based on the sleep quality parameters of the sleep stage of the user, the sleep early warning signal is generated based on the sleep quality analysis index of the sleep cycle of the user, multi-dimensional data such as brain waves, body movement, pulse, breathing and sleeping postures can be combined, the sleep stage can be accurately divided, and the sleep early warning effect is improved. And sleep quality parameters are calculated through multi-modal data fusion, so that the accuracy and practicability of sleep monitoring are improved.

The invention discloses a driver fatigue state real-time identification system and method based on multi-modal deep learning, and relates to the technical field of fatigue driving detection. Firstly, feature extraction is performed on brain wave shapes and eye movement coordinates, and respective weights are calculated by using an attention mechanism, so that dynamic distribution of different modal features is realized. And then, in-vehicle illumination data is introduced to establish a credibility mapping function so as to carry out adaptive correction on an eye movement weight, thereby effectively reducing interference of a complex illumination environment on an identification result. And carrying out weighted splicing on the corrected multi-modal features, mapping the multi-modal features into a brain-eye collaborative fatigue value, and carrying out judgment in combination with the duration, so as to finally realize stable and reliable early warning control. The method has the advantages of high fusion precision, high environmental adaptability and low false alarm rate while ensuring the real-time performance, and the driving safety guarantee capability can be remarkably improved.

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 provides a meditation effect intelligent scoring method and system based on big data analysis, and relates to the technical field of data processing.The method comprises the steps that 1, brain wave signals, heart rate variability, skin conductance original waveforms and respiratory rhythm signals of a user during meditation are collected in real time through a wearable device, and the brain wave signals, the heart rate variability, the skin conductance original waveforms and the respiratory rhythm signals are sent to the wearable device; environment noise decibel values, illumination intensity and environment temperature parameters are obtained through an environment sensor, and a multi-dimensional original data set fusing the physiological signals and the environment parameters is generated; 2, according to the resting state physiological parameters in the multi-dimensional original data set, Gaussian mixture probability distribution is adopted, personalized reference features are generated, and the personalized reference features comprise mean vectors and covariance matrixes of brain wave signals, heart rate variability, skin conductance and respiratory rhythm parameters; according to the invention, through multi-source data acquisition and fusion, personalized reference construction, multi-dimensional physiological and environmental index analysis and dynamic score adjustment, quantitative evaluation of a meditation effect, which is comprehensive, accurate and real-time and fits individuals, is realized.

The invention discloses a sensory nerve quantitative detection method and system based on electroencephalogram characteristics, and relates to the technical field of electroencephalogram measurement, and the method comprises the steps: building detection files in one-to-one correspondence with users; respectively wearing the current stimulation module and the electroencephalogram detection module on the wrist and the head of the user; current stimulation is applied to a user step by step through a current stimulation module, and gamma waves in electroencephalogram signals of the user are collected in real time through an electroencephalogram detection module; gamma waves in the electroencephalogram signals of the user are analyzed, and whether the intensity of current applied to the user by the current stimulation module exceeds a physical stimulation sensing threshold value of the subject or not is judged; and analyzing severity grades of sensory disorders. The method has the advantages that detection is carried out through brain wave signals without relying on subjective feedback of a patient, interference of factors such as emotion and psychology is avoided, and even a special patient who cannot carry out self-perception report can complete sensory nerve quantitative detection through objective physiological reaction of brain waves.

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.

The invention relates to an intelligent hypnosis method and system based on a brain-computer interface and a storage medium, and belongs to the technical field of human-computer interaction and artificial intelligence. The method comprises the following steps: acquiring an EEG signal of a user in real time through EEG acquisition equipment, and extracting a brain wave segment power value as state input through preprocessing; a deep reinforcement learning model (such as DRQN) selects music type actions (such as classical music and white noise) based on an epsilon-greedy strategy; calculating a reward value (maximizing delta wave increment and inhibiting beta wave) according to the electroencephalogram state change after playing, and optimizing model parameters by adopting Q-Learning; and dynamically adjusting the strategy through iterative interaction until the user reaches a preset sleep target. The system comprises an electroencephalogram acquisition module, a preprocessing module, a reinforcement learning module and a music control module, and realizes closed-loop regulation and control. The method has the advantages of high personalization, high hypnosis efficiency (induction to sleep in 1-7 minutes), flexible adaptation to different users and self-evolution optimization capability, and effectively improves the sleep induction effect.

The invention relates to the technical field of biological wave resonance, provides a dynamic synchronous tracking detection method and system based on biological wave resonance, and aims to solve the problem of low dynamic synchronous detection precision of heart and brain coupled biological wave signals in the prior art. The method comprises the following steps: acquiring a mixed oscillation waveform feature formed by coupling an electrocardiosignal and a brain wave signal; identifying an electrocardiogram wave oscillation component and a brain wave rhythm fluctuation component from the mixed oscillation waveform features; determining a resonance correlation interval according to the electrocardiowave oscillation component and the brain wave rhythm fluctuation component; performing interference elimination processing on a composite oscillation waveform in a resonance correlation interval in the mixed oscillation waveform characteristics; and based on the dynamic evolution characteristics of the eliminated composite oscillation waveform in the continuous time sequence, generating a tracking detection atlas representing heart-brain coupling resonance. According to the invention, the dynamic synchronous detection precision of the heart-brain coupled biological wave signal is improved.

The invention discloses a sleep aiding method and system based on brain wave data, and the method comprises the steps: synchronously collecting brain waves, electrocardiosignals, environmental noise and user voice data through a flexible electrode array, constructing a multi-mode causal graph, and dynamically recognizing the type of a noise source (electromagnetic interference or psychological noise); the method comprises the following steps of: generating a target antagonistic intervention signal (such as reverse sound wave counteracting electromagnetic noise and binaural rhythm relieving psychological pressure) according to the target antagonistic intervention signal, generating dynamically adaptive light pulse and tactile vibration parameters in combination with a psychological-physiological collaborative model, and synchronously outputting sound, light and tactile intervention signals through a multi-modal actuator. Through causal reasoning and multi-sensory cooperative regulation and control technologies, accurate inhibition of noise interference and personalized induction of the sleep state are realized, the sleep time is remarkably shortened, the deep sleep duration is prolonged, and the method is particularly suitable for complex noise environments and anxiety-related insomnia scenes.

The invention discloses an emotion analysis method and system based on multi-mode electroencephalogram eye movement fusion. An emotional induction stimulation sequence is alternately or synchronously displayed through a stimulation presentation module according to preset time sequences such as 5-second vision and 3-second auditory sense, a 64 conductive electrode cap (10-20 system layout, the sampling rate is larger than or equal to 1000 Hz) is triggered to collect electroencephalogram data of a prefrontal lobe, a temporal lobe and the like, and meanwhile eye movement data are obtained through an infrared pupil tracking technology (the sampling frequency is larger than or equal to 120 Hz). 0.5-70 Hz band-pass filtering and ICA artifact removal processing are carried out on the electroencephalogram data, smooth interpolation and Kalman filtering optimization are carried out on the eye movement data, and then electroencephalogram alpha / beta / gamma wave power spectrums, eye movement pupil change rates and other characteristics are extracted respectively. A Transform model based on an attention mechanism is adopted, deep fusion of electroencephalogram eye movement features is realized through feature embedding and multi-head self-attention calculation, and emotion recognition accuracy greater than or equal to 85% is achieved through confusion matrix optimization and five-fold cross validation in combination with an SVM or DNN classifier.

The invention provides an intelligent safety helmet operation fatigue risk early warning classification algorithm based on a fractional order depth extreme learning machine. Belongs to the technical field of intelligent wearable equipment and artificial intelligence. The classification algorithm is executed through the following method, and the method comprises the steps that original data are obtained in real time through multiple sensors integrated on the intelligent safety helmet, and the original data comprise personnel physiological data and environment data; the method comprises the following steps: preprocessing collected original data, performing fractional differential or integral processing on the data by using a fractional calculus theory, and extracting deeper information features; by means of multiple sensors (such as heart rate, brain wave, acceleration and environment sensors) integrated on the intelligent safety helmet, physiological data and environment data of an operator can be obtained in real time.

The invention relates to the technical field of biological wave resonance, provides a migration analysis method and system based on biological wave resonance, and aims to solve the problems of low accuracy and poor anti-interference capability of biological wave resonance anomaly detection in the prior art. The method comprises the steps that electrocardiosignals and brain wave signals of a living body and micro-deformation data of the surface of the living body are collected, and the signal data are jointly aligned in a time-frequency domain; dynamically suppressing interference frequency bands of the electrocardiosignal and the brain wave signal which are jointly aligned respectively; fusing the suppressed electrocardiosignal, the brain wave signal and the micro-deformation data after joint alignment to generate a fusion result; determining an offset feature of the fusion result relative to a preset reference state by using convolution operation; and generating a biological wave resonance migration analysis report about whether the biological wave resonance is abnormal or not according to the continuous length and the spatial distribution range of the migration characteristics in the time domain dimension. According to the invention, the accuracy and anti-interference capability of biological wave resonance anomaly detection are improved.

The invention discloses an English classroom attention regulation and control method combined with brain wave detection, and relates to the technical field of English classroom attention regulation and control, and the method comprises the steps: obtaining a multi-channel brain wave signal of a student in English classroom learning through a brain wave collection device, collecting visual gazing track data through an eye tracker, and obtaining a brain wave signal; capturing a face image sequence through a camera; performing frequency band separation on the multi-channel electroencephalogram signal, extracting energy of a theta wave and a beta wave of a prefrontal lobe, and calculating a theta / beta power ratio; performing de-noising and clustering processing on the visual gaze track data, identifying an effective gaze interval, calculating a reciprocal of a gaze point distribution variance in unit time, generating a visual focusing stability parameter, performing action unit identification on a facial image sequence, extracting smile frequency, eyebrow intensity and blink period characteristics, and outputting a cognitive emotion state score; and constructing a multi-modal attention fusion evaluation model based on the theta / beta power ratio, the visual focusing stability parameter and the cognitive emotion state score.

The invention discloses a psychological counseling method and system based on artificial intelligence, relates to the field of artificial intelligence, and solves the problem of poor effect of the existing psychological counseling method.The psychological counseling method comprises the steps that S1, a plurality of psychological counseling records are collected, question and answer concentration evaluation and screening are conducted on a model user corresponding to each psychological counseling record, and a psychological counseling result is obtained; the method comprises the following steps: S1, obtaining psychological counseling screening data, S2, carrying out vocabulary negative feature analysis on each effective psychological counseling record, obtaining a vocabulary negative index according to an analysis result, and obtaining interactive vocabulary analysis data, and S3, carrying out scene interactive psychological counseling on model users corresponding to the effective psychological counseling records. And S4, feeding back a consulting result to the model user, carrying out brain wave monitoring on the model user in the consulting process, and obtaining interactive behavior monitoring data according to the monitoring result. The psychological consulting method can improve the accuracy and objectivity of the psychological consulting method.

The invention discloses a system and method for performing cranial nerve stimulation based on olfaction, hearing and synchronous magnetomotive stimulation, and the system comprises a magnetic therapy bearing main body which is a magnetic therapy helmet, serves as a basic bearing structure of the system, and is used for installing all functional assemblies, providing magnetic therapy physical auxiliary stimulation, and achieving the cooperation of magnetic therapy and multi-mode stimulation; the olfactory stimulation subsystem is used for providing olfactory stimulation with precise regulation and control; the auditory stimulation subsystem is used for providing personalized audio stimulation; the magnetomotive stimulation subsystem is used for providing magnetomotive stimulation; the nerve stimulation cooperation subsystem is used for planning multi-mode stimulation as a whole and achieving regulation and control. Through the synergistic effect of olfactory stimulation, auditory stimulation and magnetomotive stimulation, three major neural pathways of olfactory, auditory and somatosensory can be activated at the same time, multi-dimensional nerve resonance is formed, the brain wave synchronization degree is remarkably improved, and the adjuvant therapy effect on Alzheimer's disease, cognitive impairment and other diseases is superior to that of single-mode stimulation.