263 results about "Brain function" patented technology

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.

Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention relates to a depression risk detection method based on self-adaptive multi-scale neighborhood perception fusion space-time diagram convolution, and aims to solve the problems that traditional depression diagnosis is subjective and existing electroencephalogram detection space-time feature modeling is insufficient. The method comprises the following steps: acquiring electroencephalogram signals of a testee, and extracting frequency spectrum features through a frequency domain feature extraction module; in combination with an adjacent matrix (independent of an electrode physical distance) which is adaptively learned and normalized in training, embedded features are generated through a graph convolutional neural network; and then capturing long-time-history dependence through a dynamic feature extraction module, realizing feature multi-level fusion by means of a multi-cascade multi-scale convolution module, finally integrating local and global features through an adaptive feature fusion module, and inputting a prediction layer to output a detection result. According to the method, electroencephalogram signal time-space domain joint modeling is achieved, brain region function connection dynamic and multi-scale neural features are effectively captured, information redundancy and key feature loss are avoided, classification accuracy and generalization ability are remarkably improved, depression-related key brain region and function network features can be revealed, and the method has high interpretability and potential clinical application value.

The invention provides an EEG function connection prediction method based on fMRI deep cross-modal representation learning, and relates to the crossing field of neuroiconography and artificial intelligence. According to the method, a sample pair is constructed through time alignment of EEG and a blood oxygen level dependent signal, an end-to-end deep neural network architecture composed of a time projection unit, a cross-attention fusion encoder and a connection synthesis head is designed, and direct mapping from a BOLD signal to EEG function connection is achieved. The model adopts a composite loss function, and gives consideration to the consistency of the prediction connection matrix with the real EEG function connection in numerical precision and topological mode, thereby achieving the high-fidelity reconstruction of the EEG function connection map at the frequency domain level. According to the invention, the topological structure stability of the brain function network can be effectively maintained. Under the conditions of incomplete EEG data, serious noise interference or complete loss, the frequency domain function connection characteristics can still be stably recovered, and a reliable analysis substitution path is provided for brain function network research.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The brain function improvement promotion device comprises: a brain function improvement contribution exercise intensity measurement unit; a target brain function improvement contribution exercise intensity calculation unit; a first determining unit for determining whether or not the measured brain function improvement contribution exercise intensity of a user has become equal to or greater than a target brain function improvement contribution exercise intensity; a sustain time measurement control unit that, when it is determined that the user's brain function improvement contribution exercise intensity has become equal to or greater than the target brain function improvement contribution exercise intensity, performs control so as to measure the sustain time, which is the duration for which the user's brain function improvement contribution exercise intensity is equal to or greater than the target brain function improvement contribution exercise intensity; and a display control unit that, when it is determined that the total of the sustain time has reached a target time, performs control so as to display, on a display screen, the target health function improvement contribution exercise intensity calculated by the target health function improvement contribution exercise intensity calculation unit, actual related data and time, and display data generated by a display data generation unit.

The present application relates to a kind of brain stimulation target point fast positioning method based on facial recognition and AI neural navigation, by obtaining the MRI data of user's head, construct the three-dimensional digital brain model including brain function partition information;Acquisition facial three-dimensional point cloud, with the head surface model reconstructed by MRI registration, establish the space mapping of actual head and brain model;Based on this mapping, determine stimulation target point on model.In treatment, real-time tracking head pose using optical system, and in navigation interface fusion display brain model, target point and the real-time position and angle of magnetic stimulation coil.Determine the best target stimulation angle of coil according to the gyri and sulci orientation of target point, guide operator to adjust coil pose by comparing the difference between real-time angle and target angle.The present application realizes the fast, accurate, visual positioning and dynamic maintenance of target point in transcranial magnetic stimulation treatment, and upgrades the traditional experience-dependent operation to individualized, real-time feedback precision navigation treatment.

The application discloses a brain function state evaluation and psychological disease auxiliary analysis system based on a brain fluctuation map, comprising: a multi-modal electroencephalogram signal acquisition and preprocessing module; a brain fluctuation map feature extraction module; an individualized baseline modeling module; and an adaptive weighting evaluation module based on an attention mechanism, which constructs an individualized deviation vector and adaptively weights the current features and deviation features based on an attention network, and outputs a disease risk probability and a channel contribution weight map. Compared with traditional population norms or simple arithmetic means, the method can more truly reflect the stable ground state of the subject itself, effectively eliminate individual differences and measurement noise, and provide accurate dynamic reference system for subsequent abnormal deviation judgment. Meanwhile, the fluctuation threshold calculation unit sets an adaptive threshold based on a statistical confidence interval, and introduces an abnormal deviation index and a multi-dimensional joint determination, further reducing the false positive risk.

The invention relates to the technical field of brain disease target positioning, in particular to a brain disease target positioning system and method based on functional magnetic resonance imaging. The method comprises the following steps: calculating a whole brain function connection map of an individual patient based on 4D rs-fMRI data, calculating a Z score of each voxel function connection strength in the whole brain function connection map of the individual patient, and screening abnormal voxels based on a preset threshold value; aggregating the abnormal voxels through a clustering algorithm so as to determine a cluster; the therapeutic effect scores of the multiple clusters can be predicted through the machine learning model, the clusters and the candidate therapeutic targets can be screened based on the therapeutic effect scores, the positions of the candidate targets can be accurately determined, and multiple selections of the candidate targets can be provided; the target spot is converted into an accurate target spot suitable for an individual through a nonlinear registration algorithm; and determining a target positioning state according to the deviation between the accurate target and the actual treatment target, and adjusting corresponding parameters based on the target positioning state. The invention provides a plurality of targets and effectively determines the positions of the targets.

The invention provides a depression detection method, system and equipment based on a multi-modal brain image and a medium. The method comprises the following steps: acquiring a functional magnetic resonance image and a structural magnetic resonance image of the brain of a subject; performing collaborative analysis on the time sequence change information of the functional magnetic resonance image and the spatial relationship of the brain region, and extracting brain function characteristics representing brain function activity characteristics from the functional magnetic resonance image; carrying out collaborative analysis on voxel distribution and regional hierarchical relationship in the structural magnetic resonance image, and extracting brain structure features representing brain tissue morphology from the structural magnetic resonance image; inputting the brain function features and the brain structure features into a cross-modal interaction module, and performing cross-modal feature fusion on the brain function features and the brain structure features to generate cross-modal brain features; and inputting the cross-modal brain features into a classification module to obtain a depression detection result of the subject. According to the invention, the depression identification precision can be greatly improved.

A diagnostic system and method for ADHD integrating neurodynamic enhancement and domain adaptation are proposed. This belongs to the interdisciplinary field of medical image processing and artificial intelligence. It solves the technical problems of low accuracy, poor generalization ability, and poor robustness and interpretability in existing ADHD diagnostic methods. The system includes a data input and preprocessing module, a neuromass-chaotic perturbation module, a domain adaptation module with mutual information minimization, and a multimodal graph convolutional network module. The neuromass-chaotic perturbation module captures the nonlinear dynamic characteristics of brain activity, the domain adaptation module effectively eliminates domain differences in multi-site data, graph structure constraint loss ensures the consistency of sample association, and the dynamic characteristics can correspond to the pathological mechanisms of brain functional abnormalities, providing interpretable evidence for clinical diagnosis.

The invention discloses a medicine formula for preventing cerebral infarction and promoting rehabilitation and a preparation method thereof, and belongs to the technical field of preparation of medicines for preventing cerebral infarction, the medicine formula comprises the following raw materials by weight: 10-15 parts of flatstem milkvetch seed, 10-15 parts of dodder, 10-15 parts of tortoise plastron, 10-12 parts of red peony root, 6-9 parts of safflower, and 15-30 parts of red sage root. According to the invention, the flatstem milkvetch seed, the semen cuscutae and the tortoise plastron are used for nourishing liver and kidney, replenishing essence and benefiting marrow, so that the traditional Chinese medicine theory that kidney dominates bone marrow generation and brain is marrow sea is met, and brain function decline is improved from the source; meanwhile, the radix salviae miltiorrhizae, the radix paeoniae rubra and the flos carthami have the effects of promoting blood circulation to remove blood stasis, activating meridians to stop pain, effectively improving brain microcirculation, inhibiting platelet aggregation and reducing blood viscosity, and supplement each other to achieve the effects of strengthening the body resistance to eliminate pathogenic factors and treating both symptoms and root causes, so that the traditional Chinese medicine composition can be used for preventing cerebral infarction of high-risk groups and is also suitable for functional recovery in the rehabilitation period after cerebral infarction.

The invention relates to a method for determining an individualized nerve regulation target spot, and the method comprises the steps: obtaining nerve image data, which are repeatedly measured for multiple times, of a subject and corresponding clinical psychological assessment data in a preset time period; calculating a brain activity index of each voxel in a preset brain region; establishing a plurality of candidate mathematical models to describe the relationship between the brain activity indexes and the clinical scores; selecting an optimal model through statistical test of fitting residual errors; generating a parameter weight map representing'symptom-activity 'association strength based on the optimal model; and finally, determining an individualized nerve regulation target according to the numerical distribution of the map. The invention also relates to a corresponding device, a processor and a computer readable storage medium thereof, and solves the problem of inaccurate target positioning caused by neglecting symptom heterogeneity and individual brain function difference in the prior art through a core methodology of ''longitudinal tracking-multi-model fitting-residual optimal selection''. The curative effect of nerve regulation and control treatment is obviously improved.

The invention discloses a depression detection method based on EEG channel selection and multi-dimensional feature fusion, which avoids the problem of sensitivity of k-means to the clustering number and initial clustering centers, improves the k-means, determines the number of clustering centers by adopting a Karlinski-Halabass criterion, calculates the PageRank value of an EEG channel based on maximum and minimum similarity, and finally determines the number of the clustering centers by adopting the Karlinski-Halabass criterion and the PageRank value of the EEG channel based on the maximum and minimum similarity. Selecting the first k channels as initial clustering centers, designing a self-adaptive threshold model, and selecting channels with distances smaller than a threshold as key EEG channels; constructing a brain function network and a super-brain function network by using the key EEG channel, and extracting low-dimensional time domain features of the key EEG channel, low-dimensional spatial domain features of the brain function network and high-dimensional spatial domain features of the super-brain function network; and designing a multi-dimensional feature fusion strategy based on standard deviation, and fusing low-dimensional time domain features, low-dimensional spatial domain features and high-dimensional spatial domain features to realize depression detection with high accuracy.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

This invention relates to the field of brain function image classification and processing technology, specifically to an adaptive high-order fusion learning method for brain function image classification, comprising the following steps: S1, acquiring the brain function image to be classified and performing image data preprocessing; S2, constructing a high-order functional brain network matrix sequence; S3, adaptively learning the weights of different-order functional brain networks, weighted summing them to further output features; S4, capturing the contextual dependencies of the high-order functional brain network matrix sequence; S5, further extracting features and performing weighted fusion of features; S6, predicting and outputting the classification result; S7, parameter tuning. This invention, through end-to-end adaptive high-order functional brain network fusion learning, utilizes adaptive learning of different-order functional brain network weights to enhance the interpretability of the inference process between input features and prediction results, and captures temporal information before and after the generation of the high-order functional brain network matrix through a self-attention mechanism, accurately and efficiently classifying and identifying images of normal brain function and brain disorders.

The invention, which belongs to the technical field of the medical equipment, discloses a wake-up therapy system for frequency response through auricular vagus nerve magnetoelectricity stimulation, and the system comprises an acquisition conversion module, an anomaly detection module, a spatial modeling module, an anomaly analysis module, a stimulation application module, a monitoring adjustment module, a dynamic adjustment module and an interactive display module. According to the method, accurate analysis from electroencephalogram signals to the space level can be achieved, quantitative evaluation and visual presentation of abnormal brain functions of the patient are achieved, doctors are assisted in judging the brain function state of the patient, and scientific and accurate decision support is provided for awakening therapy of coma or brain injury patients; meanwhile, the therapeutic effect can be evaluated, stimulation parameters can be dynamically adjusted, individualized awakening intervention of the patient is promoted, the nerve over-stimulation risk of the patient is reduced, the abnormal brain area of the patient is accurately positioned, and a basis is provided for precise stimulation.

The invention relates to a functional partition dose optimization and dual-mode registration transcranial ultrasonic nerve regulation and control navigation system, which depends on a conventional CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) medical image and optical positioning hardware platform. By establishing an equipment side source intensity calibration model, a subject specific transcranial space transfer function and a brain function partition driven multi-region dose optimization model, joint optimization and navigation control of the position, posture and driving parameters of the transcranial ultrasonic transducer are realized; on the premise that dose constraints of multiple functional risk areas are met, the stimulation dose of the target functional area is improved, and real-time evaluation and interlocking control are carried out on dose safety in the navigation execution process. Therefore, the problems that in existing transcranial focused ultrasound nerve regulation and control, due to skull differences, a sound field is uncontrollable, the dose of a target area and the dose of a risk area are unbalanced, and navigation registration precision is insufficient are solved, and high-precision, high-safety and individualized transcranial ultrasound nerve regulation and control are achieved.

The application discloses a kind of fNIRS adaptive feedback emotion cognitive collaborative training device and method, it is related to emotional disorder cognitive training technical field.The device includes: data acquisition module, for obtaining original double-wavelength light intensity signal from brain;Data processing module, for the original double-wavelength light intensity signal is carried out multistage pretreatment;Neural feedback module, for calculating multidimensional evaluation index according to hemoglobin concentration data, and using dynamic threshold control algorithm adjusts the difficulty level and feedback threshold of training task;Training module, for training participant based on emotional stimulation task and cognitive training task;Evaluation module, for training evaluation according to comprehensive performance score and feedback threshold comparison result.The present application closely combines brain function equipment with cognitive training and emotional stimulation task, adds dynamic evaluation and real-time feedback to traditional task, and realizes the precise evaluation of user training effect by brain network analysis technology.

The light detection unit (23) includes a light detector (31) for detecting light, a housing container (71) that is cylindrical and houses the light detector (31), and a flexible substrate (73) on which a signal processing circuit including an A / D converter (33) that converts a light signal detected by the light detector (31) from an analog signal to a digital signal is mounted, the flexible substrate (73) being disposed in a bent and deformed state inside the housing container (71).

The present application relates to the technical field of anesthesia state evaluation, in particular to an anesthesia state evaluation method and system based on electroencephalogram data analysis, comprising: obtaining original electroencephalogram signals through an electroencephalogram acquisition device and preprocessing to obtain pure electroencephalogram data; extracting characteristic indexes reflecting electroencephalogram oscillation patterns and synchronization characteristics; analyzing dynamic causal relationships between the characteristic indexes based on a causal inference model, constructing a brain function network representing anesthesia depth evolution; calculating quantitative parameters of network global integration efficiency and local isolation degree; introducing baseline electroencephalogram data before anesthesia induction to calibrate the quantitative parameters; combining an anesthesia depth determination model to determine the anesthesia state grade, and generating an evaluation report by fusing network structure characteristics. The method improves the accuracy and consistency of anesthesia state evaluation and is suitable for clinical anesthesia monitoring scenarios.

The invention discloses an application of N6022 in improving brain injury after cardiac arrest resuscitation, and particularly relates to an application of N6022 or a derivative thereof given through a nose in improving the brain injury after cardiac arrest resuscitation and increasing the survival rate after cardiac arrest resuscitation. Compared with the prior art, the application has the advantages that the increased GSNOR enzyme activity of brain tissues after sudden cardiac arrest resuscitation can be effectively inhibited by nasal injection of N6022 into a sudden cardiac arrest resuscitation rat model; the N6022 can effectively improve the neurological function after cardiac arrest resuscitation and increase the survival rate after cardio-pulmonary resuscitation; n6022 is used for inhibiting the activity of GSNOR, so that the number of survival neurons of brain tissues after sudden cardiac arrest resuscitation can be increased; the delivery mode of the N6022 through the nose can effectively enable the N6022 to enter the brain tissue of the rat. According to the invention, a new application field of N6022 is developed, and a meaningful reference is provided for improving the cerebral function after sudden cardiac arrest resuscitation.

The application discloses a critical patient sedation state evaluation method based on a multi-modal network model, relates to the technical field of medical monitoring and signal processing, and comprises the following steps: analyzing a plurality of channel physiological signals of a patient, constructing a dynamic time-varying function network, and performing multi-scale community detection to identify stable function modules in the network and dynamic connection modes thereof. The network topology mode is fused with instantaneous phase information of the signals to form a fusion feature space-time atlas. After the atlas is subjected to deep feature extraction and structured coding, a sequence modeling network is used to capture time sequence dependence, and finally a quantitative sedation evaluation signal is output by a decoder. Through dynamic modular structure analysis of the brain function network and cross-level feature fusion, the method realizes more continuous and accurate objective evaluation of the sedation state.

The invention discloses an ADHD brain function connection dynamic representation system and method based on a space-time diagram. The method belongs to the technical field of medical image processing and artificial intelligence crossing. The defects of an existing deep learning method during construction of ADHD brain function connection dynamic representation are overcome. According to the system, an end-to-end learnable adaptive graph construction module is designed, individualized topology is automatically found through node embedding learning and zoom dot product attention, and physiological rationality is guaranteed by combining Top-K sparsification and RBF kernel-based prior graph fusion; double-branch space-time diagram convolution is proposed, expansion causal convolution is used in a time branch, diagram attention is used in a space branch, and space-time joint modeling is achieved through adaptive gating fusion; a multi-view contrast learning framework is introduced, and three types of enhancement (time domain; a frequency domain; the method comprises the following steps: (1), using InfoNCE loss and two-stage training to make full use of unlabeled data;

This invention discloses an essential oil-based intervention system for the simultaneous treatment of heart and brain function based on dual feedback of eye movement and heart rate variability, belonging to the field of rehabilitation medicine technology. The system includes: a data synchronization acquisition module, which acquires heart rate variability data and eye movement characteristic data and generates a synchronized physiological data stream; a dual feedback joint assessment module, which assesses the patient's real-time physical and mental state level based on the synchronized physiological data stream; a synchronized intervention decision module, which generates essential oil atomization control instructions and eye movement training control instructions in parallel according to the physical and mental state level; and a synchronized intervention execution module, which drives the essential oil atomization unit and the eye movement training unit to execute corresponding intervention actions synchronously at the hardware level on the time axis. This invention achieves integrated and precise intervention of heart and brain function through dual-modal physiological feedback closed loop and synchronized control, effectively solving the problems of brain separation, delayed intervention, and poor coordination in traditional rehabilitation centers, and improving the synchronous rehabilitation effect of cognitive function and autonomic nervous function in stroke patients.

A brain function regulating agent contains a peptide comprising an amino acid sequence represented by Glu-Hyp-Gly, a salt thereof, or a chemically modified product thereof.

The invention discloses an ADHD child food addiction neural mechanism evaluation method based on fMRI, and the evaluation method mainly comprises the following steps: obtaining the test results of the attention, execution function, intelligence quotient and food addiction of to-be-evaluated children, and dividing the to-be-evaluated children into an ADHD with food addiction group, an ADHD without food addiction group and a health control group according to the results; performing fMRI stimulation normal form execution after grouping, and performing brain function data acquisition; and finally, constructing a mathematical model of neural image data analysis, so that a specific neural circuit of ADHD children food addiction and a mechanism modulated by an emotional state can be revealed non-invasively and objectively, and an important scientific basis and tool are provided for early recognition, pathological mechanism research and targeted intervention strategy development of ADHD co-disease food addiction.

The invention discloses a neonatal precise music treatment system based on amplitude integrated electroencephalogram awakening signals and a use method thereof. The neonatal precise music treatment system comprises a collection module, a customized music treatment content design module, an automatic closed-loop module and a safety guarantee module. The collection module is used for collecting the electrooculogram, the heart rate, the pulse oxyhemoglobin saturation and the amplitude integrated electroencephalogram, screening out an amplitude integrated electroencephalogram awakening signal according to the electrooculogram, the heart rate and preset three-level grabbing logic, and then obtaining the level of the amplitude integrated electroencephalogram awakening signal and the level of the brain function state; the customized music treatment content design module is used for generating music according to a preset source material library, the level of the amplitude integrated electroencephalogram awakening signal and the brain function state level; the automatic closed-loop module is used for controlling playing and stopping of music; and the safety guarantee module is used for controlling the volume and stopping working when the amplitude integrated electroencephalogram awakening signal is interrupted. According to the method and the device, the accuracy of awakening signal grabbing is improved.