840 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 a VR-based unmanned aerial vehicle inspection method and system, a storage medium and a program product, and the method comprises the steps: calculating a low-polygon terrain model, and carrying out the subdivision smoothing processing, and obtaining a three-dimensional terrain image; matching and registering the panoramic image sequence and the three-dimensional topographic image to obtain a point cloud map; generating a virtual mining area environment map; calculating image quality parameters; when the image quality parameter is lower than a preset threshold value, generating a semi-real and semi-virtual fusion display picture; transmitting the semi-real and semi-virtual fusion display picture to a VR helmet for display, and synchronizing the attitude information of the unmanned aerial vehicle to the VR helmet; when it is determined that a fatigue feature appears according to the brain wave data, the unmanned aerial vehicle is controlled to be switched to a preset autonomous flight mode; constructing a virtual anti-collision boundary; and obtaining an optimized flight path according to the virtual anti-collision boundary, and controlling the unmanned aerial vehicle to inspect according to the optimized flight path. The continuity and reliability of the inspection task are improved, and the safety risk of the inspection task is reduced.

The invention relates to the technical field of evaluation and management, in particular to a classroom learning dynamic evaluation and management system, which comprises a multi-source heterogeneous data acquisition module, an acoustic sensor array, an optical motion capture unit, an intelligent writing track acquisition device and a brain wave monitoring terminal, the data preprocessing and feature fusion module is used for eliminating environment interference signals and generating preprocessed data; the dynamic learning state evaluation module is used for extracting three-dimensional features and outputting a dynamic evaluation matrix; the group learning portrait generation module is used for generating a multi-dimensional learning portrait in combination with a preset knowledge graph topological structure; the self-adaptive management strategy generation module is used for dynamically generating a management strategy set; and the intelligent feedback execution module is used for driving the self-adaptive illumination system, the wearable vibration reminding device and the AR enhancement projection equipment to execute physical environment regulation and control. According to the invention, personalized strategy dynamic generation is realized, and adaptive regulation and control of the classroom environment and the teaching rhythm are realized in combination with the intelligent Internet of Things equipment, so that an intelligent collaborative feedback closed loop is formed.

The invention provides an intelligent anesthesia depth monitoring method and system. A plurality of physiological parameter sensors are used for synchronously collecting composite physiological signals of a patient, and meanwhile brain wave signals of the patient in the anesthesia process are obtained; analyzing and processing the anesthesia state by fusing an electroencephalogram spectrum analysis algorithm and a nonlinear kinetic model to obtain a consciousness state map of anesthesia depth and consciousness edge fluctuation; performing real-time evaluation on the current anesthesia depth by using an adaptive threshold algorithm in combination with a Bayesian reasoning framework, and performing prediction processing on the trend change of the anesthesia depth in a future short time by using a long-short term memory neural network to obtain an evaluation result and a prediction trend; the anesthetic infusion rate is automatically adjusted so as to maintain the ideal anesthesia depth, meanwhile, potential risks are subjected to intelligent grading early warning processing, and an anesthesia management strategy is generated. According to the technical scheme provided by the invention, the safety and personalized management level of the anesthesia process are improved.

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 provides a nervous system closed-loop regulation and control system based on transcranial opto-acoustic cooperation. The nervous system closed-loop regulation and control system is composed of EEG monitoring equipment, personal information data, an opto-acoustic cooperation system, a multi-mode large model and a psychological disease database. Aiming at a transcranial infrared light and ultrasonic co-stimulation technology of a patient, through optimization of stimulation parameters, photoacoustic bidirectional regulation and control are realized, a multi-modal large model is used as a base, multi-modal data fusion and reinforcement learning are used as technical means, closed-loop photoacoustic co-stimulation system equipment is designed, and intervention parameters are automatically optimized; an effective regulation and control method for psychological diseases is realized through a non-intrusive brain stimulation technology, and adaptive adjustment and closed-loop feedback of parameters are realized. Brain waves of a patient are detected in real time, personal information of the patient is collected, the information is integrated and transmitted into the system after evaluation, intervention is adjusted in real time according to different brain states, and feedback is utilized to adjust parameters of next intervention and stimulate the precise regulation and control process.

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 discloses an emotional pressure relieving and monitoring method combined with a virtual reality technology, and the method comprises the following steps: S1, providing a virtual environment for a user through a virtual reality device, and enabling the user to interact with the environment through wearing a virtual reality head-mounted device and using a controller; s2, collecting physiological data of the user in real time through a biosensor, wherein the physiological data comprises heart rate, brain wave, electrodermal response and oxyhemoglobin saturation; s3, inputting the physiological data in the S2 into a long-short-term memory network model for processing, and predicting emotional changes; s4, adjusting the virtual reality environment in real time to adapt to the current emotional state of the user based on the emotional state analysis and prediction result of the long-short-term memory network model; and S5, repeating the steps S2 to S4 to monitor the emotion of the user. According to the method, solid technical support is provided for emotion pressure management and monitoring based on the virtual reality technology, and a traditional static emotion regulation method can be effectively replaced.

The invention relates to the technical field of music feedback, in particular to a music feedback training interface interaction method and system based on electroencephalogram data, and the method comprises the following steps: collecting electroencephalogram data of a user based on an electroencephalogram collection device, and removing environmental noise and non-target signals through signal amplification and filtering processing to obtain a user electroencephalogram data set. According to the method, the emotion recognition accuracy and time sensitivity are improved by analyzing the brain wave frequency band intensity, judging the emotion state of the user and recording the emotion change time point, and the emotion recognition accuracy and time sensitivity are improved by matching music tracks of the emotion state, combining real-time monitoring of electroencephalogram reaction and adjusting the rhythm, volume and spatial sound effect setting of music. Dynamic adjustment of music output parameters is achieved, the emotion intervention requirement is optimized, emotion improvement and user satisfaction are evaluated according to brain wave changes of the user, and the improvement effect of music intervention on the psychological state of the user can be measured.

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 relates to the technical field of brain power assessment, in particular to an operator brain power assessment and monitoring system based on manned-unmanned flight cooperative tasks, which comprises the following steps: acquiring brain wave signals, voice fluctuation and finger muscle activity data of an operator, and generating operator brain power load data; setting a personalized load reference value and a threshold range of an operator; based on multi-dimensional regression analysis and a self-adaptive deep learning model, a nonlinear relation model between task characteristics and brain load is constructed, the load state is dynamically evaluated, and a real-time load change prediction value is output; predicting a future high-load risk period according to the fluctuation mode of the mental load fluctuation curve; dynamically adjusting task parameters in a multi-task execution process; according to the invention, the dynamic reference setting of the mental load of the operator is realized, so that the individuation and accuracy of load evaluation are ensured, the influence of key task characteristics on the load can be quickly reduced when the load of the operator is increased, and an efficient and accurate load reduction effect is realized.

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 fatigue driving detection method, system and device based on multi-modal feature fusion and a medium, and the method comprises the steps: obtaining the face image information of a driver, and extracting the eye feature data and mouth feature data of the driver; acquiring a brain wave signal of a driver, and extracting concentration feature data and relaxation feature data of the driver; acquiring a steering wheel grip strength signal of a driver, and extracting grip strength change feature data of the driver; performing feature fusion on the eye feature data, the mouth feature data, the concentration feature data, the relaxation feature data and the grip change feature data to obtain multi-modal feature data; and inputting the multi-modal characteristic data into a fatigue state detection model to obtain a fatigue degree detection value of the driver, and judging whether the driver is in fatigue driving or not according to the fatigue degree detection value. The fatigue driving detection accuracy and the driving safety of the user are improved, and the method can be applied to the technical field of vehicle monitoring.

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 field of psychotherapy, and discloses a pain psychotherapy simulation system based on a virtual reality technology, which is used for solving the problem that when a female patient experiences a physiological cycle, a patient biofeedback signal collected by the system can be interfered, and comprises the following steps: combining a head-mounted display, a handle and other control equipment; the method comprises the steps that a patient enters an immersive virtual world through equipment, physiological data of the patient are obtained, the physiological data comprise heart rate data, skin conductivity and brain wave data, an initial disease pain index is calculated according to the physiological data of the patient, a regulatory factor is calculated according to physiological period information, and the initial disease pain index is calculated according to the regulatory factor; the actual disease pain index is calculated according to the initial disease pain index and the regulatory factor, and psychological intervention is performed according to the actual disease pain index, so that the accuracy of evaluating the state of the treated part of the patient by the system is effectively improved, and the use experience of the patient is improved.

The invention relates to the technical field of anesthesia monitoring, in particular to a vital sign monitoring system and method in the anesthesia process. Acquiring brain wave time sequence signals of the pregnant woman and time sequence data of various physical sign parameters; in view of different fluctuation states of physical sign parameters in different anesthesia stages, the anesthesia stage corresponding to each moment needs to be judged. Then, the change fluctuation of each physical sign parameter and the historical moment data value of the same anesthesia stage is analyzed, a fluctuation index is determined, and the weight of each physical sign parameter at each moment is dynamically calculated; and calculating the surgical anesthesia risk index in combination with the sign parameter change period, the data value and the weight at the historical moment. And finally, vital sign monitoring is carried out based on the surgical anesthesia risk index at each moment. According to the method, the weights of the physical sign parameters in different anesthesia stages are dynamically allocated for parameter fusion, the anesthesia risk model is established, more accurate surgical anesthesia risk indexes are obtained, the accuracy of a monitoring result is ensured, and risk trend prediction is realized.

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.

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 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.

Provided is an estimation method for estimating an activity region or an intracerebral network in the brain of a subject, on the basis of brain waves, a magnetoencephalogram, or near-infrared spectroscopy. The estimation method includes: a second acquisition step S11 for acquiring the brain waves, a magnetoencephalogram, or near-infrared spectroscopy data of the subject; and a second estimation step S12 for analyzing the brain waves, the magnetoencephalogram, or the near-infrared spectroscopy data acquired in the second acquisition step S11 and thereby estimating an activity region or an intracerebral network in the brain of the subject.

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 relates to the technical field of anesthesia management, in particular to an anesthesia medicine excess brain function influence assessment system applying artificial intelligence, which comprises a data acquisition module, a data preprocessing module, a model training module, a real-time assessment module, a risk early warning module, a treatment suggestion module, a system interaction module and a remote monitoring module. Through the combination of a convolutional neural network and a long-short-term memory network, spatial-temporal characteristics of brain wave and functional magnetic resonance imaging data are extracted, a brain function thermodynamic diagram is generated in real time, and the influence of anesthetic drugs is analyzed; the system is combined with a reinforcement learning algorithm to dynamically adjust the anesthetic dosage, a risk early warning module dynamically adjusts a threshold value through physiological parameters and tolerance of a patient, an expert team can provide medical assistance in real time through a remote monitoring module, and brain function safety of the patient in the operation or anesthesia process is ensured; according to the invention, the safety, accuracy and individuation level of anesthesia management are effectively 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.