47 results about "Conscious State" patented technology

The invention discloses an awareness perception quantum gravitational nerve acceleration unified computing platform and method based on formalized verification, and belongs to the field of high-performance computing, medical AI and quantum information processing. A quantum gravitational coupling neural processing unit (QGCNPU); a consciousness information integral calculation core (phi ICC); formalizing a receipt chain verifier (FRCV); and the mirror reflection symmetry correction module (MRSCM) is used for eliminating the continuous projection mistake and the discrete Boolean mistake through topological equivalence verification of a local observer and a global GodCam visual angle. The system adopts an FPGA + ASIC + quantum processor three-layer heterogeneous architecture, and supports real-time pathological modeling (precision gt; gt) of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and ALS; 99.7%), drug target discovery (acceleration by 50 times) and consciousness state monitoring (time resolution lt; and meanwhile, the mathematical proving performance and the clinical auditing performance of the calculation process are ensured, and the requirements of the global 152 billion dollar neural science and technology market and the 89 billion dollar quantum calculation industry are met.

This invention discloses an intelligent shore-based rescue method, system, and computer-readable medium. The method involves a first device determining the respective rescue routes of a drone and an unmanned vessel based on a user's distress call. The drone conducts a search and rescue operation according to its route, generating the current location of the person in the water. The first device detects the consciousness state of the person in the water based on real-time image data collected by the drone at the current location. Then, based on the detection results, different surface rescue tasks are performed on the person in the water according to the determined rescue method. Therefore, this embodiment determines different rescue methods based on the consciousness state of the person in the water, which not only enables rapid rescue but also improves the success rate of rescue. This provides an automated rescue solution with intelligent perception, precise positioning, and rapid response, solving the technical problem of low rescue rates for people in the water due to mismatched rescue methods, inaccurate positioning, or untimely rescue in existing technologies.

The invention provides an electroencephalogram consciousness monitoring data analysis method and system applied to anesthesia management. The method comprises the following steps: acquiring EEG signal entropy sequence data of an anesthesia object, carrying out dynamic entropy change analysis on the EEG signal entropy sequence data to obtain an entropy change feature vector of the anesthesia object, carrying out time domain phase coupling modeling on the EEG signal entropy sequence data to obtain a phase synchronization feature matrix of the anesthesia object, and according to the entropy change feature vector and the phase synchronization feature matrix, carrying out dynamic entropy change analysis on the EEG signal entropy sequence data to obtain the EEG signal entropy sequence data of the anesthesia object. Generating consciousness state fusion features of the anesthesia object; according to the consciousness state fusion features, anesthesia state prediction is conducted on the anesthesia object, and the EEG signal entropy of the sampling time sequence nodes of the anesthesia object in the second time period is obtained. According to the method, accurate prediction of the EEG signal entropy of the sampling time sequence node of the anesthetic object in the second time period can be achieved, the anesthetic state of the anesthetic object can be monitored more accurately, the anesthetic dosage can be adjusted in time, and the safety and effectiveness of the operation process are ensured.

The invention discloses a medical comprehensive assessment method for old people and related equipment, and relates to the field of health management, and the method comprises the steps: obtaining the basic information of the to-be-assessed old people; carrying out body function state evaluation on the human body to obtain a body function state evaluation result related to the life self-care ability and the falling risk; performing nutrition state evaluation on the old people based on the nutrition evaluation scale to screen the risk of malnutrition; performing psychological state assessment on the old people to obtain psychological state assessment results including cognitive functions, emotional states and consciousness states; and comprehensively integrating the basic information, the body function state evaluation result, the malnutrition evaluation result and the psychological state evaluation result to form an old people comprehensive evaluation report. The evaluation report can reflect the health risk characteristics of the old people from multiple dimensions, and provides a scientific basis for the formulation of subsequent medical schemes, nursing measures or intervention strategies, thereby facilitating the improvement of the pertinence and effectiveness of the health management of the old people.

The present invention provides a system for assessing the state of consciousness of injured persons based on facial recognition, which relates to the field of health assessment technology. It includes a facial information extraction module, a facial feature analysis module, a state of consciousness calculation module, an anomaly detection module, and a state of consciousness assessment module. In the present invention, an infrared camera is used to carefully capture the subtle movements of the muscles around the eyes, corners of the mouth, and forehead, thereby improving the accuracy and comprehensiveness of data collection. By evaluating the blinking frequency, displacement of the corners of the mouth, and stretching amplitude of the forehead muscles, the accuracy and dimension of the assessment are improved. By extracting deformation features and calculating the gradient of motion amplitude, combined with time series analysis, the assessment of the state of consciousness becomes more sensitive and accurate, and is able to detect potential changes in consciousness in a timely manner, thereby responding quickly. The density and distribution feature analysis of mutation points provide rich data interpretation for diagnosis and monitoring, so that the state of consciousness of the injured person can be quickly and accurately assessed without contacting the injured person.

The invention belongs to the field of flight personnel state monitoring, and discloses a meta-consciousness ability training method and system based on a neural biofeedback technology. The system can obtain neural activity data directly related to the distraction state in a real and continuous task situation, hysteresis and unreliability caused by dependence on subjective reports or behavioral expressions are avoided, and the accuracy and objectivity of distraction monitoring are improved from the source. The decoding model is embedded into a closed-loop neural feedback training system, so that an acoustic feedback signal can be triggered in time when a distraction state occurs, a trainee is prompted to perceive attention deviation, and the self consciousness state is autonomously regulated and controlled without external guidance. The closed-loop feedback can strengthen a conditioned reflex mechanism paying attention to a regression task, and the self-monitoring and self-adjusting capability of meta-awareness is remarkably improved.

The application provides a prognosis prediction method and system for chronic Doc patients, relates to the technical field of clinical prognosis evaluation, and collects multi-modal data such as neuroimaging, neuroelectrophysiological data, biochemical indexes and clinical data, carries out standardized processing and feature extraction, and fuses spatial features, time sequence features and key features.Combining a trained consciousness state grading evaluation model and a prognosis prediction model, a feature level data set is generated, and the consciousness state grading and prognosis development trend of the patient are accurately predicted.The application effectively integrates the complementary information of multi-modal data, improves the accuracy and reliability of prognosis prediction, and provides scientific support for clinical decision-making, individualized treatment and medical resource optimization.

The application provides a traditional Chinese medicine preparation for treating or improving cerebral hemorrhage and a preparation method thereof, and relates to the technical field of traditional Chinese medicine pharmacy. The traditional Chinese medicine preparation is composed of nacre, antelope horn powder, dragon's blood, Herba Schizonepetae, Prunella vulgaris, Stink-bug, Smilax riparia, bamboo juice, dried tangerine or orange peel, clubmoss, radix puerariae, Zizania aquatica and leaf of Chinese pear. The preparation has a significant therapeutic effect on cerebral hemorrhage, can obviously improve the neurological deficit of cerebral hemorrhage rats, reduce the water content of brain tissue to alleviate brain edema, simultaneously alleviate the inflammatory response in the body, thereby playing a neuroprotective effect on cerebral hemorrhage rats. In addition, the preparation can obviously improve the consciousness state of cerebral hemorrhage patients, alleviate the degree of neurological deficit, relieve the related TCM syndromes of the patients, simultaneously effectively improve the daily life ability of the patients, significantly improve the total effective rate of clinical treatment, and no obvious adverse reactions are observed in the treatment process, and the safety is good, thereby providing a safe and effective treatment scheme for the conservative treatment of cerebral hemorrhage.

The present invention discloses a consciousness state classification system and method for multimodal brain signals, which belongs to the field of biomedical signal processing. The system includes: a multimodal head data acquisition unit for collecting multi-channel EEG signals and binocular eye movement data, and a multimodal data preprocessing unit for preprocessing multimodal raw data of the human head and obtaining multi-channel EEG feature information and eye movement basic feature information sets; the preprocessing process includes signal preprocessing and feature vector construction steps; multi-channel EEG feature information extraction includes target frequency band EEG data filtering and multi-band EEG data extraction steps, and the obtained original multi-channel EEG signals are preprocessed to obtain multi-channel EEG feature signals that are convenient for subsequent Transformer model encoding. This method solves the problems of information loss in traditional convolutional neural networks during global feature extraction and low classification accuracy of single-input Transformer models.

The invention provides an anesthesia consciousness judgment method, system and device and a medium. The method comprises the steps that a target electroencephalogram signal of a target object is obtained; on the basis of the target electroencephalogram signal, consciousness state information of the target object is determined through a consciousness judgment model, and consciousness states comprise a waking state, a shallow anesthesia state and an unconsciousness state; wherein the consciousness judgment model is a machine learning model, the consciousness judgment model is obtained based on training of training samples, the training samples comprise sample electroencephalogram signals, and the sample electroencephalogram signals comprise sample waking state data, sample shallow anesthesia state data and sample unconsciousness state data. Through the trained consciousness judgment model, on the basis of accurately determining the unconsciousness state or the non-unconsciousness state of the target object, the anesthesia depth change and smooth transition of the target object in the operation can be accurately tracked, so that the dosage of the anesthetic of the target patient in the operation is more accurately regulated and controlled.

The invention provides a traditional Chinese medicine preparation for treating or improving cerebral hemorrhage and a preparation method thereof, and relates to the technical field of traditional Chinese medicine pharmacy, the traditional Chinese medicine preparation is composed of nacre mother of pearl, antelope horn powder, dragon's blood, motherwort fruit, selfheal, aspongopus, radix actinidiae chinensis, bamboo juice, pericarpium citri reticulatae, selaginella tamariscina, radix puerariae, zizania aquatica and folium eriobotryae. The traditional Chinese medicine composition can obviously improve neurological impairment of cerebral hemorrhage rats, reduce the water content of brain tissues to relieve encephaledema and reduce inflammatory response in vivo, thereby playing a role in protecting the nerves of the cerebral hemorrhage rats. Besides, the preparation can obviously improve the consciousness state of a cerebral hemorrhage patient, relieve the degree of neurological impairment, relieve related traditional Chinese medicine syndromes of the patient, effectively improve the daily living ability of the patient and obviously improve the total effective rate of clinical treatment, no obvious adverse reaction is observed in the treatment process, and the preparation is good in safety and free of toxic and side effects. And a safe and effective treatment scheme is provided for the conservative treatment of cerebral hemorrhage.

This application provides a method and system for analyzing EEG consciousness monitoring data applied to anesthesia management. The method includes: acquiring EEG signal entropy sequence data of anesthetized subjects; performing dynamic entropy change analysis on the EEG signal entropy sequence data to obtain the entropy change feature vector of the anesthetized subjects; performing temporal phase coupling modeling on the EEG signal entropy sequence data to obtain the phase synchronization feature matrix of the anesthetized subjects; generating consciousness state fusion features of the anesthetized subjects based on the entropy change feature vector and the phase synchronization feature matrix; predicting the anesthesia state of the anesthetized subjects based on the consciousness state fusion features; and obtaining the EEG signal entropy of the anesthetized subjects at sampling time points in a second time period. This application can accurately predict the EEG signal entropy of anesthetized subjects at sampling time points in a second time period, helping to more accurately monitor the anesthesia state of anesthetized subjects so as to adjust the dosage of anesthetic drugs in a timely manner and ensure the safety and effectiveness of the surgical procedure.

The present application provides a method, electronic device, and detection system for detecting a pilot's brain consciousness state, which are applied to the field of biomedical technology. The method collects a dual-channel photoplethysmography signal from the pilot's forehead; determines the pilot's forehead blood oxygen level and blood perfusion based on the dual-channel photoplethysmography signal; inputs the pilot's forehead blood oxygen level and blood perfusion into a pre-trained brain consciousness state score prediction model, so that the brain consciousness state prediction model performs a weighted calculation on the forehead blood oxygen level and blood perfusion based on the pre-trained weight coefficients and outputs the pilot's brain consciousness state score; and determines the pilot's brain consciousness state based on the brain consciousness state score and a preset state score threshold. In this way, the pilot's brain consciousness state can be comprehensively detected, which helps to improve the accuracy of the pilot's brain consciousness state detection.

The application provides a method, system, device and medium for determining anesthesia consciousness, which comprises: obtaining a target electroencephalogram signal of a target object; determining consciousness state information of the target object based on the target electroencephalogram signal through a consciousness determination model, wherein the consciousness state includes a conscious state, a light anesthesia state and an unconscious state; wherein the consciousness determination model is a machine learning model, and the consciousness determination model is obtained based on training samples, wherein the training samples include sample electroencephalogram signals, and the sample electroencephalogram signals include sample conscious state data, sample light anesthesia state data and sample unconscious state data. Through the trained consciousness determination model, the change in anesthesia depth of the target object during the operation and the smooth transition thereof can be accurately tracked on the basis of accurately determining the unconscious state or the non-unconscious state of the target object, so that the regulation of the amount of anesthetic drugs for the target patient during the operation is more accurate.

The embodiment of the invention provides an inquiry method and device and a storage medium. The method comprises the steps that a target medical expression corresponding to the chief complaint content of a patient and consciousness state data of the patient are acquired; determining at least one target red flag symptom from a plurality of preset red flag symptoms according to the target medical expression and the consciousness state data; determining a plurality of inquiry targets according to the target medical expression and the obtained target red flag symptom, and determining an inquiry strategy and an inquiry sequence of each inquiry target according to the consciousness state data; performing interrogation on the patient according to the interrogation strategy and the interrogation sequence of each interrogation target; and outputting the inquiry medical history of the patient according to the answers of the patient to the plurality of inquiry targets. According to the embodiment of the invention, patients with unconsciousness can be accurately inquired.

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

The invention discloses an intelligent terminal and system for evaluating the consciousness state of an unconscious patient based on neurology department monitoring, and belongs to the technical field of medical treatment. According to the method, feature sampling is carried out on the collected neurology department monitoring real-time data of the coma patient, the preset evaluation index of the patient is obtained and analyzed, the key consciousness state risk evaluation parameter is marked as the patient consciousness state basic score of the current monitoring period, and the comprehensive evaluation result of the consciousness state of the coma patient is obtained. An evaluation model is constructed by combining monitoring data collection with key feature extraction, a convolutional neural network of a multi-feature extraction layer and a recurrent neural network of time sequence learning, weights are dynamically corrected by combining individual factors, intelligence and individuation of consciousness state evaluation are achieved, feature vectors of four types of indexes are optimized through the multi-feature extraction layer, and the evaluation accuracy of the consciousness state evaluation is improved. Dynamic correction is carried out by combining the age of the patient, the cause of coma and the monitoring period, so that the evaluation result can reflect the overall consciousness state and highlight the key risk point of the individual.

The invention discloses a critical patient brain function assessment method and system based on electroencephalogram analysis. The method comprises the steps that 1, EEG signals are collected; 2, a data buffering and windowing step; 3, a sub-window data preprocessing step; 4, a brain function prediction step; and 5, a data visualization and alarm step. The evaluation system comprises an EEG signal acquisition device, an EEG signal acquisition module, a data processing module, a brain function prediction module and a visualization module. The critical patient brain function assessment method and system based on electroencephalogram analysis have the advantages that subtle changes of the consciousness state can be captured in time clinically, and objective, continuous and real-time prediction of the brain function state of an ICU patient is achieved.

The present invention relates to a therapeutic system, device and method for sound-based therapy that combines music and frequency-based auditory stimuli to facilitate brainwave entrainment and inducing alterations in the state of consciousness, thereby resulting in therapeutic healing. The therapeutic system for sound-based therapy includes a wearable device (1) that is equipped with plurality of modules in order to provide therapy to the user with the help of sound. The sound-based therapy is provided by amalgamation of sound bites from both the specific frequency-based sound module (5) and classical raga-based sound module (6), thereby creating a unique audio environment and positively influencing and relaxing the user's state of mind.

The application provides a distributed computing and communication system with brain-like consciousness and an implementation method thereof, comprising: a plurality of computing nodes and a consciousness state management module; each computing node comprises a computing unit, a storage unit and a communication unit; the communication units constitute a network on chip (NoC) as a whole; the computing unit and the storage unit are connected with nodes of the NoC, and the cooperation and data sharing between the computing nodes are completed through an efficient communication protocol; and the consciousness state management module is configured on each computing node to constitute a consciousness management system of a CTM, that is, a brain-like model of the CTM, so that the mutual cooperation between each node dynamically adjusts the computing strategy to complete a complex computing task. Each computing node of the application is configured with a consciousness state management module, can perceive the real-time state of the node, and accepts the input from other nodes; through analyzing the data, the module can adjust the computing strategy of the node in real time, and realize the formation and dynamic adjustment of the consciousness.

The application discloses a kind of to the consciousness level evaluation system for wounded person, it is related to personnel consciousness evaluation grading technical field, including the acquisition module for obtaining the electroencephalogram signal and the photoplethysmogram signal of two channels of brain of wounded person, the digitization processing module of corresponding module electric signal digitization conversion, feature extraction module extracts time domain feature, frequency domain feature and entropy from electroencephalogram digital signal, from the ratio of pulse wave digital signal, calculate blood oxygen saturation and calculate blood perfusion index, consciousness evaluation module passes through fusion feature vector input consciousness state assessment model, output continuous consciousness state score, and with multiple threshold mapping obtains consciousness grade.The system of the application carries out time domain, frequency domain and nonlinear analysis respectively in the feature extraction of electroencephalogram signal and pulse wave signal, and blood oxygen and blood perfusion degree calculation, realizes the quantitative description and grading determination to the consciousness level of wounded person.

This invention provides a collaborative control method based on consciousness impairment assessment and monitoring and synchronous neural stimulation. The method includes: an eye-tracking monitoring and analysis system extracting eye-tracking feature data from eye-tracking signals using image preprocessing and eye-tracking feature extraction algorithms; a server-side management system combining scale assessments and eye-tracking feature data, utilizing a multi-source data fusion assessment algorithm to output the patient's consciousness state assessment results and stimulation parameter information; a neural correlation electrical stimulator receiving the stimulation parameter information and using a timing synchronization control algorithm to synchronously initiate neural electrical stimulation with the eye-tracking monitoring and analysis system; and the server-side management system dynamically adjusting the consciousness state assessment results and stimulation parameter information based on post-stimulation eye-tracking response characteristics and stimulation parameter feedback, reusing the multi-source data fusion assessment algorithm to form a closed-loop control. This invention achieves timing synchronization and command closed-loop through a collaborative linkage algorithm, ensuring efficient collaborative operation of multiple modules and adapting to complex clinical scenarios such as ICUs and rehabilitation departments.

The application discloses a consciousness level recognition device based on electroencephalogram data and a computer readable storage medium. The device can perform the following operations: obtaining multi-lead scalp electroencephalogram data of a patient to be recognized; inputting the multi-lead scalp electroencephalogram data into a trained neural network model for consciousness level recognition operation, to output a response vector composed of response intensities of each lead, the response intensity representing the correlation degree between the electroencephalogram feature of the lead and the target consciousness level; calculating the distance between the response vector and the average response vector of the target consciousness level group; and determining the consciousness level recognition result of the patient to be recognized according to the distance. The consciousness level recognition scheme based on electroencephalogram data can improve the accuracy of distinguishing between the vegetative state and the weak consciousness state, and realize objective and accurate evaluation of the consciousness level.

The invention belongs to the technical field of electroencephalogram signal processing, and particularly relates to a subthreshold unconscious electroencephalogram signal acquisition system which is composed of a masking material automatic generation module, a stimulation induction module, an electroencephalogram signal acquisition and analysis module and an optical stereoscope hardware platform. Subthreshold unconscious electroencephalogram signals are collected by acquiring initial resolution, establishing a visual experiment engine, generating Mondrian masking pictures of color blocks at different positions, exploring influence factors of consciousness breakthrough and brain network function connection conditions and analyzing differences of brain dominant brain areas and brain function connection conditions. The accuracy of subthreshold stimulation, the repeatability of the experiment and the induction effectiveness of an unconscious state are ensured, a Mondrian picture with a high masking effect is automatically generated, the accurate adjustment of an experiment interface is ensured, and the accuracy and the signal-to-noise ratio of electroencephalogram signal acquisition are improved.

The invention relates to the technical field of consciousness state monitoring, in particular to a consciousness state real-time monitoring system and method based on multi-modal physiological signal fusion, and the system comprises a server side and a medical care side; the server comprises a physiological index data acquisition module used for acquiring current physiological index data of a target patient, and a feature extraction module used for performing feature recognition extraction on the acquired current physiological index data to form corresponding feature data; the consciousness state recognition module is used for taking the extracted feature data as input data, inputting the input data into a pre-trained consciousness state classification model, and outputting consciousness state type data corresponding to the target patient; and the feedback module is used for feeding back the consciousness state type data corresponding to the target patient to the medical care terminal of the medical care personnel.

The invention relates to an intelligent mind and pupil monitoring method and device, and the method comprises the steps: collecting a brain heat radiation signal, a micro-motion feature and a pupil video sequence of a patient, and obtaining the cerebral cortex neural activity data of the patient; inputting the pupil video sequence into a deformed pupil positioning model, and extracting a pupil contour, a pupil diameter, a light reflex incubation period and a contraction speed curve; inputting the brain heat radiation signal, the micro-motion characteristic and the cerebral cortex neural activity data into a time-frequency fusion neural network model, and identifying the consciousness state of the patient; according to the pupil contour, the pupil diameter, the light reflex incubation period, the contraction speed curve and the consciousness state of the patient, a comprehensive neural state index report of the patient is generated. According to the method, sub-pixel-level pupil contour segmentation is achieved, the pupil diameter is accurately extracted, the method has higher adaptability to light reflection latency and contraction speed curves, pupil deformation, shielding and illumination changes, and finer changes of the consciousness state can be captured.

The present disclosure provides a method, apparatus, and device for estimating the category of consciousness state based on heartbeat-induced EEG, which can be applied to the field of EEG signal detection technology. The method for estimating the category of consciousness state based on heartbeat-induced EEG includes: transmitting focused ultrasonic signals to multiple encoding sites in the somatosensory cortex and insula of the target subject's brain to obtain multiple encoded EEG signals; decoding the multiple encoded EEG signals based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain multiple target EEG data, wherein the predetermined EEG cycle interval is determined based on the peak value of the R wave in the target subject's electrocardiogram signal, and the target EEG data includes multiple envelope data corresponding to the encoding sites; performing variance calculation on the multiple target EEG data to obtain a target EEG variance index; and obtaining the target subject's consciousness state category information based on a predetermined consciousness state threshold and the target EEG variance index.

The embodiment of the invention discloses a consciousness state detection system. The system comprises a first association index determination module, a second association index determination module and a consciousness state detection module. Wherein the first association index determination module is used for determining a first association index of a target object in a preoperative stage, and the first association index comprises a first heart rate association index and a first heart rate variability association index; the second association index determination module is used for determining a second association index of the target object in the intraoperative stage or the postoperative stage, and the second association index comprises a second heart rate association index and a second heart rate variability association index; and the consciousness state detection module is used for detecting the consciousness state of the target object according to the first association index and the second association index. According to the technical scheme of the embodiment of the invention, the problems of high hardware cost, high operation complexity and high analysis complexity of consciousness state detection can be solved.

The invention discloses an electroencephalogram data-based consciousness level identification device and a computer readable storage medium. The device can execute the following operations: acquiring multi-lead scalp electroencephalogram data of a patient to be identified; the multi-lead scalp electroencephalogram data are input into the trained neural network model for consciousness level recognition operation, so that a response vector composed of response intensity of all leads is output, and the response intensity represents the correlation degree of the electroencephalogram features of the leads and the target consciousness level; calculating a distance between the response vector and an average response vector of the target consciousness level population; and determining a consciousness level identification result of the to-be-identified patient according to the distance. By means of the electroencephalogram data-based consciousness level identification scheme, the accuracy of distinguishing the vegetative state from the weak consciousness state can be improved, and objective and accurate evaluation of the consciousness level is achieved.

The invention relates to the technical field of biomedical signal processing, and discloses a whole brain consciousness level visual analysis method and equipment, and the method comprises the steps: obtaining resting state electroencephalogram data and task state electroencephalogram data of a target object; performing resting state power spectrum density typing based on the resting state electroencephalogram data to obtain a resting state electroencephalogram channel quantitative score; task state feature quantitative analysis is carried out based on the task state electroencephalogram data, and a task state negative wave function score is obtained; performing weighted fusion based on the resting state electroencephalogram channel quantitative score and the task state negative wave function score to obtain a local consciousness comprehensive index; and carrying out whole-brain consciousness visual imaging based on the local consciousness comprehensive index. According to the method, the consciousness state of each local brain region of the whole brain can be comprehensively, accurately and intuitively presented.