451 results about "Brain waves" patented technology

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

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

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

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

The invention belongs to the technical field of nerve regulation and control engineering, and provides a cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system, which is characterized in that first frequency band range data and second frequency band range data in brain wave signals are coupled to obtain cerebrospinal fluid rhythm data; then, calculating the phase difference between the first frequency band range data and the cerebrospinal fluid rhythm data; finally, the stimulation frequency is dynamically controlled according to the phase difference, and the stimulation current is dynamically controlled according to the brain tissue water volume fraction. The problem that specific metabolism requirements of non-fast eye movement sleep and fast eye movement sleep cycles cannot be matched by adopting single-frequency-band stimulation is solved, dynamic changes of post-traumatic encephaledema and requirements and influences of cerebrospinal fluid rhythm on current control are considered, the real-time adaptive capacity to the dynamic changes of the post-traumatic encephaledema is improved, and the application prospect is wide. And the treatment effect of the Alzheimer's disease is ensured.

The invention discloses an electroencephalogram data processing method for electroencephalogram glasses, which comprises the following steps: acquiring original electroencephalogram signals acquired by the electroencephalogram glasses, synchronously acquiring multi-mode auxiliary signals such as eye movement, acceleration and myoelectricity, and monitoring and acquiring quality parameters in real time; forming an initial data set containing the original electroencephalogram signal, the multi-mode auxiliary signal and a quality mark; taking the initial data set as input, and outputting a processed data set containing pure electroencephalogram signals and neural activity features through preprocessing, multi-modal artifact separation and personalized feature extraction in sequence; and packaging the processed data set and processing process meta-information thereof into a standard format file, executing local structured storage and cloud synchronous storage, recording a data operation log through a block chain technology, ensuring data security through an encryption technology, and completing whole-process data management.

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

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

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

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

The invention discloses an interactive humanoid robot expression simulation method based on artificial intelligence, and relates to the technical field of expression simulation, and the method comprises the steps: collecting a brain wave signal of a user, carrying out the preprocessing, and generating a power spectrum density feature; inputting the power spectral density features into a pre-trained emotion classification model, and outputting an emotion category and an emotion intensity value of the user; performing emotional expression mapping on the emotional category and the emotional intensity value, and outputting a bionic muscle contraction instruction; performing facial adjustment on facial muscle fibers of the humanoid robot according to the bionic muscle contraction instruction, dynamically adjusting surface texture details of the humanoid robot, and generating a simulated micro expression; and capturing user face feedback data through camera shooting eyeballs, evaluating the acceptance degree of the user on the simulated micro-expression based on the user face feedback data, and outputting an expression optimization strategy. According to the method, through deformation matching and expression optimization double-closed-loop modeling driven by user feedback, biological simulation of micro-expressions of the humanoid robot is realized.

The invention relates to the technical field of sleep quality evaluation, in particular to a sleep monitoring optimization system based on artificial intelligence. The system comprises the steps of collecting sleep monitoring data of a user at different collection moments, extracting dominant frequency components in snore audio data through spectrum envelope analysis, extracting audio primitives based on the dominant frequency components, inputting the audio primitives into a snore classification model, outputting a snore category corresponding to the snore audio data of the user, and outputting the snore category corresponding to the snore audio data of the user. Carrying out Bayesian inversion analysis on the brain wave data to obtain a posterior sample of random field model parameters, executing reliability analysis based on the posterior sample to calculate a posterior failure probability, obtaining a first sleep quality index of the user based on the posterior failure probability, obtaining a second sleep quality index of the user based on vital sign data analysis, and sending the second sleep quality index to the user; and obtaining a user sleep quality evaluation result based on the snore type, the user first sleep quality index and the user second sleep quality index. The accuracy and efficiency of user sleep quality evaluation can be improved.

The invention relates to the technical field of brain-computer interfaces, and particularly discloses a fusion system combining a brain-computer interface with transcranial electrical stimulation and a feedback stimulation method. The fusion system comprises a main machine part, an auxiliary machine part, a PCB substrate, a terminal integrated receiver and a feedback calculation module. The host part comprises an electroencephalogram signal acquisition device and a transcranial electrical stimulation device which are both installed on a PCB substrate. The auxiliary machine part comprises an MCU and a first wireless device which are both installed on the PCB substrate. The auxiliary machine part further comprises a battery which is connected to the MCU, the electroencephalogram signal collecting device and the transcranial electrical stimulation device. The terminal integrated receiver has a second wireless device. And the terminal integrated receiver is connected to the feedback calculation module. According to the fusion system, the electroencephalogram signal acquisition device and the transcranial electrical stimulation device are combined to acquire brain waves and feed back and adjust transcranial electrical stimulation parameters, so that nervous excitability can be better induced, and an electroencephalogram value is enhanced.

A method of facilitating a skill learning process or improving performance of a task, comprising: determining a brainwave pattern reflecting neuronal activity of a skilled subject while engaged in a respective skill or task; processing the determined brainwave pattern with at least one automated processor; and subjecting a subject training in the respective skill or task to brain entrainment by a stimulus selected from the group consisting of one or more of a sensory excitation, a peripheral excitation, a transcranial excitation, and a deep brain stimulation, dependent on the processed temporal pattern extracted from brainwaves reflecting neuronal activity of the skilled subject.

The invention relates to the technical field of psychological assessment, in particular to a psychological health assessment method based on VR (virtual reality), which comprises the following steps: based on virtual scene event nodes, analyzing event types, interaction states and priorities, screening adaptive nodes, adjusting an acquisition window, judging amplitude variation of brain waves, identifying segmentation moments, and combining extreme values and response intervals. And matching a high-risk reference, and classifying psychological risk indexes. According to the method, through dynamic mapping of user interaction behaviors and virtual situation events, whole-course closed-loop management of data streams is achieved, an acquisition window is adjusted in a linkage mode according to multi-source parameters, signal segmentation is defined in real time according to actual brain wave motion and a scene cognitive state, brain wave extreme values and behavior response time sequences are synchronously extracted, and coupling analysis is conducted on multi-dimensional features; dynamic grouping of psychological risks is jointly set based on multiple index intervals, timeliness guarantee is obtained for data in a high-frequency interaction scene, and an evaluation result can reflect continuous evolution of psychological states of a user in a complex environment.

The invention discloses a brain wave monitoring and intelligent feedback combined modularized head ring glasses device, and relates to the technical field of brain wave sensing and adjusting equipment.According to the device, brain electrical activities, eye movement behaviors, eye fatigue states, wearing fitness of the head ring glasses device, environmental noise interference and use situations of a user are monitored in real time; the brain wave fitting quality index is calculated through the pressure variance, the EEG signal-to-noise ratio and the historical template fitting degree, and self-adaptive compensation adjustment is carried out when the brain wave fitting quality index is unqualified; combining electroencephalogram and eye movement characteristics to calculate a brain-eye collaborative concentration index, and generating a comprehensive adjustment strategy when the brain-eye collaborative concentration index does not reach the standard; and calculating an emotional scene switching index according to the brain wave emotional characteristics, the situation parameters and the historical trend, and triggering personalized emotional regulation when the emotional scene switching index is not matched with the situation parameters. The device can quantify the brain wave and behavior state of the user in real time, realizes intelligent concentration and emotion management, and improves the reliability and individual suitability of electroencephalogram monitoring.

The invention provides an information processing method and system based on a brain-computer interface, and belongs to the technical field of information processing.The method comprises the steps that brain wave signals of a user are collected through a multi-channel electrode; based on STFT, DWT and SVM models, feature extraction and pattern recognition are carried out on the brain wave signals, and control instructions corresponding to the brain wave signals are generated; and controlling the rehabilitation training equipment based on the control instruction to obtain control feedback corresponding to the brain wave signal. According to the information processing method and system based on the brain-computer interface, the signal discrimination robustness can be effectively improved, and the stability and generalizability of the system under individual differences of different patients and environmental interference are guaranteed.

The invention relates to an electric power big data efficient collection and storage system and method based on brain-computer interaction, and the system comprises an electroencephalogram signal collection module which is used for obtaining an electroencephalogram signal of a user through a brain-computer interface, and transmitting the electroencephalogram signal to an edge node in real time; the intention recognition module is used for recognizing the current intention of the user by adopting a pre-training intention recognition model based on the brain wave signal of the user and generating a corresponding electric power data acquisition instruction; the data acquisition control module is used for dynamically scheduling the power sensor nodes in different areas to carry out power data acquisition according to the current intention of the user in combination with the state of the power sensor nodes fed back by the edge nodes in real time; the edge calculation processing module is used for carrying out edge processing and feature compression on the collected power data by adopting edge nodes; and the distributed storage module is used for carrying out distributed storage on the power data subjected to feature compression. Compared with the prior art, the method has the advantages of non-contact rapid instruction input, improvement of response efficiency and the like.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying a value of a heart rate variability metric that indicates a variation in a heart waveform of a patient; identifying a value of a brain activity metric that indicates a type of electrical activity represented by a brain waveform of the patient; providing values for a collection of metrics to a computational model, the values for the collection of metrics including the value for the heart rate variability metric and the value for the brain activity metric; and receiving, from the computational model as a result of having provided the values for the collection of metrics to the computational model, an indication of mental state of the patient.

An augmented reality headset, with an eye tracking device, and a secondary user input device, such as, a brain wave sensor detecting the user's brain waves, a microphone detecting user sounds, or finger recognition sensor, or facial recognition sensor detecting facial gestures. When entering an elevator, the user views elevator input icons, displayed in mid-air. The user gazes at, an elevator floor icon, a cursor follows the gaze to the icon. The user thinks click, or says enter, or finger gestures, or facial gestures, which activates the icon. The activated floor icon, directs the elevator, to move to the floor. The input icons can be associated with activating, an internet web page, or a public device, wirelessly connected to the glasses, like, a multi-user door opener. The touch-free operation, enables the user to avoid bacteria, that may be on physical touch input buttons, of the public door opener.

The invention provides a multi-mode sleep intervention system and method based on self-adaptive dynamic control. The system comprises a sensor module used for collecting brain wave activity signals in real time in a non-invasive mode; the AI module is used for performing sleep stage identification and stage division on the brain wave activity signal through a sleep stage prediction model, judging a current sleep state through a first AI strategy engine and automatically generating an optimal first intervention parameter sequence; the control module is used for issuing a corresponding control instruction to the multi-mode intervention module according to the optimal first intervention parameter sequence; and the multi-mode intervention module is used for executing one or more of sound, light, magnetism, temperature, touch and smelling multi-mode intervention according to the control instruction. Through multi-mode fusion perception, closed-loop monitoring control, dynamic adjustment of an intervention strategy and multi-mode interaction and intervention, the effects of accurately perceiving the sleep state in real time, improving the data anti-interference capability, self-adapting to sleep management, adapting to individual differences and meeting the requirements of different intervention scenes are achieved.

The invention relates to a brain wave signal acquisition device, belongs to the technical field of medical instruments, and solves the problems that in the prior art, a brain wave signal acquisition device is implanted into the cerebral cortex of a human body through craniotomy, the trauma of a patient is large, the surgical risk is high, and various complications can be caused. The brain wave signal acquisition device comprises a bracket main body, an electrode plate and a conductive wire; the stent body is of an open curled cylindrical structure composed of a plurality of grid units, openings are formed in the two axial ends of the stent body, and gaps are formed in the curled butt joint positions on the side wall face of the stent body. The electrode plates are arranged on the grid units; the conductive wire is wound on the stent main body, one end of the conductive wire is connected with the electrode plate, and the other end of the conductive wire is wound to the near end of the stent main body. The brain wave signal acquisition can be realized without a craniotomy operation, so that the trauma to a patient is small, and the operation risk is obviously reduced.

The invention discloses a self-adaptive regulation and control method and system for an in-cabin environment driven by vital signs of a wounded person, and relates to the technical field of crossing of medical equipment and environment control. According to the self-adaptive regulation and control method and system for the in-cabin environment driven by the vital signs of the wounded person, a four-dimensional data acquisition system composed of physiology, environment, medical intervention and space-time scenes is built; the method comprises the following steps: synchronously acquiring multi-dimensional vital signs including brain electricity, skin electricity and other nerve and stress indexes, an in-cabin dynamic environment including detailed parameters such as airflow velocity and particulate matter concentration, external medical operation covering drug dosage and equipment working parameters, and transfer scene data including altitude change and bumping intensity; the limitation that only single environment or physiological data is acquired in the prior art is solved; when a craniocerebral injury wounded person is transported in an air transport mode, the system can be combined with the altitude change rate and the electroencephalogram alpha wave power, the blood oxygen lowering risk can be pre-judged 10 minutes in advance, and the problem that misjudgment is conducted only through current data traditionally is avoided.

The invention relates to the technical field of intelligent learning planning, and discloses an intelligent learning planning system based on multi-dimensional learning data driving. Comprising a main control module, a multi-modal data acquisition module, a data quality enhancement module, a multi-modal feature fusion module, a dynamic strategy planning module, a man-machine intention alignment module, a knowledge topology management module, an evaluation and federal optimization module and an auxiliary visualization module, and the main control module is used for coordinating operation of each module and data scheduling; the multi-modal data acquisition module acquires HRV, EDA and other physiological signals through a wearable device, records a fixation track and an alpha wave proportion through an eye tracker / electroencephalograph, and receives a user instruction in real time through a voice interaction device. According to the method, the dynamic adaptability is higher, full-process dynamic adjustment of stage division-parameter fine adjustment-load balance is achieved through hierarchical reinforcement learning and NSGA-III optimization, and a learning path can be flexibly adapted according to the real-time state (such as sudden fatigue and rapid knowledge point mastering) of a user.

The invention discloses an information data processing system based on brain wave signals, and particularly relates to the field of medical signal processing and brain science application, and the system comprises a data importing and filtering module which supports the input of an original electroencephalogram file and carries out band-pass filtering on the original electroencephalogram file; the bad track detection module is used for automatically detecting and marking bad tracks based on a local outlier factor algorithm, and comparing local density differences between each channel and surrounding channels; the artifact suppression module is used for correcting non-engraving plate transient artifacts in the electroencephalogram; the data restoration and interpolation module is used for carrying out interpolation on the detected bad track and restoring the overall channel layout; the frequency band and ratio characteristic module is used for calculating energy, relative power and ratio of delta, theta, alpha, SMR, beta and betaH frequency bands; the spectrum-space-time characterization module is used for constructing the features into two-dimensional tensors; and the judgment module is used for outputting the mental state category and the attention score. The mental state can be identified based on the juvenile electroencephalogram.

The present disclosure relates to a device and method for generating avatar lip-sync animation based on multimodal biosignals, The device comprises a multimodal data collection unit configured to collect data including biosignal data including brain waves when a user imagines speaking and image data; a preprocessing unit configured to preprocess the multimodal data; a feature extraction unit configured to extract feature vectors including the user's biosignal feature and facial feature from the preprocessed multimodal data; an avatar generation unit configured to generate an avatar; a lip-sync reconstruction unit configured to predict the mouth shape and facial movement when the user imagines speaking by inputting the extracted feature vectors to a pre-prepared lip-sync reconstruction model; and a lip-sync animation implementation unit for implementing an avatar lip-sync animation by applying the mouth shape and facial movement predicted by the lip-sync reconstruction unit to the avatar generated by the avatar generation unit.

The device (1) is used to determine insomnia in a patient (8). It has a sensor unit (2) for recording at least one EEG measurement signal of a brain wave of the patient (8) and a control and evaluation unit (3) for evaluating the recorded EEG measurement signal. The control and evaluation unit (3) is configured to carry out an objective assessment routine and in doing so, for the subsequent recording of the EEG measurement signal, to give the patient (8) a stipulation to open his eyes for the duration of an eyes-open period and to close his eyes for the duration of an eyes-closed period, and then to initiate the recording of the EEG measurement signal during at least one eyes-open period and during at least one eyes-closed period. The control and evaluation unit (3) is further configured to perform an evaluation of the EEG measurement signal thus recorded and in doing so to extract from the EEG measurement signal at least one first partial measurement signal from the at least one eyes-open period and at least one second partial measurement signal from the at least one eyes-closed period, and to decide on the presence of insomnia if the at least one second partial measurement signal from the at least one eyes-closed period has a lower proportion of α waves than in a healthy individual.

A sleep care device is disclosed. The sleep care device is mounted on the ear of a user, is not easily separated from the user's body while the user is in a sleep state, and can reliably measure brain wave signals. The sleep care device according to an example embodiment of the present invention includes a housing, an ear hook, and an ear tip. The ear hook is connected to the housing to form a closed loop, is placed between the user's head and the auricle, and includes an electrode having electrical conductivity. The ear tip is connected to the housing, is placed at the entrance to the external auditory canal, and has electrical conductivity. In addition, the ear hook and the ear tip are made of a material having flexibility and elasticity, and measure the user's brain waves.

The invention relates to the technical field of nerve regulation and control, and discloses a brain wave frequency modulation instrument which comprises a wearing shell and two groups of brain wave frequency modulation optimization modules which are integrally mounted at two ends of the wearing shell and are configured to acquire brain wave signals of a user in real time, according to the brain wave frequency modulation instrument, the defect of single-frequency static stimulation of traditional equipment is overcome, personalized brain wave regulation and control are achieved through the AI algorithm, and the brain wave frequency modulation instrument has the advantages that the brain wave frequency modulation instrument is simple in structure, convenient to use and high in practicability. Frequency conversion guide field signals can be dynamically matched with the brain wave state of a user, and the central control module can adaptively regulate and control multi-module combination. According to the equipment, brain wave optimization, purification and energizing are synchronously carried out, a closed-loop feedback system improves regulation and control accuracy, the wearable design is portable and easy to use, brain wave disorder can be accurately improved, anxiety and fatigue can be relieved, and multi-directional guarantee is provided for physical and psychological health.