27 results about "Alpha wave" patented technology

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 discloses an electroencephalogram signal state evaluation method. The method comprises the following steps: acquiring a to-be-evaluated electroencephalogram signal and a reference electroencephalogram signal; determining a plurality of to-be-evaluated electroencephalogram features and a plurality of reference electroencephalogram features; determining a difference degree between the to-be-evaluated electroencephalogram feature and the corresponding reference electroencephalogram feature to obtain a plurality of difference degrees, and determining a comprehensive difference degree according to the plurality of difference degrees; judging whether the comprehensive difference degree is within a preset normal state range or not; if yes, determining that the to-be-evaluated person is in a normal state; if not, determining that the to-be-evaluated person is in an abnormal state; the types of the electroencephalogram characteristics to be evaluated and the reference electroencephalogram characteristics are theta wave energy characteristics, alpha wave energy characteristics, alpha wave frequency band energy characteristics, aperiodic offset characteristics and aperiodic telescopic transformation characteristics, and the abnormal state comprises fatigue and / or hypoxia. The method can quickly and accurately judge whether the person to be evaluated has an abnormal state caused by fatigue or oxygen deficit. The invention further discloses a state evaluation device, electronic equipment and a computer readable storage medium.

The invention relates to the technical field of nerve regulation and control, in particular to a depressive state adjusting system and method based on transcranial electrical stimulation, and the method comprises the steps: collecting electroencephalogram signals of left and right brain frontal lobe areas of even-level leads; the method comprises the following steps: performing noise filtering on collected electroencephalogram signals of a left brain and a right brain, extracting 8-13Hz power spectrum data of Alpha waves of the left brain and the right brain, calculating a numerical difference value of power spectrums of the Alpha waves of the left brain and the right brain and an average proportion of the difference value in a test time period, and taking the numerical difference value and the average proportion as quantitative indexes of the depression-associated lateral deviation level; the adaptive transcranial electrical stimulation module is matched through the acquisition module according to basic information such as the age and the medical history of a patient, initial parameters are set, the parameters are dynamically corrected through the control module in combination with real-time physiological signals, accurate personalized treatment is achieved, and treatment safety can be guaranteed.

The invention relates to a multifunctional music therapeutic apparatus which comprises a main treatment module provided with a music treatment module, and the main treatment module further comprises a multi-mode physical treatment module, a physiological parameter monitoring module, an interaction and display module and a power supply and safety protection module. The system further comprises an auxiliary operation module, and the auxiliary operation module comprises a therapeutic scheme management unit, a self-adaptive adjustment unit, a data recording and analyzing unit and a safety early warning unit. The multi-mode physical therapy module comprises an acousto-optic stimulation unit, a low-frequency pulse unit and a vibration massage unit; the physiological parameter monitoring module comprises a heart rate / blood oxygen monitoring unit adopting the PPG technology and a forehead single-lead electroencephalogram monitoring unit, and the forehead single-lead electroencephalogram monitoring unit is used for collecting alpha wave and beta wave signals; the self-adaptive adjusting unit receives data of the physiological parameter monitoring module in real time. The multifunctional music therapeutic apparatus integrates multiple therapeutic functions, and the curative effect brought by music therapy can be greatly improved.

The application discloses an immersive space virtual-real interaction method and relates to the technical field of virtual reality; after the game starts, the first sub-time zone of each game stage is taken as a starting data collection window, EEG signal data of a player is collected, an alpha wave trend graph is constructed, through a series of calculation and analysis, including determining a reference frequency range, identifying an abnormal point, calculating an alpha wave variation ratio, an alpha wave evaluation value of the player in the starting data collection window is obtained, this process can accurately evaluate the brain state of the player in the starting stage of the game, the change trend of the player's immersion is predicted in advance and the intensity of the tactile feedback is adjusted in time, the player's frequent "out of the game" in the game process is avoided, the coherence and immersion of the game experience are maintained, when the alpha wave evaluation value of the player shows that the immersion may decrease, the tactile feedback is enhanced to attract attention, the game participation is improved, and the player can continuously immerse in the game.

The invention relates to the field of medical instruments, in particular to an intelligent acousto-optic magnetic sleep treatment system and a control method thereof. The method comprises the following steps: collecting physiological data of a patient in real time; reducing the illumination intensity of the phototherapy module to be below a first illumination intensity threshold value; the phototherapy module is used for dynamic photostimulation; sound waves output by the sound therapy module are adjusted to theta waves, and light output by the light therapy module is switched to warm-color low-frequency light; if the value of the SDNN index of the heart rate variability of the patient is not less than 50ms and the brain wave signal of the patient is beta wave, adjusting the sound wave output by the sound therapy module to alpha wave; if the brain wave signal of the patient is delta wave, reducing the sound intensity output by the sound therapy module to be lower than a sound intensity threshold value; if the brain wave signal of the patient is theta wave, the pulse frequency output by the magnetic therapy module is reduced to be lower than a second frequency threshold value. By adopting the method provided by the invention, the treatment effect of the acousto-optic magnetic sleep treatment system can be effectively improved.

The present application relates to the technical field of neuromodulation, and particularly relates to a depression state adjustment system and method based on transcranial electrical stimulation, comprising collecting electroencephalogram signals of left and right frontal lobe regions of even-numbered leads; after noise filtering of the collected left and right brain electroencephalogram signals, power spectrum data of left and right brain alpha waves 8-13 Hz are extracted, a numerical difference of left and right brain alpha wave power spectrum is calculated, and an average proportion of the difference in a test period is calculated, and the numerical difference and average proportion are used as quantitative indicators of depression correlation lateralization level; the present application matches the transcranial electrical stimulation module according to the basic information such as patient age and medical history through the collecting module and sets the initial parameters, and then the control module dynamically corrects the parameters combined with real-time physiological signals, so that precise personalized treatment is realized, and treatment safety is guaranteed.

The invention discloses an electroacupuncture stimulation scheme personalized generation method fusing myoelectricity and brain function network states, and relates to the technical field of nerve regulation and epilepsy electroacupuncture treatment.The method comprises the steps that alpha wave and beta wave power spectral density, a myoelectricity root-mean-square value and brain region metabolism and blood flow signals are collected, and electroacupuncture stimulation intensity, waveform and frequency are recorded; calculating a brain-muscle collaborative response index, judging brain-muscle pathway collaboration and stimulation effectiveness, and optimizing parameters; performing time-frequency analysis and waveform identification on the electroencephalogram signals in the effective stimulation state, extracting epilepsy sample discharge characteristics, calculating a power change rate, and adjusting stimulation parameters to form a safe stimulation parameter set; constructing a brain-muscle network state space, establishing a reinforcement learning initial model by taking BMEI as a reward and punishment function, and performing offline training to form a model M0; and introducing an online learning module to update the model in real time to form a second model M1, and outputting an individual optimal stimulation parameter Popt to realize safe, effective and individualized long-term treatment optimization.

A method and a system of optimizing a neurofeedback training session are presented. The neurofeedback training session includes an alternating sequence of neurofeedback training periods and cognitive tasks to be performed by a user on a computer device. An EEG signal is obtained from electrodes worn on the user's head during the neurofeedback training periods and during the cognitive tasks, the EEG signal including, amongst others, alpha waves and beta waves. A relative power is determined based on at least an alpha power of the alpha waves and a beta power of the beta waves. One or more characteristics of the relative power over time are determined during the neurofeedback training periods and during the cognitive tasks. A duration of the neurofeedback training periods and / or the cognitive tasks are set based on the one or more characteristics.

The invention relates to the technical field of transcranial alternating current stimulation for brain synchronization of two persons, and particularly discloses a transcranial alternating current stimulation device for enhancing brain synchronization of two persons and an application method of the transcranial alternating current stimulation device. The present invention relates to an emotional training system (100) suitable for situations such as co-emotional regulation, cooperative training, relation modeling, and the like, comprising: a first positive channel (110A) and a second positive channel (110B), which are respectively connected to forehead lobe region electrodes of a first individual and a second individual, for outputting an alternating current at a set frequency, the frequency being set as an intermediate frequency of an emotional-related alpha wave frequency band, i.e., a 10Hz alpha wave frequency band, and the frequency being set as an intermediate frequency of the emotional-related alpha wave frequency band; and the induction synchronization capability is relatively high. According to the transcranial alternating current stimulation device for enhancing brain synchronization of two persons and the application method thereof, the phase synchronization of alpha waves of the two prefrontal lobes can be remarkably enhanced in practical application, and the cross-brain emotion cooperation capability is improved.

The present invention discloses a method for EEG recognition of natural hand movements based on a time-frequency multi-layer brain network, comprising: (1) collecting multi-channel EEG signals of natural hand movements; (2) preprocessing the multi-channel EEG signals to extract the delta wave, theta wave, alpha wave, beta wave, and gamma wave at each time point; (3) constructing a time-domain multi-layer brain network using a wSAR model; (4) calculating a frequency-domain multi-layer brain network using cross-frequency coupling; (5) combining and standardizing the time-domain and frequency-domain multi-layer brain networks; (6) calculating the multi-layer network metric and the decomposed super-adjacency matrix of the multi-layer brain network, and inputting them into a two-layer graph convolutional network (GCN) to fuse artificial, shallow, and deep features for classification. The present invention proposes a new time-frequency fusion multi-layer brain network using the wSAR model and cross-frequency coupling (CFC), and uses two layers of GCN to comprehensively extract the feature information of the multi-layer brain network, which is conducive to revealing the neurophysiological mechanism of the brain and improving the decoding accuracy of natural movement EEG.

The invention discloses a psychological intervention method of a wearable relaxation device, the wearable relaxation device comprises a wearable waistcoat and a headgear, the front and back of the wearable waistcoat are both provided with a heating sheet and an air bag, and the headgear is internally provided with an ultrasonic generator; a user wears the head sleeve on the head of the waistcoat; an ultrasonic generator emits audio stimulation of specific brain wave guiding components, brain activities of a treated individual are guided to a deep relaxation or semi-sleep state mainly including alpha waves (8-12 Hz) and theta waves (4-8 Hz), and dominant defense and high-alertness reactions are reduced; by means of linguistic or audio meditation guidance, establishment of a safety scene is assisted, excessive activation of sympathetic nerves is reduced, and vagus nerve dominance is improved; the obvious improvement in the aspects of sleep, emotion, self value feeling and the like can be observed in 7-14 days; the original energy feeling is reprogrammed through a multi-layer path, the deep subconsciousness is changed, and the recurrence probability is low.

The invention provides an intelligent brain wave training earphone based on alpha waves, and relates to the technical field of brain wave training earphones, the intelligent brain wave training earphone comprises a first mounting plate, a second mounting plate, an adjusting assembly, a sound source, a central processing module, a brain wave identification module, a music processing module and a frequency modulation module; the outer ends of the first mounting plate and the second mounting plate are connected with a mounting frame in a clamped mode, and the outer ends of the first mounting plate and the second mounting plate are connected with a protective cover in a clamped mode and located outside the mounting frame. Therefore, the dry electrode sensor is not fixed at one position to collect the alpha wave, so that the dry electrode sensor can be adjusted at different positions of the human brain under the actual use condition of a user, the optimal alpha wave can be conveniently collected, the optimal experience feeling of the user can be ensured, and the user experience is improved. And secondly, Aer wave sending is realized by adopting a mode of collecting and externally arranging a sound source, so that the functions are more comprehensive.

The invention relates to the technical field of intelligent medical equipment, and discloses a control method, device and equipment of sleep aiding equipment and a storage medium, the sleep aiding equipment comprises an electroencephalogram collection unit and an execution unit, and the control method of the sleep aiding equipment comprises the steps that electroencephalogram signals collected by the electroencephalogram collection unit are obtained; analyzing brain waves of different wave bands in the electroencephalogram signals, wherein the different wave bands comprise at least two of a delta wave band, a theta wave band, an alpha wave band, a beta wave band and a gamma wave band; adjusting the parameters of the execution unit based on the analysis results of the brain waves of different wave bands; and controlling the execution unit to work according to the adjusted parameters. The control method of the sleep-aiding equipment provided by the invention has personalized adjustment capability, can accurately adapt to individual differences, and improves the sleep-aiding effect and sleep-aiding efficiency.

The invention discloses a meditation disk audio frequency response monitoring method based on a Clarii graph, and relates to the technical field of information, a piezoelectric actuator is used for generating a multi-frequency superposition sound wave signal, fluorescent powder on the surface of a vibration disk is driven to form a composite Clarii graph, the frequency value and the amplitude value of each frequency component are identified in real time, and the node line distribution pattern is analyzed. When it is detected that the phase conflict intensity of the node line overlapping area exceeds the standing wave stability critical value, the node ambiguity is quantized by extracting the gray gradient change range of the node line boundary, and the boundary undefinition is evaluated according to the boundary point position distance. In combination with user alpha-wave frequency band heart rate stability data, the driving frequency configuration of the ceramic vibration disc is dynamically adjusted, meanwhile, the LED light projection area and the color temperature switching rate are optimized, and clear presentation and intelligent control of the Clariy graph in the multi-frequency environment are achieved.

The application discloses a kind of intelligent feedback regulation and control method for man-machine interactive experience optimization, the present application relates to interactive regulation and control technical field, lack of the receiving and strategy adjustment mechanism of user feedback is solved, cannot form the technical problem of complete interactive closed loop, the present application is through the weighted calculation of theta wave power ratio and alpha wave invasion rate, through a large number of driving data statistics, establish personalized fatigue index judgment interval, solve the problem of different user eeg baseline difference, compared with single frequency band analysis, fatigue monitoring accuracy can be improved, cross validation is carried out in combination with other modal fatigue state information, avoid single signal misjudgment, secondly, real-time analysis road condition, automatically generate the optimization route of shorter time, and through multimodal feedback, guide user decision, support user to the "acceptance / rejection" feedback of route optimization, and dynamically adjust navigation strategy accordingly.

The invention relates to the technical field of brain-computer interfaces, and discloses a power-assisted exoskeleton robot based on a brain-computer interface and a control method thereof.The method comprises the steps that S1, electroencephalogram signals of a user are collected, and a mu rhythm power value, a beta rhythm power value and pre-starting signal duration in a motion pre-starting time window are extracted; s2, calculating a joint angle target value of the exoskeleton robot according to the variation amplitude of the mu rhythm power value and the beta rhythm power value and the duration of the pre-starting signal; s3, according to the joint angle target value, controlling each joint to output assistance torque and calculating the mismatch degree of each joint movement, when the mismatch degree exceeds a preset mismatch degree threshold value, adjusting the assistance strength and triggering an alarm, and when it is detected that alpha wave power in the electroencephalogram signal is suddenly reduced, cutting off assistance driving and locking the exoskeleton posture. The motion intention can be recognized 100-300 ms before the user actually initiates the motion, and the synchronism problem that in the prior art, the power-assisted motion lags behind the motion of the user is solved.

The invention discloses a multi-scale convolution fusion neural network for motor imagery electroencephalogram signal classification, which comprises a multi-scale convolution attention module (MSCAM), a multi-stage residual time convolution module (MRITCN) and a cross attention fusion module (CAFM), which are used for extracting effective information (alpha wave and beta wave) in motor imagery (MI) signals; the MSCAM is used for extracting spatio-temporal characteristics under different scales, adding an attention mechanism to enhance effective information of the network, and inhibiting redundant information; the MRITCN is used for preliminarily extracting a time dependency relationship; and the CAFM further extracts a time dependency relationship, and meanwhile, the spatial-temporal features of different scales are fused and then classified. The neural network combines the MSCAM module, the MRITCN module and the CAFM module, so that the network can learn complex characteristics of EEG signals, the problem that spatial-temporal characteristics under different scales are difficult to fuse is solved, and the classification performance is remarkably improved.

The invention provides an information processing method of massage equipment and the massage equipment, and the method comprises the steps: collecting biological sign data of a target user using the massage equipment in real time through a plurality of sensors on the massage equipment, the biological sign data comprising actual sign values of multiple dimensions; determining the physical state of the target user according to the biological sign data of the target user; if the body state of the target user is a relaxed state, a target alpha-wave music track is determined from an alpha-wave music track library stored in a storage module of the massage equipment, the target alpha-wave music track is played, the alpha-wave music track library comprises a plurality of alpha-wave music tracks, and the alpha-wave music tracks are used for playing the target alpha-wave music track. The frequency of each alpha wave sound music is matched with the frequency of the alpha brain wave of the human brain. The user can be helped to stably enter the sleep state, and the experience of the user using the massage equipment is greatly improved.

The invention discloses a transaural vagus nerve stimulation method based on electroencephalogram feedback, and relates to the technical field of nerve regulation and control. According to the method, electroencephalogram signals of a specific brain region of a user are collected in real time through a flexible integrated electrode, time-frequency characteristics (such as delta, theta and alpha wave power) and nonlinear characteristics (such as entropy) are extracted after preprocessing, the current sleep stage or fatigue degree of the user is identified by using a CNN + LSTM model, taVNS stimulation parameters (frequency and intensity) are dynamically generated in combination with individual baseline electroencephalogram characteristics, and the user can be subjected to sleep state and fatigue state simulation. Stimulation is applied through an auricle stimulation electrode, and meanwhile, neural state changes are monitored in real time to iteratively optimize parameters. The problems that an existing taVNS target spot is not accurate, the stimulation mode is unknown, and the individual difference is large are solved through electroencephalogram feedback, safe, portable and personalized sleep and fatigue intervention is achieved, and remarkable clinical and application values are achieved.

The application discloses a brain wave-based attention monitoring and alarming method, device and equipment, constructs a purified brain wave library by collecting brain wave signals of operating personnel and carrying out artifact removal processing; extracts alpha wave and beta wave power spectrum, carries out chaos dynamics analysis on the alpha wave power spectrum to obtain a traction response degree, identifies attention anti-interference ability, and fuses to generate an attention state domain; carries out cluster analysis on the state domain to identify a high and low vigilance area, extracts peak brain wave features and a falling rate parameter, and predicts a critical attenuation moment; releases the peak brain wave features to generate a pre-charge activation sequence, carries out timing optimization and personalized intervention parameter matching; distributes multi-channel stimulation based on an early warning advance, decomposes the pre-charge activation sequence into a gradual stimulation string; extracts a stimulation parameter sequence to generate a brain wave synchronous stimulation signal through rhythm coding, converts the brain wave synchronous stimulation signal into an audible, visual and vibratory alarm signal, and realizes intelligent monitoring and timely alarming of the attention state of the operating personnel.

The application discloses a kind of neuroregulation systems based on alpha wave closed loop pink noise stimulation, including real-time sleep monitoring module, data quality detection module, alpha wave intensity calculation module, alpha wave rising edge detection module and pink noise control module.The application monitors the rising edge of alpha waveform by personalized morphological waveform monitoring algorithm, adjusts the Alpha wave activity of brain by closed loop pink noise stimulation in specific rising stage, can also adaptively adjust the sound intensity of pink noise, provides accurate sound stimulation, to improve the sleep experience of user, help user gradually lead into calm state from wakefulness, lay the foundation for subsequent deep sleep, compared with traditional sleep aid system, based on Alpha wave control technology can not only improve sleep speed, but also improve the overall quality of sleep, with more extensive applicability and practical significance.

The invention relates to a learning ability analysis system and method based on brain waves, and the method comprises the steps: S1, collecting five basic waves of brain wave frequency through a brain wave collection module, and S2, carrying out the wave band subdivision of an alpha wave, a beta wave and a gamma wave collected by the brain wave collection module through a frequency band subdivision module; s3, data visualization conversion is carried out on the brain wave maps of the theta wave and the delta wave collected in the step S1 and the brain wave maps of the low alpha wave, the high alpha wave, the low beta wave, the high beta wave, the low gamma wave and the high gamma wave obtained after wave band subdivision in the step S2, and the brain wave maps are converted into images with the brain wave energy of all frequency bands changing along with time intensity; s4, performing electroencephalogram analysis and scoring; through the image information, calculating the image information when the tested student answers a single question, and obtaining a single question brain wave proportion score; s5, carrying out transverse comparison calibration; the method has the advantages that more comprehensive learning ability evaluation can be provided through multi-band correlation analysis, and the user is helped to better know the learning ability condition of the user.

A method of treatment for improving cognitive or mental functions and neural activity in an individual comprising the step of subjecting a body of the individual to a combination of 10 Hz stimulation and 40 Hz stimulation, wherein the 10 Hz simulation and 40 Hz stimulation induce respective alpha and gamma waves in the individual.

The application discloses an Alpha brain wave music generation method, product, medium and equipment, and relates to the field of medical treatment and health care. The application can automatically extract Alpha wave band brain wave signals in multi-channel electroencephalogram signals and cut the same into brain wave signal segments; divides the brain wave signal segments of each channel into multiple brain wave signal segments; maps brain wave characteristic parameters of each brain wave signal segment into note characteristic parameters of corresponding candidate notes, so as to convert the brain wave signal segments into a candidate note sequence composed of multiple candidate notes; groups multiple channels of the electroencephalogram signals, and generates multiple note track candidate note sequences of different voices for each group; optimizes and adjusts the pitch of the candidate notes in the multiple note track candidate note sequences according to a preset mode, filters the pitch combination in the optimized candidate note sequence through the pitch combination in a large symbolic music data set, and then converts and synthesizes an audio file in a preset format, so that the music quality and audibility of the generated brain wave music are improved.

The invention discloses an attention monitoring and alarming method, device and equipment based on electroencephalogram. The method comprises the steps that electroencephalogram signals of an operator are collected, artifacts are removed, and a purified electroencephalogram library is constructed; extracting alpha wave and beta wave power spectrums, performing chaotic dynamics analysis on the alpha wave power spectrum to obtain traction responsivity, identifying attention anti-interference capability, and fusing to generate an attention state domain; performing clustering analysis on the state domain to identify high and low alertness regions, extracting peak electroencephalogram characteristics and descent rate parameters, and predicting a critical attenuation moment; releasing peak electroencephalogram characteristics to generate a pre-charging activation sequence, and performing time sequence optimization and personalized intervention parameter matching; multi-channel stimulation distribution is carried out based on the early warning advance, and the pre-charging activation sequence is decomposed into progressive stimulation strings; a stimulation parameter sequence is extracted for rhythm coding to generate an electroencephalogram synchronous stimulation signal, the electroencephalogram synchronous stimulation signal is converted into an acousto-optic vibration alarm signal, and intelligent monitoring and timely alarm of the attention state of an operator are achieved.

The invention provides a necklace, an ornament, a brain stimulation device and a method for using the necklace. The necklace can stimulate the brain and is suitable for long-term use. The necklace (100) comprises an alpha wave generating main body part (10) for generating alpha wave pulse current, a necklace wire (30) connected with the alpha wave generating main body part (10), and an electrode (20) arranged on a part of the necklace wire (30). The alpha wave generation body (10) includes a pulse current generation unit (41) that generates an alpha wave pulse current, and a filter (42) that is electrically connected to the pulse current generation unit (41) and attenuates a frequency higher than the alpha wave. The electrode (20) is fixed by an electrode fixing member (25) (21, 22)) connected to the necklace wire (30).