14 results about "Alpha wave" patented technology

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 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 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 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 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 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.