76 results about "Alpha wave" patented technology

The present invention provides methods and systems for patient-specific seizure onset detection. In one embodiment, at least one EEG waveform of the patient is recorded, and at least one epoch (sample) of the waveform is extracted. The waveform sample is decomposed into one or more subband signals via a wavelet decomposition of the waveform sample, and one or more feature vectors are computed based on the subband signals. A seizure onset can then be identified based on classification of the feature vectors to a seizure or a non-seizure class by comparing the feature vectors with a decision measure previously computed for that patient. The decision measure can be derived based on reference seizure and non-seizure EEG waveforms of the patient. In another aspect, similar methodology is employed for automatic detection of alpha waves. In other aspects, the invention provides diagnostic and imaging systems that incorporate the above seizure-onset and alpha-wave detection methodology.

The invention relates to a monitoring and rousing method for weary doze, comprising the following steps: 1) sticking three brain wave-collect electrodes on the collect point of head center, the reference point of earlobe and the earthing contact of forehead center; 2) collecting the potential difference of the collect point and the reference point to amplify, A/D convert and filter; 3) counting and selecting the power spectral value in the least error, and then sending the control signal to the alarm system when the ratio of the power spectral value between the Alpha wave with frequency in 8 to 13 Hz and the Theta wave with frequency in 4 to 7 Hz is between 1.582 to 2.08; 4) controlling the frequency output device to alarm by the alarm system. The weary doze-monitor and alarm device made by the invention comprises a brain wave-collect and treat system (1), a photocoupling module (2), a digital signal processing module (3), a information processing system (4), an alarm information processing system (5) and a frequency output device (6), wherein, the brain wave-collect and treat system (1) is connected by data lines. The invention and the device are high in accuracy rate and can avoid the accident during high risk operation due to weary doze furthest.

The invention pertains to the EEG signal treatment technology field, in particular to a method for automatic recognizing and eliminating artefact component from EEG and extracting fundamental rhythm therefrom. The method separates artefact and fundamental rhythm like alpha wave from EEG signal by use of independent component analysis method, then conducts non-linear index analysis of all independent components, automatically recognizes the artefact therein and EEG fundamental rhythm to be extracted by a set value, finally setting the recognized independent component to zero and sending back the residual signal to electrodes of the original signals, thus the EEG signal free of artefact is obtained. The invention combines three kinds of non-linear index and can effectively obtain a relative pure EEG signal without artificial interference.

The invention discloses a fatigue brain electrical characteristic research method based on different experiment difficulties. Mental fatigue is a gradual and cumulative phenomenon, and generally shows weakening of physiological activities of the human body. Fatigue brain electricity under different states is induced by setting two groups of Stroop experiments with different difficulties, and characteristic analysis is carried out on brain electrical signals in a waking state and in a fatigue state in the two groups of experiments by using wavelet packet decomposition and a sample entropy algorithm. Experimental results show that the relative energy of alpha waves and theta waves is increased from the waking state to the fatigue state, the relative energy of beta waves is obviously reduced (P<0.05), the ratio (P<0.05) of parameters alpha/beta and (alpha+theta)/beta is gradually increased along with deepening of fatigue, and sample entropy values of encephalic regions are in decreasing tendency; compared with the experiment group with high difficulty, parameter changes in low-difficulty tasks are more obvious, and therefore the parameters alpha/beta and (alpha+theta)/beta can serve as potential indicators for measuring mental fatigue and meanwhile can verify that proper increase of experiment difficulty can prevent generation of metal fatigue to a certain extent.

The invention discloses a vision induced motion sickness detection method based on electrocardiogram signal gravity center frequency. Two pairs of electrocardiogram signal sensors are arranged in left and right frontal lobe zones and left and right temporal lobe zones of a user, four channel electrocardiogram signals of the user at a normal stage and a detection stage are respectively acquired, different signals of the electrocardiogram signals of the left and right frontal lobe zones are calculated, alpha wave signals are extracted from the electrocardiogram signals, then the gravity center frequency sequences of the alpha wave signals are calculated according to preset time window parameters, the gravity center frequency average values of the alpha wave signals of the right frontal lobe zone and the right temporal lobe zone at the normal stage and at the detection stage and the gravity center frequency standard deviations of the alpha wave signals of the different signals of the electrocardiogram signals of the right frontal lobe zone, the left temporal lobe zone, the right temporal lobe zone and the left frontal lobe zone are calculated, the six pairs of signals serve as detection signals, it is determined that a vision induced motion sickness phenomenon occurs when the difference of the signals detected at the normal stage and the detection stage is larger, and accordingly vision induced motion sickness detection is achieved.

The present invention discloses method and device for developing brain's potential, and belongs to the field of brain's potential developing technology. The method includes stimulating the hearing sensation, visual sensation, smell sensation and touch sensation of bright brain with music, picture, smell and massage; increasing the nerve impulse information amount transferred between two brain hemispheres; regulating the functions of the two brain hemispheres; stimulating the release of polypeptides, acetylcholine and other neurotransmitters; and regulating the brain wave to the alpha wave range of 8-13 Hz for maintaining in optimal work state. The present invention has the features of comprehensive performance and obvious effect. The present invention makes it possible for people to breed up excellent learning habit.

The invention relates to the field of fatigue detection, in particular to a controller fatigue detection method and system based on a BP neural network. The controller fatigue detection method based on the BP neural network includes the steps that brain waves of a controller are collected, and a slow alpha wave power percentage, a power ratio of alpha waves to beta waves and a power ratio of theta waves to slow alpha waves are obtained according to the brain waves; the slow alpha wave power percentage, the power ratio of the alpha waves to the beta waves and the power ratio of the theta waves to the slow alpha waves are input into a pre-trained BP neural network model, and a PERCLOS value simulation result is obtained; if the PERCLOS value simulation result is larger than a fatigue threshold value, the controller is judged to be in a fatigue state. According to the controller fatigue detection method and system based on the BP neural network, based on the BP neural network, the fatigue degree of the human body is detected in real time by detecting the brain waves, which makes the real-time fatigue detection simpler and reduces the detection cost.

The invention discloses a method, apparatus and device for obtaining pain feeling evaluation indexes. The method comprises the steps of acquiring brain wave signals of the cerebral cortex of a detected person and pre-processing the brain wave signals; extracting time domain brain wave signals from the preprocessed brain wave signals, obtaining frequency domain information from the time domain brain wave signals and analyzing and calculating energy values of the frequency domain information to obtain energy values of Alpha waves and using the energy values of the Alpha waves as the pain feeling evaluation indexes to be output. The apparatus comprises an acquiring module and a processing module. The device comprises the device. The method, apparatus and device for obtaining the pain feeling evaluation indexes performs subjective and objective dimensional comprehensive evaluation by providing continuously changing pain intensity perception, objective evaluation results can be obtained, and application and popularization are facilitated.

The invention discloses a hypnosis music playing method and system based on a television and the television. The hypnosis music playing method based on the television comprises the following steps that an audio file is stored in the television in advance, wherein the frequency spectrum and the frequency of the audio file are identical to those of the alpha wave of brain waves; an audio file is stored in the television in advance, wherein the frequency spectrum and the frequency of the audio file are identical to those of the delta wave of the brain waves; when the hypnosis function is needed, the audio file with the frequency spectrum identical to that of the alpha wave of the brain waves and the frequency identical to that of the alpha wave of the brain waves is controlled to be played by the television, so that the alpha wave is generated, after a preset period, the audio file with the frequency spectrum identical to that of the delta wave of the brain waves and the frequency being identical to that of the delta wave of the brain waves is controlled to the played by the television, so that the delta wave is generated, physical resonance is caused by sound wave vibration, the body and the mind of a user are made to be relaxed under the intervention of the alpha wave, the user is guided to enter the asleep state by the delta wave, and the excited mood of the user is rapidly relieved. Due to the fact that the hypnosis music playing method is achieved in the television through software, cost is low and the hypnosis music playing method is easy to achieve.

The invention discloses a sleep monitoring and improving apparatus and method. The sleep monitoring and improving apparatus comprises a gyroscope, a preprocessing unit, a processing unit, and an alpha wave transmission unit, wherein the gyroscope is used for detecting a vibration state of the apparatus and outputting movement information; the preprocessing unit is connected with the gyroscope and used for receiving angle information, performing preprocessing and converting the angle information into a digital signal; the processing unit is connected with the preprocessing unit and used for receiving the digital signal, performing FFT conversion on the digital signal, and performing characteristic extraction on a conversion result to obtain heart rate and respiratory rate which reflect the sleep state of a user; and the alpha wave transmission unit is connected with the processing unit and used for transmitting alpha wave which promotes sleep under the control of the processing unit. By adoption of to the apparatus, whether a to-be-detected user exists or not is sensed by the gyroscope in a human body isolation way; the apparus vibration caused by respiration and heart beat of the user in sleep is detected; the sleep state of the user is obtained according to the vibration information; the alpha wave which promotes sleep is transmitted; and therefore, the sleep quality of the user can be improved while the sleep state of the user is monitored.

For measuring a brain local activity, a predetermined frequency bandwidth wider than a frequency bandwidth of alpha waves of scalp potentials is divided into a predetermined number of frequency banks each having a fixed frequency width, data of each divided frequency bank is divided into segments of a predetermined duration on a time axis, a Z-score of the subject is determined from a first mean value of normalized power variances determined for the segments and a second mean value of normalized power variances predetermined in the same manner as the first mean value for a predetermined normal person group and a standard deviation of the normalized power variances in the group, and a mean value of the Z-scores determined over all of the frequency banks is mapped on a brain surface for each sensor, whereby a template of a specified brain disorder and likelihood of a subject to a specified brain disorder are calculated.

The invention discloses a wearable electroencephalogram relaxation training instrument. A virtual screen is generated at a position which is two meters away from a human eye by a portable projector, and video simulating signals are provided for relaxation training. Audio signals are outputted by an audio interface, and audio simulating signals such as background music and training instructions are provided for relaxation training. Electroencephalogram electrodes are placed at the forehead of a brain by the aid of a rotary adjusting rod and a telescopic adjusting rod. The electroencephalogram electrodes transmit acquired brain wave signals into an embedded processing unit, the embedded processing unit performs spectral analysis for the brain wave signals, alpha wave components reflecting the relaxing state of the brain are extracted, neural feedback is performed for relaxation training, and data are transmitted to a man-machine interaction device through a wireless data interface. Electroencephalograms are used as feedback signals of relaxation training, and relaxation training efficiency and reliability are greatly improved. By the aid of a wearable computer, many unnecessary interference factors are eliminated, and a training environment is improved.

The multiple perception music therapeutic instrument includes therapeutic music, ear phone volume regulator, music magnetic vibration sensing device and music electric stimulator connected together. The cerebral electric signal analysis includes cerebral electric signal collection, signal pre-treatment and analysis, manual regulation to display option, display cerebral electric signal, storing cerebral electric data, judging whether to print, manual selecting data, displaying selected signal, analyzing selected data, displaying selected spectrogram and printing cerebral electric signal and cerebral electric spectrum. The present invention adopts music electronic transducer to produce body's vibration effect and body sense effect as well as sync magnetic field, and stereo ear phone to stimulate ears for producing resonant vibration and alpha wave to cooperate the right and left brains, so as to induce subconsciousness and promote release of beneficial hormone.

The invention discloses an intelligent eyesight protection instrument with brain wave detection and stimulation functions. The intelligent eyesight protection instrument comprises a sensor, a controller, an alarming device and a stimulating device, the sensor collects and measures brain waves, the controller receives corresponding data acquired by the sensor, and compares a processed measuring result with a set value to determine whether the processed measuring result meets learning requirements or not, if the brain waves of a user do not belongs to an alpha wave type, the stimulating device is started, and the brain waves are enabled to enter an alpha wave state through external stimulation in various forms. The intelligent eyesight protection instrument has the advantages that brain wave types are detected by an electrode pasted on a brain, and judged whether belonging to the types suitable for learning or not, if not, the brain wave types can be changed into the types suitable for learning by the stimulating device through playing background music.

The invention discloses a canonical correlation analysis-based electroencephalogram alpha wave detection and identification method, and solves the problem in quick and efficient detection and identification of an electroencephalogram alpha wave signal. The method comprises the steps of inputting an electroencephalogram signal; preprocessing the electroencephalogram signal to obtain training and test data; selecting different frequency sets, and establishing reference signals corresponding to different frequencies; calculating correlation coefficients of the training data and the different frequency reference signals through canonical correlation analysis, thereby forming a correlation coefficient set; performing feature frequency selection to obtain a feature frequency set; selecting the correlation coefficients corresponding to the feature frequency set from the correlation coefficient set to form a training feature set; training a classifier by using the training feature set; and calculating a test data feature set; and performing classified identification by using the classifier, thereby finishing electroencephalogram alpha wave detection and identification. Through the featurefrequency selection, quick detection of the electroencephalogram alpha wave signal is realized; and the method is high in detection speed, high in accuracy and stable in work, and is used for signal detection in a brain computer interface system of the electroencephalogram alpha wave signal.

The invention discloses an electroencephalogram recognition method based on machine learning, and the method comprises the steps: building sample data comprising a plurality of electroencephalograms,extracting the alpha-wave rhythm of each electroencephalogram in the sample data, training a deep learning framework, and obtaining an initial model; and inputting the alpha wave rhythm of the targetelectroencephalogram signal into the initial model for retraining to obtain an electroencephalogram signal deep learning model, extracting feature parameters of each electroencephalogram signal in thesample data, and dividing the data set into a first reference interval, a second reference interval, a third reference interval and a fourth reference interval. The method comprises the following steps: acquiring a to-be-detected electroencephalogram signal, extracting characteristic parameters of the to-be-detected electroencephalogram signal to obtain to-be-detected parameters, identifying a reference interval in which the to-be-detected parameters are located, and detecting a user state represented by the corresponding to-be-detected electroencephalogram signal according to the reference interval in which the to-be-detected parameters are located. And the identified reference interval has relatively high accuracy thus, the accuracy of the detected user state is improved.

The invention belongs to the technical field of information processing, and discloses an intelligent sleep and fatigue state information monitoring control system and method and a monitor. An electroencephalogram module is used for collecting electroencephalogram signals; fourier transform is carried out on the collected signals, and energy proportions of alpha waves, beta waves, theta waves and delta waves are analyzed to judge the sleep state or fatigue degree of the user; a white noise emitting module is used for emitting white noise to improve the sleep state when the user is in the mild sleep state; a heart rate monitoring module measures pulse waves through a photoplethysmography method and detects the real-time heart rate. The sleep quality or fatigue state of the user can be obtained through analysis by monitoring the brain wave data of the user, and meanwhile the real-time heart rate of the user is monitored. When it is found that the user is in a light sleep state, the systemautomatically plays white noise to improve sleep quality of the user; and when the user enters deep sleep, the white noise stops. When the user is found to be in a deep fatigue state, alarm sound canbe emitted to remind the user to have a rest timely.

A support device includes: a data acquisition unit which acquires brain activity data of a user; a stimulation unit which gives an electromagnetic stimulus corresponding to the brain activity data, to a head of the user; and a brain wave storage unit which stores a change in the brain activity data. The support device is configured to give an electromagnetic stimulus which causes alpha waves of the brain activity data to change, when acquiring the brain activity data.

The present invention relates to a bed to adjust a slope of the bed according to brain waves and a method thereby, to adjust the slope to generate the maximum alpha waves of brain waves which are generated by the change of the blood amount to be provided in a bed user's brain. For this, there is provided a bed to adjust the slope of the bed according to brain waves that an ankles fixing device 14 is mounted at one side of the bed 12 and which is capable of rotating within predetermined angles around a rotation central hinge 18 which is installed at one side of the lower surface of the bed, wherein the bed comprises brain waves detection means 50 which is mounted at another side of the bed 12, and which user is able to put on, separation means for separating alpha waves and beta waves having predetermined frequency range from output signals of the brain waves detection means 50, and adjustment means for adjusting the inclination of the bed 12 so that the maximum alpha waves can be detected based on the alpha waves and the beta waves.