299 results about "Electro encephalogram" patented technology

A system (10) analyzes signals representative of a subject's brain activity in a signal processor (12) for information indicating the subject's current activity state and for predicting a change in the activity state. One preferred embodiment uses a combination of nonlinear filtering methods to perform real-time analysis of the electro-encephalogram (EEG) or electro-corticogram (ECoG) signals from a subject patient for information indicative of or predictive of a seizure, and to complete the needed analysis at least before clinical seizure onset. The preferred system then performs an output task for prevention or abatement of the seizure, or for recording pertinent data.

A system evaluates stimulus materials such as videos, imagery, web pages, text, etc., in order to determine resonance and priming levels for various products and services at different temporal and spatial locations including advertisement breaks in the stimulus materials. The stimulus materials are tagged with resonance and priming level information to allow intelligent selection of suitable advertisement content for insertion at various locations in the stimulus materials. Response data such as survey data and/or neuro-response data including Event Related Potential (ERP), Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding data may be used to determine resonance and priming levels.

Central nervous system, autonomic nervous system, and effector data is measured and analyzed to determine the effectiveness of marketing and entertainment stimuli. A data collection mechanism including multiple modalities such as Electroencephalography (EEG), Electrooculography (EOG), Galvanic Skin Response (GSR), etc., collects response data from subjects exposed to marketing and entertainment stimuli. A data cleanser mechanism filters the response data. The response data is enhanced using intra-modality response synthesis and/or a cross-modality response synthesis.

A system determines neuro-response stimulus and stimulus attribute resonance. Stimulus material and stimulus material attributes such as communication, concept, experience, message, images, audio, pricing, and packaging are evaluated using neuro-response data collected with mechanisms such as Event Related Potential (ERP), Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding. Neuro-response data is analyzed to determine stimulus and stimulus attribute resonance.

A system analyzes neuro-response measurements including regional electroencephalography (EEG) measurements from subjects exposed to stimulus materials to determine locations in stimulus materials eliciting controlled attention and automatic attention. Additional stimulus materials are inserted into locations having salient attention attributes. In some examples, a challenging task is used to direct controlled attention onto a location and additional stimulus material is subtly presented in the location to benefit from automatic attention and salient attention measurements.

The invention relates to a sleep therapy apparatus based on electroencephalogram biofeedback, also called as a good sleep generator. The apparatus comprises an electroencephalogram electrode, an electroencephalogram signal amplifying and filtering circuit, an A/D (analog/digital) sampler, a measuring and controlling system based on a single chip microcomputer, a D/A (digital/analog) output circuit, a stimulating signal drive circuit, a system power supply and the like, wherein an electroencephalogram signal, the electroencephalogram electrode, the amplifying and filtering circuit, the A/D sampler, the measuring and controlling system, the D/A output circuit, the drive circuit and a stimulating electrode are orderly connected, and an analytical algorithm module and a database are connected with the measuring and controlling system. According to invention, the single chip microcomputer system is used as a measuring and controlling platform, and collection, amplification and analysis processing of the electroencephalogram signal are realized in combination with the electroencephalogram signal amplifying and filtering circuit and a stimulating wave signal database which are arranged at the front end and a stimulating signal generating and drive circuit arranged at the rear end; corresponding therapy mode, stimulating wave group and stimulating intensity are selected according to the characteristics of the brain waves; and at last a therapy scheme is input by a post-stage stimulating circuit so as to realize biofeedback type sleep therapy.

The invention provides a parallel convolutional neural network motor imagery electroencephalogram classification method based on spatial-temporal feature fusion. According to the invention, motion imagery electroencephalogram signals are used as research objects, and a novel deep network model-parallel convolutional neural network method is provided for extracting spatial-temporal features of themotion imagery electroencephalogram signals. Different from a traditional electroencephalogram classification algorithm which often discards electroencephalogram spatial feature information, Theta waves (4-8 Hz), alpha waves (8-12 Hz) and beta waves (12-36 Hz) are extracted through fast Fourier transform to generate a 2D electroencephalogram feature map; training of the electroencephalogram feature map is conducted based on a multi-convolutional neural network so as to extract spatial features; in addition, a time convolution neural network is used for parallel training so as to extract time-order features; and finally, the spatial features and the time-order features are fused and classified based on Softmax. Experimental results show that the parallel convolutional neural network has good recognition precision and is superior to other latest classification algorithms.

A method and system are disclosed for assessing the capability of a first person to operate a machine, such as an aircraft or other transport vehicle. The method includes the steps of:

• • (a) Sensing the value of at least one, and preferably two or more information parameters for assessing the fitness of the person operating the machine. The parameters include one or a combination of electroencephalogram information, electrocardiogram information, heart rate information, respiratory rate information, eye motion information, eyelid position information, eyelid motion information, expired gas mixture information, blood oxygen content information, blood oxygen saturation information and blood pressure information for the person operating the machine, and/or information about operation of the machine as well as information about actions taken by the person operating the machine.
  • (b) Determining whether at least one value of one information parameter is/are not within the range of values deemed to be acceptable for a capable person.
  • (c) Indicating when the at least one information parameter is/are not within the range of acceptable values, thereby indicating the person's decreased fitness and ability to operate the machine.

The invention relates to a method of measuring an electroencephalogram signal in response to an auditory stimulus applied to a subject or patient (99), wherein the method uses at the most a first electrode (EL1), a second electrode (EL2), a third electrode (EL3). The method comprises: i) positioning the first electrode (EL1) on the head (10) of the patient or subject (99) in a first region (R1) extending substantially between a right ear and a right eye; ii) positioning the second electrode (EL2) on the head (10) of the subject or patient (99) in a second region (R2) extending substantially between a left ear and a left eye; iii) positioning the third electrode (EL3) on the head (10) of the subject or patient (99) in a third region (Cz), the third region (Cz) being different from the first region (R1) and the second region (R2); iv) applying the auditory stimulus to at least one ear of the subject or patient (99); v) measuring a first potential difference between the first electrode (EL1) and the third electrode (EL3) in response to the auditory stimulus to obtain a first electroencephalographic signal, and measuring a second potential difference between the second electrode (EL2) and the third electrode (EL3) in response to the auditory stimulus to obtain a second electroencephalographic The invention further relates to a method of hearing screening a subject or patient, comprising such method. The invention also relates to a measurement system (such as a hearing screener) for carrying out such method and to an apparatus (head-set) for use in such system. The invention provides for an improved method of measuring an electroencephalogram signal in response to an auditory stimulus applied to a subject or patient, in particular where two channels are measured using only three electrodes.

The invention provides an epileptic state recognition method based on transfer learning and cavity convolution. The epileptic state recognition method comprises the following steps: S1, extracting a plurality of wavelet packet coefficient groups of each group of original epileptic electroencephalogram signals under a specific frequency as a feature group; s2, removing significantly related waveletpacket coefficient groups in the feature groups to realize dimensionality reduction of the feature groups, wherein each wavelet packet coefficient of the feature group after dimension reduction is afeature value; s3, standardizing all characteristic values extracted from the plurality of groups of original epilepsy electroencephalogram signals; s4, taking all the characteristic values subjectedto standardization processing as a test data set, and taking characteristics in an existing epilepsy electroencephalogram signal characteristic database as a training data set; achieving cross-domainknowledge migration through an improved CMJAE migration learning method, taking a two-dimensional hole convolutional neural network as a classifier, and iteratively acquiring a classification result of the test data set; and S5, verifying the classification accuracy by adopting a ten-fold cross validation method.

The invention relates to an automatic identification method of electroencephalogram artifact and an automatic identification electroencephalograph using the same. The method comprises the following steps that: electroencephalosignals are collected by an electrode and are analyzed to generate the required electroencephalogram detection data; video signals related to detected persons and/or environments are collected by a camera device, and are analyzed to judge a time period in which a video situation easily causing the artifact occurs in the detected persons and/or environments as an artifactsuspicious interval; and then the artifact suspicious interval is identified, and an electroencephalogram or tendency chart containing artifact suspicious interval identification is displayed. A computer host or an operating server in the video electroencephalograph can be used for executing the analysis and processing task of the data collected by the electrode and the camera device in the method. In the automatic identification method, the artifact suspicious interval can be visually displayed in the electroencephalogram or tendency chart, the difficulty of human analysis and artifact signal elimination is reduced, and the accuracy and efficiency of electroencephalograph analysis are improved.

Non-intrusive assessment of fatigue in drivers using eye tracking. In a simulated driving experiment, vigilance was assessed by power spectral analysis of multichannel electroencephalogram (EEG) signals, recorded simultaneously, and binary labels of alert and drowsy (baseline) were generated for each epoch of the eye tracking data. A classifier and a non-linear support vector machine were employed for vigilance assessment. Evaluation results revealed a high accuracy of 88% for the RF classifier, which significantly outperformed the SVM with 81% accuracy (p<0.001). In a simulated driving experiment, the simultaneously recorded multichannel electroencephalogram (EEG) signals were used as the baseline. A random forest (RF) and a non-linear support vector machine (SVM) were employed for binary classification of the state of vigilance. Different lengths of eye tracking epoch were selected for feature extraction, and the performance of each classifier was investigated for every epoch length. Results revealed a high accuracy for the RF classifier in the range of 88.37%-91.18% across all epoch lengths, outperforming the SVM with 77.12%-82.62% accuracy. A feature analysis approach was presented and top eye tracking features for drowsiness detection were identified. A high correspondence was identified between the extracted eye tracking features and EEG as a physiological measure of vigilance and verified the potential of these features along with a proper classification technique, such as the RF, for non-intrusive long-term assessment of drowsiness in drivers.

System and method for processing, non-concurrently collected, electroencephalogram (EEG) data and resting station functional magnetic resonance imaging (rsfMRI) data, non-invasively, to create a patient-specific three-dimensional (3D) mapping of the patient's functional brain network. The mapping can be used to more precisely identify candidates of resective neurosurgery and to help create a targeted surgical plan for those patients. The methodology automatically maps the patient's unique brain network using non-concurrent EEG and resting state functional MRI (rsfMRI). Generally, the current invention merges non-concurrent EEG data and rsfMRI data to map the patient's epilepsy/seizure network.

The invention discloses a multi-channel electroencephalogram automatic epilepsy detection device based on one-dimensional CNN-LSTM (convolutional neural network-long short term memory). The multi-channel electroencephalogram automatic epilepsy detection device based on the one-dimensional CNN-LSTM comprises a computer. The computer is programmed to perform the following step: acquiring collected data, wherein the collected data is collected by placing data acquisition electrodes according to a 10-20 system electrode method meeting the international standard. The multi-channel electroencephalogram automatic epilepsy detection device has the advantages that different from conventional epileptic seizure detection, the device does not need manual feature design for classification; instead, multi-channel original signals are input into a training network directly, features of the signals are automatically learned through one-dimensional CNN and LSTM neural networks, and finally, classification is performed; due to the multi-channel signals, the effect is better than that of a method only using single-channel signals, so that the stability and universality are achieved; the device has agood effect in practical clinical data in addition to excellent performance in a database.

The present invention provides a light-weight wearable sensor that can capture electroencephalogram (EEG or brain signals), electromyography (EMG or muscle signal), and electrooculography (EOG or eye movement signal) using a pair of modified off-the-shelf earplugs. The present invention further provides a supervised non-negative matrix factorization learning algorithm to analyze and extract these signals from the mixed signal collected by the sensor. The present invention further provides an autonomous and whole-night sleep staging system utilizing the sensor's outputs.

The invention provides an electroencephalogram electrode. The electroencephalogram electrode comprises an electrode sheet, a gauze, an O-shaped sealing ring, a liquid storing groove passage, cotton, a liquid storing groove, a liquid storing groove cover, an electrode shell, a pure silver connector, a silver wire lead, a lead passage, a fixing frame and a positioning plate, wherein the electrode sheet is embedded into the bottom of the electrode shell and is welded with the pure silver connector through the lead passage by the silver wire lead; the liquid storing groove provided with the liquid storing groove passage is accommodated in the electrode shell; a groove is formed in the bottom of the electrode shell and is matched with the O-shaped sealing ring; the cotton is arranged in the liquid storing groove and is extended to the surface of the electrode sheet; the gauze is covered on the cotton on the surface of the electrode sheet; and the electrode shell is connected with the fixing frame and the positioning plate. The electroencephalogram electrode is reasonable in structural design, low and stable in electrode impedance, stable in impedance of electrode surface and skin, low in noise and good in effect; base line drift is reduced; and conducting liquid is not required to be implemented in a test process, so detection speed is increased. Because the cover is added to seal, so the conducting liquid placed for a long time is prevented from being volatilized.