316 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 neuro-informatics repository system is provided to allow efficient generation, management, and access to central nervous system, autonomic nervous system, effector data, and behavioral data obtained from subjects exposed to stimulus material. Data collected using multiple modalities such as Electroencephalography (EEG), Electrooculography (EOG), Galvanic Skin Response (GSR), Event Related Potential (ERP), surveys, etc., is stored using a variety of data models to allow efficient querying, report generation, analysis and / or visualization.

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.

A system evaluates and selects temporal and spatial locations for introduction of stimulus material. Video streams, physical locations, print advertisements, store shelves, images, commercials, etc. are analyzed to identify locations for introducing stimulus material, such as messages, brand images, products, media, marketing and / or other sales materials. The system analyzes neuro-response measurements from subjects exposed to stimulus material in different temporal and spatial locations. Examples of neuro-response measurements include Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding measurements. Neuro-response measurements are analyzed to select temporal and spatial locations for introduction of stimulus material.

A system assesses consumer experience by evaluating neuro-response measurements for a consumer exposed to products, services, offerings, and stimulus. Examples of neuro-response measurements include Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding measurements. Components of a consumer experience are analyzed to assess neuro-response measurements specific to each component. In many instances, neuro-response data is combined with other data and analyzed to determine total consumer experience.

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.

<heading lvl="0">Abstract of Disclosure< / heading> Apparatus utilizing electrical activity of the brain to control a series of low-stimuli educational exercises displayed on a computer monitor to increase the following educational components: time on-task, visual tracking, short-term memory, visual discriminatory processing, auditory discriminatory processing, and focus. The exercises are governed by real-time analysis of the focus and processing states of the user. Specific relative exercise performance data are collected and recorded from the use of each of the educational components to demonstrate improvement over time of the user in each of the sited educational components.

A system presents stimulus materials such as products, product packages, displays, services, offerings, etc., in virtual reality environments such as market aisles, store shelves, showroom floors, etc. Sensory experiences output to the user via the virtual reality environment elicit user interactivity. User activity and responses are used to modify marketing materials and / or virtual reality environments. Neuro-response data including electroencephalography (EEG) data is collected from users in order to evaluate the effectiveness of marketing materials in virtual reality environments. In particular examples, neuro-response data is used to modify marketing materials and virtual reality environments presented to the user.

A system performs stimulus targeting using neuro-physiological and neuro-behavioral data. Subjects are exposed to stimulus material such as marketing and entertainment materials and data is collected using mechanisms such as Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding. Neuro-physiological and neuro-behavioral data collected is analyzed to select targeted stimulus materials. The targeted stimulus materials are provided to particular subjects for a variety of purposes.

A system identifies the location of a user and presents location aware stimulus material to the user. Discounts, promotions, and advertising can be delivered to the user on a wireless device based on location and path information. Stimulus material on in store monitors, billboards, and displays are modified based on information about individuals near the monitors and displays. In particular embodiments, neuro-response data is collected using a portable electroencephalography (EEG) headset while a user is exposed to stimulus materials to allow the effectiveness of the stimulus material to be determined. Stimulus materials presented can also be evaluated for neuro-response effectiveness prior to presentation to users in a location aware and personalized manner.

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.

Techniques are disclosed for authentication and identification of a user by use of an electroencephalographic (EEG) signal. For example, a method for authenticating a user includes the following steps. At least one electroencephalographic response is obtained from a user in accordance with perceptory stimuli presented to the user. The user is authenticated based on the obtained electroencephalographic response. The authenticating step may be based on detection of an event-related potential in the obtained electroencephalographic response. The event-related potential may be a P300 event-related potential. The method may also include the step of enrolling the user prior to authenticating the user. The enrolling step may include a supervised enrollment procedure or an unsupervised enrollment procedure.

The invention provides a method for determining fatigue state according to electroencephalogram (EEG) which adopts a plurality of electroencephalographs and connecting electrodes for realizing the real time acquisition of electroencephalogram. The method comprises the following steps: running interface programs of a PC and the electroencephalographs; realizing the synchronous acquisition of data by using a VC++ to compile visual interface program of the electroencephalographs under the Windows platform, and displaying EEG waveforms acquired in real-time; pre-processing the acquired data; carrying out the low-pass filtering at 0Hz to 30Hz to the data by an FIR (Finite Impulse Response) filter, so as to eliminate the power frequency noise and external interference; decomposing the filtered EEG waveforms by the blind-source separation method, so as to acquire each component of the mixed signal comprising electro-oculogram (EOG) and left and right brain EEGs; carrying out the fast Fourier transform (FFT) on the left and right brain EEGs, and converting the time-domain signals to the frequency-domain signals; working out the energy of alpha, beta, theta and delta waves in the EEGs and classifying the BP (back propagation) neural network of the multi-layer perceptron. The invention has the characteristics of directness and rapidness.

The invention provides a method, intelligent device and system for preventing fatigue driving. According to the method, by means of comprehensive analysis of collected human physiological information such as the five-organ state and the electroencephalogram of a driver, collected external driving environment information of the driver, and historical fatigue driving records of the driver, the comprehensive fatigue driving degree of the driver is obtained, when the equipment and the system find that the driver is in a fatigue state, an alarm is given locally, and an alarm is also given to family members of the driver or a law executor. The precision of obtaining the fatigue state of the driver is higher, after fatigue driving, the alarm is given locally, the alarm is also given remotely, and alarm information is pushed to the family members of the driver or the law executor to require the driver to stop a car and have a rest; big data analysis can be carried out to effectively predict possibility of future fatigue driving of the driver.

Methods and apparatuses for estimating activation of cerebral cortical sources from scalp site measurements of electroencephalograms (EEG) from a human subject are provided. In an exemplary embodiment, a method comprises: specifying cortical neural sources within the brain of the subject; simulating activation potentials for the specified cortical neural sources; projecting the simulated cortical neural source voltage activations to scalp site measurements locations for the subject; simulating scalp voltage potentials for one or more extra-cranial sources outside the brain; constructing simulated scalp site voltage potentials from the projected simulated modeled cortical neural source voltage activations and the simulated extra-cranial scalp voltage potentials; comparing the simulated scalp site voltage potentials and the electroencephalograms scalp site measurements for the subject; and adjusting parameters for simulating the cortical neural source voltage activations until the simulated scalp site voltage potentials generally correspond to the electroencephalograms scalp site measurements based on the comparison.

A system and method for estimating cognitive efficacy of an individual is provided. The individual is subjected to a plurality of tasks. Electroencephalogram (EEG) data from EEG sensors coupled to the individual are collected while the individual is performing the plurality of tasks. The EEG data are supplied to a trained classifier that generates an estimate of cognitive effort of the individual for each of the plurality of tasks performed by the individual.

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 relates to a children ASD diagnosis device based on a magnetoencephalogram and an electroencephalogram. The children ASD diagnosis device comprises a magnetic shielding system, a head-mounted electroencephalogram and magnetoencephalogram array type sensor system, an electroencephalogram and magnetoencephalogram data acquisition system, a sensory stimulation system, a signal processing system and a data analysis system. The magnetic shielding system can effectively reduce background magnetic field noise. The head-mounted electroencephalogram and magnetoencephalogram array type sensor system can carry out whole-brain collection on a tested child in a combined nesting mode. The electroencephalogram and magnetoencephalogram data acquisition system can record brain electrical activity information. The sensory stimulation system can present visual and auditory sensory stimulation for the tested child. The signal processing system removes biological magnetic noise and backgroundmagnetic noise. The data analysis system can extract pathological features in the signals for analysis. The children ASD diagnosis device provided by the invention has the advantages of high sensitivity and specificity, multiple information sources, high signal quality, easy acceptance by children and the like. The children ASD diagnosis device has the advantages of high sensitivity and specificity in the aspect of ASD diagnosis of children.

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.

The present invention disclosed a device and method for assessing in real time the hypnotic and analgesic effect in a subject during wakefulness, sedation and general anaesthesia via drug interactions and physiological signals. In a preferred embodiment of the present invention, the analgesic and hypnotic effect of drug(s) infused in a subject could be accurately assessed in real time through the device and method disclosed in the present invention, comprising steps of receiving data from electroencephalography (EEG) device, receiving data from brain impedance tomography device, obtaining pharmacodynamic and pharmacokinetic parameters of drug(s) infused in the subject, defining initial indices of consciousness and nociception as a function of said EEG and brain impedance tomography data, and generating output of final indices of consciousness and nociception in real time from processing input of EEG, brain tomography and drug interaction data using established mathematical manipulation.

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.

According to the invention, when a general practitioner robot arrives at a preset position, by adoption of a remote control or intelligent control mode and by means of man-machine interaction and man-machine fusion, the general practitioner robot monitors vital signs of the human body in real time, carries out physical examinations like whole-body examination, electrocardio, ultrasound, X-ray, electroencephalogram and lung functions, collects specimens, completes assay pathology detection, implements remote diagnosis and treatment, automatically generates medical record documents, executes medical advice, performs emotional accompanying and environmental disinfection, thereby completing basic diagnosis and treatment operations in wards, homes and dangerous environments, improving energy efficiency and reducing the risk of medical staff.

Systems and methods are provided for performing neurometric evaluation of Quantitative Electro Encephalogram (QEEG) data, derived from Dry Sensor technology, as opposed to the use of any types of conventional paste / gel and silver / silver chloride sensors. The individuals who would be helped by these screening procedures are identified.

An electroencephalogram (EEG) classification detection device based on lacuna characteristics belongs to the technical field of electroencephalogram automatic detection. The EEG classification detection device comprises a multi-way EEG amplifier, a data collection card and a computer which are sequentially connected through a circuit. A signal preprocessing module, a signal segmentation module, a lacuna characteristic extraction module, a Bayes linear discriminant analysis classification module and a threshold judgment module are built in the computer. The multi-way EEG amplifier first amplifies EEG signals, then the data collection card collects the EEG signals and transmits the signals to the computer, finally the modules in the computer are utilized to conduct preprocessing and segmentation on the EEG signals and calculate the lacuna characteristics of the signals, a Bayes linear discriminant analysis classification device is utilized to classify the EEG lacuna characteristics, and the threshold judgment module is used for marking the classification and obtaining a result. The EEG classification detection device has the advantages of being simple in characteristic operation, high in practice and classification speed, high in classification accuracy and capable of achieving good classification detection effect.

A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000 / 17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e., in a deeper depth of anesthesia.