131 results about "Neurofeedback" patented technology

A noise-free portable EEG system is provided. The system has hardware and software and can evaluate mental state quantitatively. The quantitative data of mental states and their levels can be applied to various areas of brain-machine interface including consumer products, video game, toys, military and aerospace as well as biofeedback or neurofeedback.

A wearable signal acquisition and computerized display device including at least one first sensor configured to measure at least one of the following attributes from a subject wearing the device: attention, focus, concentration, alertness, and mood. The device has a heads-up display or goggles, glasses, or another head piece having lenses. The measurement is derived from the subject's brainwaves by use of one or more algorithms. The subject wearing the device receives neurofeedback data based on this measurement through the projected image to train them how to control attention, mood, etc. and improve understanding as to how various external stimuli in the real world or virtual world impact these attributes. In one variation the sensors measure brainwave signals at two positions of the subject and use the difference in these measurements to derive an attention indicator signal used to provide neurofeedback.

A method for changing appearance of EEG or a relationship of brain wave patterns while achieving behavioral changes, the method including individualized assessing of dysregulated patterns of brain function using topographic quantitative electroencephalogram (LORETA [low resolution brain electromagnetic tomography] qEEG) or regular EEG recollection, modifying or priming neuroplasticity with transcranial direct current stimulation (tDCS) or with magnetic or electromagnetic fields to modify cortical excitability of neurons in dysregulated areas, and self-regulating LORETA patterns with neurofeedback to change an appearance of EEG or a relationship of brain wave patterns while achieving behavioral changes.

A method and system provides for headgear usable for electrophysiological data collection and analysis and neurostimulation/neuromodulation or brain computer interface for clinical, peak performance, or neurogaming and neuromodulation applications. The headgear utilizes dry sensor technology as well as connection points for adjustable placement of the bi-directional sensors for the recoding of electrophysiology from the user and delivery of current to the sensors intended to improve or alter electrophysiology parameters. The headgear allows for recording electrophysiological data and biofeedback directly to the patient via the sensors, as well as provide low intensity current or electromagnetic field to the user. The headgear can further include auditory, visual components for immersive neurogaming. The headgear may further communication with local or network processing devices based on neurofeedback and biofeedback and immersive environment experience with balance and movement sensor data input.

Embodiments of the present invention provide a method, system and computer program product for message notification management through brain monitoring of end user concentration. In an embodiment of the invention, a method for message notification management through brain monitoring of end user concentration can include receiving neurofeedback for an end user through an interface for a brain monitoring system, comparing the neurofeedback to a threshold level of brain activity indicating a degree of concentration of the end user, and suppressing message notifications for messages in a messaging system responsive to the neurofeedback exceeding the threshold level of brain activity, but otherwise permitting message notifications for messages in the messaging system.

Methods, systems, and apparatus implementing a generalizable self-calibrating protocol coupled with machine learning algorithms in an exemplary setting of classifying perceptual states as corresponding to the experience of perceptually opposite mental states (including pain or no pain) are disclosed. An embodiment presented represents inexpensive, commercially available, wearable EEG sensors providing sufficient data fidelity to robustly differentiate the two perceptually opposite states. Low-computational overhead machine learning algorithms that can be run on a mobile platform can be used to find the most efficient feature handles to classify perceptual states as self-calibrated by the user. The invention is generalizable to states beyond just pain and pave the way towards creating EEG NFB applications targeting arbitrary, self-calibrated perceptual states in at-home and wearable settings.

A means and method for inducing a temporary physiological state-of-mind to effect persistent changes to the cognitive-emotive profile of an individual, which is adaptable for neurofeedback and “mental-state” therapeutic and non-therapeutic interventions. The system comprises an EEG Recording Module (ERM), a Neurodynamics Assessment Module (“NAM”), and a Transcranial Magnetic Stimulation module (“TMS”) for acquiring and manipulating bioelectrical and/or EEG data, defining a cognitive-emotive profile, and mapping the cognitive-emotive profile to selectively control transcranial magnetic stimulation to drive therapeutic and non-therapeutic stimulus interventions. A bi-directional feedback feature is provided to further enhance the performance of the system to effect prolonged changes.

A method and system provides for electrophysiological data analysis in a networked processing environment. The method and system includes receiving, via a networked connection, electrophysiological data of a patient and electronically performing, via at least one network processing device, a data analysis on the electrophysiological data. The method and system includes generating at least one report based on the data analysis, wherein the at least one report includes determination of one or more intervention options for the patient and therein transmitting the report to a recipient device across the network connection for utilization with the patient. The results of the report direct the user to apply from within the same system non-invasive brain stimulation, neurofeedback, and biofeedback modalities. Re-assessment can occur from within the same system following the training or modulation of electrophysiology and thereby generate a comparison report showing functional changes from the provided intervention or combined interventions.

The invention relates to a nervous feedback system based on a real-time functional magnetic resonance signal, comprising a real-time functional magnetic resonance data reading and format transforming module, a real-time functional positioning detection module and a real-time feedback stimulation module, wherein the real-time functional positioning detection module extracts the active mode of the brain of a trainee under a specific task, activates the active state of the brain area through the real-time feedback stimulation module and shows the activated active state to the trainee in a visual way, and the trainee achieves the effect of regulating a specific cognitive function by learning to control the activity level of the activated brain area. The invention detects the activated state of the brain on line through the real-time functional magnetic resonance signal, feeds back to the trainee in real time, enhances or recovers the corresponding cognitive function of the trainee by repeatedly training, regulating and controlling the cognitive activity level of the brain and has important social value on aspects, i.e. brain function mechanism research, brain perceptive and cognitive ability regulation, treatment of diseases related to cognition, and the like.

The invention provides a neural feedback training system and a neural feedback training method on the basis of optical brain imaging. A trainee completes training tasks to achieve training purposes in the neural feedback training method. The neural feedback training method includes that neural activity data of the trainee are acquired by optical brain imaging equipment in a training task completion procedure; neural activity intensity indexes of a specific function system of the brain of the trainee are extracted from the neural activity data and are displayed to the trainee as feedback information; and the trainee adjusts training strategies according to the acquired feedback information, so that neural activities of the specific function system of the brain of the trainee are trained and are developed towards a target. The neural activity data of the trainee are acquired by the optical brain imaging equipment in the neural feedback training method. Compared with electroencephalogram and magnetic resonance imaging, the optical brain imaging is high in targeting, the optical brain imaging equipment is low in cost, and accordingly the neural feedback training system and the neural feedback training method are applicable to long-term neural feedback training.

The invention provides a neuro feedback device, system and method, and relates to the technical field of neuro feedback. The neuro feedback device comprises a shell, a brain electricity acquiring module, a brain electricity signal processing module, a data transmitting module and a power supply module, wherein the brain electricity acquiring module is arranged on one side of the shell; the brain electricity signal processing module, the data transmitting module and the power supply module are arranged in the shell; the brain electricity acquiring module is connected with the brain electricity signal processing module; the brain electricity signal processing module is connected with the data transmitting module; and the brain electricity acquiring module, the brain electricity signal processing module and the data transmitting module are separately connected with the power supply module. The neuro feedback device is suitable for clinical treatment and personal families, a targeted training scheme can be provided for neurological defects of different diseases, the autognosis of a patient is improved in a treatment process, and the neuro feedback training effect can be improved effectively.

The invention provides a self-adaption brain neural feedback method and system based on a near-infrared spectrum technology. The method comprises the steps that 1) cerebral blood oxygen signals of a participant during a specific training task are collected; 2) the collected cerebral blood oxygen signals are pretreated to remove motion artifacts and abnormal signals in original signals and performbaseline correction; 3) a neural activity intensity index of a specific functional area of the brain is extracted from the pre-treated cerebral blood oxygen signals and fed back to the participant; 4)self-adjustment is performed according to received visual feedback information, and the cerebral blood oxygen signals of the participant are collected again and input into a self-adaption feedback module, the self-adaption feedback module treats the cerebral blood oxygen signals, extracts the neural activity intensity index of the specific functional area of the brain, compares the neural activity intensity index with the neural activity intensity index in step 3), and gives corresponding reward feedback and penalty feedback, thereby achieving the self-adaption feedback adjustment of the neural activity.

The invention provides a feedback system combining electroencephalogram and functional magnetic resonance signals, which jointly collects and analyzes electroencephalogram and functional magnetic resonance signals, extracts spatial-temporal characteristics of brain specific activity states reflected by the two signals simultaneously, and applies a multi-mode signal to nervous feedback regulation.The system comprises a data detection and pre-processing module, a spatial-temporal characteristic extraction module and a display and feedback module, wherein the data detection and pre-processing module detects the synchronously collected electroencephalogram and functional magnetic resonance signals on line, marks synchronous start points of the two signals, and performs pre-processing respectively; the spatial-temporal characteristic extraction module respectively extracts different electroencephalogram characteristics by aiming at resting state data and task state data, and performs statistic modeling analysis on the electroencephalogram characteristics together with functional magnetic resonance data to extract brain functional regions with the spatial-temporal characteristics; and different characteristics reflecting the same state of the brain are independently or jointly fed back. The system has important application value in clinical rehabilitation, brain-machine interface and other aspects.

The invention discloses a novel motor imagery (MI)-steady state somatosensory evoked potential (SSSEP) mixed brain-computer interface method. The method comprises: two electrocardioelectrodes are placed at a left wrist and a right wrist, electrical simulation is carried out a left hand and a right hand according to a preset frequency, thumbs are induced to tremble slightly, thereby inducing obvious steady-state somatosensory evoked potentials; a tester is simulated and is processed by motor imagery, electroencephalogram data are collected, and pretreatment is carried out; feature extraction and pattern recognition are carried out on the electroencephalogram data by using a common spatial pattern algorithm and a single-task electroencephalogram feature vector is obtained; the single-task electroencephalogram feature vector is inputted into a support vector machine to train a classifier and classification identification is completed by using the support vector machine based on a ten-fold cross validation strategy, so that six sub frequency bands are built by using 4 Hz as stepping at frequency bands of 8 to 32Hz so as to complete classification identification. With the method, the ERD feature and the SSSEP feature are integrated, thereby realizing performance improvement; and robustness of neural-feedback-based rehabilitation training is enhanced.

The invention discloses a group neural feedback training method and a group neural feedback training system. The group neural feedback training system comprises at least two brain imaging devices, at least three central processing units and a plurality of display devices. Each of the central processing units comprises a server-side processing unit and at least two client-side processing units. An output end of each brain imaging device is connected with an input end of a corresponding client-side processing unit. An output end of each client-side processing unit is connected with an input end of each corresponding display device. A plurality of client-side processing units are respectively connected with the server-side processing units. In the group neural feedback training system, the client-side processing units are used for obtaining a brain neural activity indicator of a local trainee and the server-side processing units are used for obtaining a brain neural activity interactivity indicator of the local trainee. Through feeding the brain neural activity interactivity indicator back to the trainee, the trainee can regulate a training strategy autonomously, so that the aim that group cognitive behaviors are changed can be achieved.

The invention discloses a multi-user neural feedback training method and a multi-user neural feedback training system. The multi-user neural feedback training system comprises at least one brain imaging device, a central processing unit and a plurality of display devices, the brain imaging device is used for acquiring neural activity data of a plurality of users and transmitting the acquired neural activity data to the central processing unit, the central processing unit is used for analyzing the neural activity data by combining training tasks to obtain brain neural activity interactivity indexes of all users and transmitting the brain neural activity interactivity indexes to the display devices, and the display devices are used for displaying feedback information to the users. The users can adjust a training strategy according to the feedback information so that neural activity interactivity among the users can be trained to develop towards a target pattern, behavior consistency among the multiple users can be trained, interpersonal relationship can be adjusted, and interpersonal cognition and behavior of the users can be changed.