Elminda Ltd.

Innereye Ltd.

Yissum Research Development Company of the Hebrew University of Jerusalem Ltd.

Ben-gurion University of the Negev Research and Development Authority

B.G. Negev Technologies and Applications Ltd.

Brainsway Ltd.

Firefly Neuroscience Ltd.

Elmina Ltd.

Amir B Geva

Amit Reches

Goded Shahaf

Yaki Stern

Ran El Manor

Leon Y Deouell

Sergey Vaisman

Eitan Netzer

Noga Pinchuk

Guy Ben-Bassat

Ayelet Kanter

Ronen Gadot

Shani Shalgi

Omri Harish

Ziv Peremen

Tomer Carmeli

Revital Shani-Hershkovich

Israel Tauber

Urit Gordon

Uri Antman

Yiftach Roth

Abraham Zangen

Gaby S Pell

Galit Fuhrmann Alpert

Dror Haor

Ronen Segal

Uri Alyagon

Boaz Sadeh

An image classification neural network is trained based on images that are the presented to an observer as a visual stimulus while collecting neurophysiological signals from a brain of the observer. The neurophysiological signals are processes to identify a neurophysiological event indicative of a detection of a target by the observer in one or more of the images, and the image classification neural network is trained to identify the target in the image based on the identification of the neurophysiological event.

System and method for iterative classification using neurophysiological signals

A method of analyzing neurophysiological data recorded from a subject is disclosed. The method comprises identifying activity-related features in the data, and parceling the data according to the activity-related features to define a plurality of capsules, each representing a spatiotemporal activity region in the brain. The method further comprises comparing at least some of the defined capsules to at least one reference capsule, and estimating a brain function of the subject based on the comparison.

Neurophysiological data analysis using spatiotemporal parcellation

A method of training an image classification neural network comprises: presenting a first plurality of images to an observer as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of a target by the observer in at least one image of the first plurality of images; training the image classification neural network to identify the target in the image, based on the identification of the neurophysiological event; and storing the trained image classification neural network in a computer-readable storage medium.

Apparatus and methods are described for use with electrophysiological signal detecting electrodes, and a transcranial magnetic stimulation device. A computer processor drives the transcranial stimulation device to apply one or more pulses of transcranial magnetic stimulation to a subject. Within a given time period of applying one of the one or more pulses of transcranial magnetic stimulation to the subject, the computer processor detects an electrophysiological signal of the subject, using the electrophysiological signal detecting electrodes. At least partially in response thereto, the computer processor predicts an outcome of treating the subject for a neuropsychiatric condition, using a given therapy, and generates an output on an output device in response to the predicted outcome. Other applications are also described.

Apparatus and methods for predicting therapy outcome

A method of classifying an image is disclosed. The method comprises: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of the target by the observer; and determining an existence of the target in the image is based, at least in part, on the identification of the neurophysiological event.

Image classification by brain computer interface

Systems and method for classifying EEG signals of a human subject generated responsive to a series of images containing target images and non-target images. The EEG signals are in a spatio-temporal representation. The time points are classified independently, using a linear discriminant classifier, to compute spatio-temporal discriminating weights that are used to amplify the spatio-temporal representation, to create a spatially-weighted representation. Principal Component Analysis is used on a temporal domain for dimensionality reduction, separately for each spatial channel of the signals, to create a projection, which is applied to the spatially-weighted representation onto a first plurality of principal components, to create a temporally approximated spatially weighted representation. The temporally approximated spatially weighted representation is classified over the channels, using said linear discriminant classifier, to yield a binary decisions series indicative of each image of the images series as either belonging to said target image or to said non-target image.

Classifying EEG signals in response to visual stimulus

A method of configuring a local brain stimulation tool is disclosed. The method comprises: obtaining a reference brain network activity (BNA) pattern, and a BNA pattern describing a neurophysiological state of the subject, each of the BNA patterns having a plurality of nodes and each node representing a brain location and at least one brain wave frequency. The method further comprises comparing the BNA patterns; and configuring the local brain stimulation tool to apply local brain stimulation at a frequency selected based on the comparison.

Brain stimulation tool configuration

A method of analyzing neurophysiological data is disclosed. The method comprises: identifying activity-related features in the data, constructing a brain network activity (BNA) pattern having a plurality of nodes, each representing a feature of the activity-related features, and assigning a connectivity weight to each pair of nodes in the BNA pattern.

Method for diagnosing a brain related disorder using brain network activity patterns

A method of estimating the likelihood of brain concussion from neurophysiological data acquired from the brain of the subject is disclosed. The method comprises: identifying activity-related features in the data; constructing a subject-specific brain network activity (BNA) pattern having a plurality of nodes, wherein each node represents a feature of the activity-related features, and each pair of nodes is assigned with a connectivity weight; calculating a BNA pattern similarity describing a comparison between the constructed BNA pattern and a baseline BNA pattern being specific to the subject; and assessing the likelihood of brain concussion responsively to the BNA pattern similarity.

Method and system for estimating brain concussion

A method for functional analysis of neurophysiological data by decomposing neurophysiological data and EEG signal to form a plurality of signal features. The signal features may then optionally be analyzed to determined one or more patterns.

Functional analysis of neurophysiological data

Systems and methods for identifying and analyzing neuropsychological flow patterns, include creating a knowledge base of neuropsychological flow patterns. The knowledge base is formed by obtaining signals from multiple research groups for particular behavioral processes, localizing sources of activity participating in the particular behavioral processes, identifying sets of patterns of brain activity for the behavioral processes and neuropsychologically analyzing the localized sources and the identified patterns for each of the research groups. The neuropsychological analysis includes identifying all possible pathways for the identified sets of patterns, ranking the possible pathways based on likelihood for the particular behavioral process and reducing the number of ranked possible pathways based on additional constraints. A system for comparison of obtained signals from an individual to the created knowledge base is provided. These obtained signals are then used to further update the existing knowledge base.

Neuropsychological spatiotemporal pattern recognition

A method of managing pain using neurophysiological data acquired from the brain of a subject is disclosed. The method comprises: identifying activity-related features in the data; parceling the data according to the activity-related features to define a plurality of capsules, each representing a spatiotemporal activity region in the brain; comparing at least some of the defined capsules to at least one reference capsule; and assessing the likelihood that the subject is experiencing pain responsively to the comparison.

Growing community of inventors

Tel Aviv, Israel

Amir B Geva