Innereye Ltd.

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

Ben-gurion University of the Negev Research and Development Authority

Leon Y Deouell

Amir B Geva

Ran El Manor

Eitan Netzer

Sergey Vaisman

Shani Shalgi

Omri Harish

Uri Antman

Galit Fuhrmann Alpert

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 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.

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.

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Tel Aviv, Israel

Leon Y Deouell