The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 30, 2020

Filed:

Apr. 13, 2014
Applicants:

Yissum Research Development Company of the Hebrew University of Jerusalem Ltd., Jerusalem, IL;

B.g. Negev Technologies & Applications Ltd., AT Ben-gurion University, Beer-Sheva, IL;

Inventors:

Leon Y. Deouell, Tel-Aviv, IL;

Amir B. Geva, Tel-Aviv, IL;

Galit Fuhrmann Alpert, Jerusalem, IL;

Ran El Manor, Savyon, IL;

Shani Shalgi, Hod-HaSharon, IL;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/0484 (2006.01); A61B 5/04 (2006.01); A61B 5/0476 (2006.01); A61B 5/00 (2006.01);
U.S. Cl.
CPC ...
A61B 5/04842 (2013.01); A61B 5/04012 (2013.01); A61B 5/0476 (2013.01); A61B 5/4064 (2013.01); A61B 5/7203 (2013.01); A61B 2576/026 (2013.01);
Abstract

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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