Patents by Inventor Galit FUHRMANN ALPERT

Galit FUHRMANN ALPERT has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Classifying EEG signals in response to visual stimulus
    Patent number: 10694968
    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.
    Type: Grant
    Filed: April 13, 2014
    Date of Patent: June 30, 2020
    Assignees: Yissum Research Development Company of the Hebrew University of Jerusalem Ltd., B.G. Negev Technologies & Applications Ltd., at Ben-Gurion University
    Inventors: Leon Y. Deouell, Amir B. Geva, Galit Fuhrmann Alpert, Ran El Manor, Shani Shalgi
  • CLASSIFYING EEG SIGNALS IN RESPONSE TO VISUAL STIMULUS
    Publication number: 20160051163
    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.
    Type: Application
    Filed: April 13, 2014
    Publication date: February 25, 2016
    Inventors: Leon Y. DEOUELL, Amir B. GEVA, Galit FUHRMANN ALPERT, Ran El MANOR, Shani SHALGI