MULTI-TASK LEARNING TO RECOGNIZE NEURAL ACTIVITIES, AND APPLICATIONS THEREOF

Abstract: in an embodiment, a computer-implemented method for decoding neural activity is provided. in the method, at least one machine learning model is trained using a training data set of eeg data and concurrently collected environmental data collected from data collection participants. once the at least one machine learning model is trained, eeg data measured from sensors attached to or near a user's head is received. environmental data describing stimulus the user is exposed to concurrently with the measurement of the eeg data is also received. the eeg data and the environmental data is input into the at least one machine learning model to determine an inference related to the neural activity. based on the inference, an operation of a computer program is altered.

Inventor(s): Hagai LALAZAR, Gideon LITTWIN, Nizan KLINGHOFFER, Jonathan BERREBI, Ilay GORDON

CPC Classification: A61B5/372 (DIAGNOSIS; SURGERY; IDENTIFICATION (analysing biological material , e.g. ; obtaining records using waves other than optical waves, in general ))

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