SSVEP Brain Hash Function
The electro-physiological visual feedback screen
This project had a goal to research if steady-state visually evoked potential (SSVEP) can be used to create Brain Hash Function Algorithm able to distinguish between unique footprints of each individual brain under specific visual stimulation with electro-physiological visual feedback based on consumer-grade EEG monitoring device.
The project scope consist of two major parts:
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Implementation of electro-physiological visual feedback system based on consumer-grade EEG monitoring device. It should perform monitoring of EEG signals in real time and perform preprocessing of received raw EEG signal.
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Implementation of advance machine-learning pipeline to automatically extract important features from data stream and perform classification of encoded features. It should perform confident classification of the collected EEG data in order to (a) reliably distinguish signal from noise and (b) reliably distinguish between EEG records collected from different human participants.
The project timeline and results can be found at: ResearchGate
Iaroslav Omelianenko
CTO/Research Director at NewGround LLC
My research interests include genetic algorithms/neuroevolution, synthetic cognitive systems, smart environment, and cooperative robotics.