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Brain Computer Interfaces - SSVEP (PESIT South Campus)

This is a demonstration of the Final Year Project for the year 2015 - 2016 designed and developed by students of ECE Department at PESIT - Bangalore South Campus on Brain Computer Interfaces (BCI). In this video, an SSVEP based BCI application is described. The subject controls a mini wheelchair by just focusing on a set of boxes flickering on the screen. The EEG signal is recorded using OpenBCI and in real-time processed using OpenViBE. Commands are sent on a wireless interface using a ZigBee Module. This is a 4-class machine learning problem. The 4 classes correspond to the 4 flicker boxes, i.e. 10Hz, 12Hz, 15Hz and No flicker Box (Null Class). The features used are: Band Power features and the Classification algorithm used: LDA - Linear Discriminant Analysis. The training time is approximately 7 minutes, comprising of 32 trials - 8 trials for each class. At this stage the LDA classifier is trained and parameters are extracted. The testing phase consists of the subject trying to control the wheelchair. This real-time features are tested against the parameters obtained from the training stage. This way we can implement an SSVEP based BCI for any application. Project Guided by: Mr. Sastry V. Ramachandrula Assistant Professor Dept. of ECE PESIT Bangalore South Campus Group Members: Aravind R Gagandeep Panwar Indrapriyadarsini Samit Shah Final Year Students Dept. of ECE PESIT - Bangalore South Campus