Paper Title
Recurrent Neural Networks on EEG based Classification for Brain Computer Interface

Abstract
Brain Computer Interface is a system that allows humans to interact with the surroundings just by brain intentions. The most successful BCI’s are EEG based due to the portable non-invasive devices available. Although EEG systems have not entered the day to day life applications due the problems such as low signal to noise ratio, connectivity issues, low accuracy etc. In this paper, we introduce recurrent neural network for identifying binary class human intention on EEG time-frequency stream. The data is recorded using an Emotiv Insight 5 channel headset. A vehicular authentication system was developed using the proposed RNN classifier. In addition to this, an Android Application targeted towards disabled quadriplegic people is developed too using the proposed model. Keywords - Brain Computer Interface, Electroencephalography, Recurrent Neural Networks, Emotiv Insight, Vehicular Authentication.

Author - Pratik Kamble, Akanksha Kale, Adwait Relekar, Rushikesh Shinde, Deepali Javale, Sunita Kulkarni

Published : Volume-6,Issue-9  ( Sep, 2019 )

	|		PDF	|	Viewed - 54	|	Published on 2019-11-28