Classification of EEG Signals in BCI

Saurabh Diwaker, Satyendra Nath Shukla,Rahul Yadav, Rahul Srivastava

Abstract


A Brain Computer Interface or BCI is an emerging technology that targets to directly convey intentions of people to the outside world from their thoughts. This works as a promising tool for normal people to improve the way of communication with machines or computer systems and it can also be used for helping those people who are physically disabled. BCI has introduced not only the dimensions in machine controlling terminologies but also it has shown a bright way to researcher around the world to explore the possibilities to use this new growing technology in real life applications. It has driven anticipation to researcher that an alternative to communication can be provided for those people who are physically disabled. In this paper we are going to present a brief idea about classification of EEG signals used in BCI and the accuracy of the method of classification implemented. The methods which are used for pre-processing of the raw EEG signals will also be discussed as it can enhance the quality of input data for classification so that we can get more accurate results.

 

Keywords- BCI, EEG signals, Pre-processing, Classification, Independent Component Analysis, Support Vector Machine.


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DOI: https://doi.org/10.26483/ijarcs.v3i6.1380

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