Paper Title :Classification Algorithms Employed by EEG-Based BCI - A Comparative Survey

Author : Gurpreet Kaur Saimy, Abha Mutalik, Harsh Jain, Sudhir N. Dhage

Article Citation :Gurpreet Kaur Saimy ,Abha Mutalik ,Harsh Jain ,Sudhir N. Dhage , (2019 ) " Classification Algorithms Employed by EEG-Based BCI - A Comparative Survey " , International Journal of Advances in Science, Engineering and Technology(IJASEAT) , pp. 35-40, Volume-7, Issue-1, Spl. Iss-1

Abstract : Brain-Computer Interface (BCI) is a technology in cognitive science that maps a user’s neural signals to commands that are further relayed to an output device in order to carry out the desired action. A variety of signals can be acquired and analysed for BCI applications, however, we will be focusing on the Electroencephalographic (EEG) signals in this survey. Fundamentally, a BCI system consists of signal acquisition, data preprocessing, extracting relevant features and their classification. For the final classification module, a number of machine learning approaches such as Support Vector Machines, Linear Discriminant Analysis, Naive Bayes, Decision Trees, k-NN and Random Forest have been used traditionally. However, the focus is now shifting towards the more efficient deep learning techniques like Convolutional Neural Networks, Deep Belief Networks and a combination of models, for classification. The neural network classifiers are by and large seen to be favored over the one-size-fits-all strategies of the traditional machine learning classifiers which are suitable for a wide range of solutions. In this survey, we present the major classification techniques employed over the years in the research of EEG-based BCI and provide a comparative analysis of the same. We take the percentage accuracy as a performance measure for comparison. Keywords - Machine Learning, Deep Learning, Classification, Brain-Computer Interface, Electroencephalographic signals.

Type : Research paper

Published : Volume-7, Issue-1, Spl. Iss-1

	|		PDF	|	Viewed - 48	|	Published on 2019-05-18