|International Journal of Advances in Science, Engineering and Technology(IJASEAT)-IJASEAT
|Volume-7, Issue-1, Spl. Iss-1 ( Feb, 2019 )
|Classification Algorithms Employed by EEG-Based BCI - A Comparative Survey
|Gurpreet Kaur Saimy, Abha Mutalik, Harsh Jain, Sudhir N. Dhage
|Department of Computer Engineering, Bharatiya Vidya Bhavan's, Sardar Patel Institute of Technology, Andheri (West), Mumbai-400 058
|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.