Classification Algorithms Employed By Eeg-Based Bci - A Comparative Survey
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.