Online Eye state recognition from EEG data using Deep architectures

Abstract

In the past decade, improvements in the production of in-expensive PC equipment and software has permitted more refined real-time signal processing in BCI systems. In the literature, Deep learning concepts have not been applied to EEG data analysis in a systematic manner. This paper applies various existing Deep learning architectures and algorithms for the classification of EEG data applied to eye state detection. The deep learning based classifier systems presented in this work are comparable to the state of the art classifiers devised by Roesler and Suenderman (2013), and Cameron et al. (2015). The goal of this work is to construct a system producing accuracies comparable to Roesler's K* classifier, Cameron et al.'s (RRF+K*) classifiers and at the same time providing enough speed to be used in an online BCI framework. In order to meet the constraints, following architectures were designed: A Multi layered neural network with ReLU and drop-out, deep belief networks based unsupervised learning, drop-out masks on deep neural networks. Specifically, we compare our results with K*, RRF, (K*+RRF), ada(RJ48F) classifiers. Also an in-depth analysis of binary class features has been done using t-SNE based visualizations while fitting elliptical contours to the features. Prior research suggests that instance-based/lazy learners like the K* algorithm are likely to be too slow to be used in a BCI framework, with ada(RJ48F) model performing decently well. But our chosen deep neural network architectures produce higher classification accuracies and have lower convergence times making them even faster within the time specifications of real-time classification and control applications.