Classifying High-Noise EEG in Complex Environments for Brain-Computer Interaction Technologies

Future technologies such as Brain-Computer Interaction Technologies (BCIT) or affective Brain Computer Interfaces (aBCI) will need to function in an environment with higher noise and complexity than seen in traditional laboratory settings, and while individuals perform concurrent tasks. In this paper, we describe preliminary results from an experiment in a complex virtual environment. For analysis, we classify between a subject hearing and reacting to an audio stimulus that is addressed to them, and the same subject hearing an irrelevant audio stimulus. We performed two offline classifications, one using BCILab [1], the other using LibSVM [2]. Distinct classifiers were trained for each individual in order to improve individual classifier performance [3]. The highest classification performance results were obtained using individual frequency bands as features and classifying with an SVM classifier with an RBF kernel, resulting in mean classification performance of 0.67, with individual classifier results ranging from 0.60 to 0.79.