Brain Neural Data Analysis Using Machine Learning Feature Selection and Classification Methods

The Electroencephalogram (EEG) is a powerful instrument to collect vast quantities of data about human brain activity. A typical EEG experiment can produce a two-dimensional data matrix related to the human neuronal activity every millisecond, projected on the head surface at a spatial resolution of a few centimeters. As in other modern empirical sciences, the EEG instrumentation has led to a flood of data and a corresponding need for new data analysis methods. This paper summarizes the results of applying supervised machine learning (ML) methods to the problem of classifying emotional states of human subjects based on EEG. In particular, we compare six ML algorithms to distinguish event-related potentials, associated with the processing of different emotional valences, collected while subjects were viewing high arousal images with positive or negative emotional content. 98% inter-subject classification accuracy based on the majority of votes between all classifiers is the main achievement of this paper, which outperforms previous published results.