Multiclass classification of single-trial evoked EEG responses

Abstract

The detection of event-related potentials (ERPs) in the electroencephalogram (EEG) signal has several real-world applications, from cognitive state monitoring to brain-computer interfaces. Current systems based on the detection of ERPs only consider a single type of response to detect. Hence, the classification methods that are considered for ERP detection are binary classifiers (target vs. non target). Here we investigated multiclass classification of single-trial evoked responses during a rapid serial visual presentation task in which short video clips were presented to fifteen observers. Each trial contained potential targets that were human or non-human, stationary or moving. The goal of the classification analysis was to discriminate between three classes: moving human targets, moving non-human targets, and non-moving human targets. The analysis revealed that the mean volume under the ROC surface of 0.878. These results suggest that it is possible to efficiently discriminate between more than two types of evoked responses using single-trial detection.