Supporting Real-time Cognitive State Classification on a Mobile Individual

The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and cognitive state classification. While this presents a difficult technical challenge in any context, these concerns were particularly pronounced in a system designed for mobile contexts. This paper describes a neurophysiologically-derived cognitive state classification approach designed for ambulatory task contexts. We highlight signal processing and classification components that render the electroencephalogram (EEG) based cognitive state estimation system robust to noise. Field assessments show classification performance that exceeds 70% for all participants in a context that many have regarded as intractable for cognitive state classification using EEG.