Honeywell Laboratories

ABSTRACT: The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and cognitive state classification. Although this presents a difficult technical challenge in any context, these concerns are particular-ly pronounced in a system designed for mobile contexts. This paper describes a neuro-physiologically 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 par-ticipants in a context that many have regarded as intractable for cognitive state class-ification using EEG