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An Evaluation Of Real-Time Cognitive State Classification In A Harsh Operational Environment, Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Stephen D. Whitlow
An Evaluation Of Real-Time Cognitive State Classification In A Harsh Operational Environment, Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Stephen D. Whitlow
Michael C. Dorneich
This paper describes an evaluation conducted with a full platoon of 32 Soldiers at Aberdeen Proving Grounds' MOUT site in Aberdeen, MD. The objective was to assess the cognitive workload classification techniques driven by neuro-physiological (EEG) and physiological (ECG) sensors. In a first ever evaluation of real-time cognitive monitoring in the harsh operational environment, the assessment culminated in a three phase, 24-hour mission consisting of a coordinated Route Reconnaissance, a Cordon and Search of a village, and a Hasty Defense operation. Task load levels were manipulated by introducing unexpected and unplanned events requiring re-planning and extensive coordination by the leadership …
Supporting Real-Time Cognitive State Classification On A Mobile Individual, Michael C. Dorneich, Stephen D. Whitlow, Santosh Mathan, Patricia May Ververs, Deniz Erdogmus, Andre Adami, Misha Pavel, Tian Lan
Supporting Real-Time Cognitive State Classification On A Mobile Individual, Michael C. Dorneich, Stephen D. Whitlow, Santosh Mathan, Patricia May Ververs, Deniz Erdogmus, Andre Adami, Misha Pavel, Tian Lan
Michael C. Dorneich
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 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 …