Computational Intelligence and Neuroscience 
Volume 2007 (2007), Article ID 74895, 12 pages
doi:10.1155/2007/74895
Research Article

Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

Tian Lan,1 Deniz Erdogmus,1 Andre Adami,2 Santosh Mathan,3 and Misha Pavel1

 1Department of Biomedical Engineering, Oregon Health and Science University, Portland 97239, OR, USA
2Department of Computer Science, University of Caxias do Sul, Caxias do Sul 95070-560, RS, Brazil
3Human Centered Systems, Honeywell Laboratories, Minneapolis 55401, MN, USA
Received 14 February 2007; Accepted 18 June 2007

Recommended by Andrzej Cichocki

Abstract

We present an ambulatory cognitive state classification system to assess the subject's mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.