Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEGReport as inadecuate




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Computational Intelligence and Neuroscience - Volume 2007 2007, Article ID 74895, 12 pages

Research Article

Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA

Department of Computer Science, University of Caxias do Sul, Caxias do Sul, RS 95070-560, Brazil

Human Centered Systems, Honeywell Laboratories, Minneapolis, MN 55401, USA

Received 14 February 2007; Accepted 18 June 2007

Academic Editor: Andrzej Cichocki

Copyright © 2007 Tian Lan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.





Author: Tian Lan, Deniz Erdogmus, Andre Adami, Santosh Mathan, and Misha Pavel

Source: https://www.hindawi.com/



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