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

Lan, Tian; Erdogmus, Deniz; Adami, Andre; Mathan, Santosh; Pavel, Misha

doi:https://doi.org/10.1155/2007/74895

Computational Intelligence and Neuroscience

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Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

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Research Article | Open Access

Volume 2007 | Article ID 074895 | https://doi.org/10.1155/2007/74895

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

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

Academic Editor: Andrzej Cichocki

Received14 Feb 2007

Accepted18 Jun 2007

Published19 Aug 2007

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.

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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.

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