Channel selection and feature projection for cognitive load estimation using ambulatory EEG

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

Research output: Contribution to journalArticle

43 Scopus citations

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.

Original languageEnglish (US)
Article number74895
JournalComputational Intelligence and Neuroscience
Volume2007
DOIs
StatePublished - 2007

ASJC Scopus subject areas

  • Computer Science(all)
  • Neuroscience(all)
  • Mathematics(all)

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