Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2007
- Source ID
- 10.1155/2007/74895
Entities
People
- Andre Adami
- Deniz Erdoğmuş
- Misha Pavel
- Santosh Mathan
- Tian Lan
Organizations
- Defense Advanced Research Projects Agency