摘要：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.
