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.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Lan T,Erdogmus D,Adami A,Mathan S,Pavel Mdoi
10.1155/2007/74895subject
Has Abstractpub_date
2007-01-01 00:00:00pages
74895eissn
1687-5265issn
1687-5273pub_type
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