Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities

Abstract Objective Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. Methods We present a new met...

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Bibliographic Details
Published in:Clinical neurophysiology 2008-04, Vol.119 (4), p.897-908
Main Authors: Besserve, Michel, Philippe, Matthieu, Florence, Geneviève, Laurent, François, Garnero, Line, Martinerie, Jacques
Format: Article
Language:English
Subjects:
Algorithms
Behavioral psychophysiology
Biological and medical sciences
Brain - physiology
Brain Mapping
Classification
Cognition - physiology
Computer Science
Electrodiagnosis. Electric activity recording
Electroencephalography
Electrophysiology
Fundamental and applied biological sciences. Psychology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Medical Imaging
Medical sciences
Nervous system
Neurology
Phase locking value
Phase synchrony
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Support vector machine
Task Performance and Analysis
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Summary:Abstract Objective Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. Methods We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject. Results The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49 bit/min. The main discriminating activities are laterally distributed theta power and anterio–posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance. Conclusions Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative. Significance Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes.
ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2007.12.003