Learning EEG-based Spectral-Spatial Patterns for Attention Level Measurement

Abstract : In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person's level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc. In this paper we propose a novel approach to extract, select and learn spectral-spatial patterns from electroencephalogram (EEG) recordings. Our approach improves over prior-art methods that was, typically, only concerned with power of specific EEG rhythms from few individual channels. In this new approach, spectral-spatial features from multichannel EEG are extracted by a two filtering stages: a filter-bank (FB) and common spatial patterns (CSP) filters. The most important features are selected by a Mutual Information (MI) based feature selection procedure and then classified using Fisher linear discriminant (FLD). The outcome is a measure of the attention level. An experimental study was conducted with 5 healthy young male subjects with their EEG recorded in various attention and non-attention conditions (opened eyes, closed eyes, reading, counting, relaxing, etc.). EEGs were used to train and evaluate the model using 4x4fold cross-validation procedure. Results indicate that the new proposed approach outperforms the prior-art methods and can achieve up to 89.4% classification accuracy rate (with an average improvement of up to 16%). We demonstrate its application with a two-players attention-based racing car computer game.
Type de document :
Communication dans un congrès
2009 IEEE International Symposium on Circuits and Systems (ISCAS2009), May 2009, Taipei, Taiwan. pp.1465-1468, 2009
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Contributeur : Brahim Hamadicharef <>
Soumis le : mercredi 16 décembre 2009 - 04:31:48
Dernière modification le : lundi 21 décembre 2009 - 09:59:24
Document(s) archivé(s) le : jeudi 17 juin 2010 - 21:52:02


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  • HAL Id : inria-00441412, version 1


Brahim Hamadicharef, Haihong Zhang, Cuntai Guan, Chuanchu Wang, Kok Soon Phua, et al.. Learning EEG-based Spectral-Spatial Patterns for Attention Level Measurement. 2009 IEEE International Symposium on Circuits and Systems (ISCAS2009), May 2009, Taipei, Taiwan. pp.1465-1468, 2009. 〈inria-00441412〉



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