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ICANN
2007
Springer

Classifying EEG Data into Different Memory Loads Across Subjects

11 years 3 months ago
Classifying EEG Data into Different Memory Loads Across Subjects
Abstract. In this paper we consider the question of whether it is possible to classify n-back EEG data into different memory loads across subjects. To capture relevant information from the EEG signal we use three types of features: power spectrum, conditional entropy, and conditional mutual information. In order to reduce irrelevant and misleading features we use a feature selection method that maximizes mutual information between features and classes and minimizes redundancy among features. Using a selected group of features we show that all classifiers can successfully generalize to the new subject for bands 1-40Hz and 160Hz. The classification rates are statistically significant and the best classification rates, close to 90%, are obtained using conditional entropy features.
Liang Wu, Predrag Neskovic
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where ICANN
Authors Liang Wu, Predrag Neskovic
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