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 1-60Hz. The classification rates are statistically significant and the best classification rates, close to 90%, are obtained using conditional entropy features.
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Wu, L., Neskovic, P. (2007). Classifying EEG Data into Different Memory Loads Across Subjects. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_16
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DOI: https://doi.org/10.1007/978-3-540-74695-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74693-5
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