On the classification of mental tasks: a performance comparison of neural and statistical approaches

Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior can be used to diagnose anomalies in brain activity. The goal of this paper is to find a concise representation of EEG data, corresponding to 5 mental tasks performed by different individuals, for classification purposes. For that, we propose the use of Welch's periodogram as a powerful feature extractor and compare the performance of SOM-and MLP-based neural classifiers with that of standard Bayes optimal classifier. The results show that the Welch's periodogram allow all the classifiers to achieve higher classification rates (73%-100%) than those presented so far in the literature (les 71%)

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