ON THE CLASSIFICATION OF MENTAL TASKS: A PERFORMANCE COMPARISON OF NEURAL AND STATISTICAL APPROACHES Guilherme A. Barreto, Rewbenio A. Frota and F´ atima N. S. de Medeiros Department of Teleinformatics Engineering, Federal University of Cear´ a Campus do Pici, 60455-760, Fortaleza, Cear´ a, Brazil Phone: +55 85 288 9467, Fax: +55 85 288 9468 E-mails: rewbenio, fsombra, guilherme@deti.ufc.br Abstract. Electroencephalogram (EEG) signals represent an im- portant 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 opti- mal classifier. The results show that the Welch’s periodogram allow all classifiers to achieve higher classification rates (73%-100%) than those presented so far in the literature (≤ 71%). 1. INTRODUCTION The EEG signal is a useful tool in medical clinic and research. For instance, it can be used to determine the global activity of the cerebral cortex and, to some extent, to locate abnormal activity in relatively small cortical areas. It also serves as an important auxiliary source of information for the diagnosis of sleep disturbances and epilepsy, and to differentiate between coma and brain death [9]. In engineering-oriented scenarios, EEG signals are used for the classification of mental tasks performed by subjects [3, 2, 12] and the design of man-machine interfaces [11, 12]. For a suitable utilization by the aforementioned applications, it is worth having a good representation of EEG data, which have been obtained, for example, by principal component analysis [15], autoregressive (AR) models [2], wavelet transform [4] and power spectral density (PSD) analysis [12, 7]. All of them have provided acceptable results in extracting and classifying different patterns from EEG signals. However, especially for the discrimination of several mental tasks, the classification rates are not satisfactory. This is mainly due to the noisy and