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This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.

In this letter, an improvement of the recently developed neighborhood-based Levenberg-Marquardt (NBLM) algorithm is proposed and tested for neural network (NN) training. The algorithm is modified by allowing local adaptation of a different learning coefficient for each neighborhood. This simple add-in to the NBLM training method significantly increases the efficiency of the training episodes carried out with small neighborhood sizes, thus, allowing important savings in memory occupation and computational time while obtaining better performance than the original Levenberg-Marquardt (LM) and NBLM methods.

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