Neuro-fuzzy Classifier to Recognize Mental Tasks in a BCI

This paper presents the first results of online classification using a model based on a neuro-fuzzy architecture called S-dFasArt, in order to recognize in real time and with sufficient reliability between two mental tasks in a Brain Computer Interface (BCI). Spontaneous brain activity recorded with non-invasive techniques and processed through the Fast Fourier Transform (FFT) has been used to test the classifier. The dynamic characteristics and the ability of the online classification of neuro-fuzzy algorithm make it very suitable to interpret EEG signals. The classifier designed is based on the creation and combination of diferent classification models S-dFasArt, alterning sessions of EEG signals in the adjustment phase and performing a complete study to find the best values of the classifier parameters. In the paper, the adjustment phase of each classification model has been described. New voting strategies and levels of uncertainty have been incorporated to improve the success rate in the online classification. The experimental results with different users have been reported.