FuRIA: An Inverse Solution Based Feature Extraction Algorithm Using Fuzzy Set Theory for Brain–Computer Interfaces

This paper presents FuRIA, a trainable feature extraction algorithm for noninvasive brain-computer interfaces (BCI). FuRIA is based on inverse solutions and on the new concepts of fuzzy region of interest (ROI) and fuzzy frequency band. FuRIA can automatically identify the relevant ROI and frequency bands for the discrimination of mental states, even for multiclass BCI. Once identified, the activity in these ROI and frequency bands can be used as features for any classifier. The evaluations of FuRIA showed that the extracted features were interpretable and can lead to high classification accuracies.