Fuzzy sets, fuzzy logic, and fuzzy systems
The scientist and engineer's guide to digital signal processing
The scientist and engineer's guide to digital signal processing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The estimation of cortical activity for brain-computer interface: applications in a domotic context
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Presence: Teleoperators and Virtual Environments
A comprehensive survey of the feature extraction methods in the EEG research
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Hi-index | 35.68 |
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 dliscrimination 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.