Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems
Microelectronics Journal
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
P300 detection based on feature extraction in on-line brain-computer interface
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Decoding stimulus-reward pairing from local field potentials recorded from monkey visual cortex
IEEE Transactions on Neural Networks
Comparison of classification methods for P300 brain-computer interface on disabled subjects
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Hi-index | 0.00 |
In this paper, we report on the feasibility of the Error-Related Potential (ErrP) integration in a particular type of Brain-Computer Interface (BCI) called the P300 Mind Speller. With the latter, the subject can type text only by means of his/her brain activity without having to rely on speech or muscular activity. Hereto, electroencephalography (EEG) signals are recorded from the subject's scalp. But, as with any BCI paradigm, decoding mistakes occur, and when they do, an EEG potential is evoked, known as the Error-Related Potential (ErrP), locked to the subject's realization of the mistake. When the BCI would be able to also detect the ErrP, the last typed character could be automatically corrected. However, since the P300 Mind Speller is optimized to correctly operate in the first place, we have much less ErrP's than responses to correctly typed characters. In fact, exactly because it is supposed to be a rare phenomenon, we advocate that ErrP detection can be treated as a novelty detection problem. We consider in this paper different one-class classification algorithms based on novelty detection together with a correction algorithm for the P300 Mind Speller.