Feasibility of Error-Related Potential Detection as Novelty Detection Problem in P300 Mind Spelling
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.
KeywordsAmyotrophic Lateral Sclerosis Novelty Detection Outlier Class Mistake Rate P300 Speller
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- 1.Mak, J.N., Wolpaw, J.R.: Clinical applications of brain-computer interfaces: current state and future prospects. IEEE Reviews in Biomedical Engineering 2, 187–199 (2009)Google Scholar
- 2.Manyakov, N.V., Chumerin, N., Combaz, A., Van Hulle, M.M.: Comparison of classification methods for P300 Brain-Computer Interface on disabled subjects. Computational Intelligence and Neuroscience 2011, Article ID 519868, 1–12 (2011)Google Scholar
- 7.Birbaumer, N., Kübler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., Iversen, I., Kotchoubey, B., Neumann, N., Flor, H.: The Thought Translation Device (TTD) for Completely Paralyzed Patients. IEEE Transactions on Rehabilitation Egineering 8(2), 190–193 (2000)CrossRefGoogle Scholar
- 10.Combaz, A., Manyakov, N.V., Chumerin, N., Suykens, J.A.K., Van Hulle, M.M.: Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling. In: Proc. International Conference on Machine Learning and Applications, pp. 386–391 (2009)Google Scholar
- 12.Luck, S.: An Introduction to the Event-Related Potential Technique. MIT Press, Cambridge (2005)Google Scholar
- 14.Combaz, A., Chumerin, N., Manyakov, N.V., Robben, A., Suykens, J.A.K., Van Hulle, M.M.: Error-related Potential recorded by EEG in the context of a P300 Mind Speller Brain-Computer Interface. In: Proc. IEEE International Workshop on Machine Learning for Signal Processing, pp. 65–70 (2010)Google Scholar
- 15.Chumerin, N., Manyakov, N.V., Combaz, A., Suykens, J.A.K., Yazicioglu, R.F., Torfs, T., Merken, P., Neves, H.P., Van Hoof, C., Van Hulle, M.M.: P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS (LNAI), vol. 5803, pp. 339–346. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 17.Tax, D.M.J.: One-class classification, PhD thesis, p. 202 (2001)Google Scholar
- 18.Ypma, A., Duin, R.: Support objects for domain approximation. In: ICANN 1998, pp. 2–4 (1998)Google Scholar
- 19.Japkowicz, N., Myers, C., Gluck, M.: A novelty detection approach to classification. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 518–523 (1995)Google Scholar
- 20.Tax, D.M.J.: DDtools, the Data Description Toolbox for Matlab, ver. 1.9.0 (2011)Google Scholar