Subject independent EEG-based BCI decoding

Siamac Fazli; Cristian Grozea; Márton Danóczy; Florin Popescu; Benjamin Blankertz; Klaus Robert Müller

Subject independent EEG-based BCI decoding

Siamac Fazli, Cristian Grozea, Márton Danóczy, Florin Popescu, Benjamin Blankertz, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an electrode cap. Another time consuming step is the required individualized adaptation to the BCI user, which involves another 30 minutes calibration for assessing a subject's brain signature. In this paper we aim to also remove this calibration proceedure from BCI setup time by means of machine learning. In particular, we harvest a large database of EEG BCI motor imagination recordings (83 subjects) for constructing a library of subject-specific spatio-temporal filters and derive a subject independent BCI classifier. Our offline results indicate that BCI-naïve users could start real-time BCI use with no prior calibration at only a very moderate performance loss.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Publisher	Neural Information Processing Systems
Pages	513-521
Number of pages	9
ISBN (Print)	9781615679119
Publication status	Published - 2009
Event	23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Vancouver, BC, Canada Duration: 2009 Dec 7 → 2009 Dec 10

Publication series

Name	Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Other

Other	23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
Country/Territory	Canada
City	Vancouver, BC
Period	09/12/7 → 09/12/10

ASJC Scopus subject areas

Information Systems

Cite this

Fazli, S., Grozea, C., Danóczy, M., Popescu, F., Blankertz, B., & Müller, K. R. (2009). Subject independent EEG-based BCI decoding. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 513-521). (Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference). Neural Information Processing Systems.

Subject independent EEG-based BCI decoding. / Fazli, Siamac; Grozea, Cristian; Danóczy, Márton et al.
Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. Neural Information Processing Systems, 2009. p. 513-521 (Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fazli, S, Grozea, C, Danóczy, M, Popescu, F, Blankertz, B & Müller, KR 2009, Subject independent EEG-based BCI decoding. in Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference, Neural Information Processing Systems, pp. 513-521, 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009, Vancouver, BC, Canada, 09/12/7.

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AB - In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an electrode cap. Another time consuming step is the required individualized adaptation to the BCI user, which involves another 30 minutes calibration for assessing a subject's brain signature. In this paper we aim to also remove this calibration proceedure from BCI setup time by means of machine learning. In particular, we harvest a large database of EEG BCI motor imagination recordings (83 subjects) for constructing a library of subject-specific spatio-temporal filters and derive a subject independent BCI classifier. Our offline results indicate that BCI-naïve users could start real-time BCI use with no prior calibration at only a very moderate performance loss.

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Subject independent EEG-based BCI decoding

Abstract

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ASJC Scopus subject areas

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