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Subject independent EEG-based BCI decoding

: Fazli, Siamac; Grozea, Cristian; Danoszy, Marton; Blankertz, Benjamin; Popescu, Florin; Müller, Klaus-Robert

Bengio, Y. ; Neural Information Processing Systems -NIPS- Foundation:
Advances in neural information processing systems 22. Vol.1 : 23rd Annual Conference on Neural Information Processing Systems 2009; December 7 - 10, 2009, Vancouver, B.C., Canada; Proceedings
Red Hook, NY: Curran, 2010 (Advances in neural information processing systems 22 1)
ISBN: 978-1-61567-911-9
Annual Conference on Neural Information Processing Systems (NIPS) <23, 2009, Vancouver>
Conference Paper
Fraunhofer FIRST ()

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.