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Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI

Abstract : A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by a direct control from the decoding of brain activity. To improve the ergonomics and to minimize the cost of such a BCI, reducing the number of electrodes is mandatory. A theoretical analysis of the subjacent model induced by the BCI paradigm leads to derive a closed form theoretical expression of the spatial filters which maximize the signal to signal-plus-noise ratio. Moreover, this new formulation is useful to improve a previously introduced method to automatically select relevant sensors. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 8 sensors, the loss in classification accuracy is less than 2%. Furthermore, the computational time required to rank the 32 sensors is reduced by a 4.6 speed up factor allowing dynamical monitoring of sensor relevance as a marker of the user's mental state.
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Contributor : Bertrand Rivet Connect in order to contact the contributor
Submitted on : Wednesday, September 7, 2011 - 10:16:23 AM
Last modification on : Tuesday, December 21, 2021 - 3:48:50 AM
Long-term archiving on: : Thursday, December 8, 2011 - 2:22:44 AM


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  • HAL Id : hal-00619997, version 1


Bertrand Rivet, Hubert Cecotti, Antoine Souloumiac, Emmanuel Maby, Jérémie Mattout. Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI. EUSIPCO 2011 - 19th European Signal Processing Conference, Aug 2011, Barcelone, Spain. pp.1382-1386. ⟨hal-00619997⟩



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