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Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry
Abstract : The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.
Keywords :
Coherence in biomedical signal processing
Multivariate signal processing
Machine Learning
Biosignals classification
Electroencephalography
Multi-User BCI
Hyperscanning
Minimum To Mean Distance
Riemannian Geometry
Classification
EEG
Brain-Computer Interface
Biomedical signal classification
BCI
Cited literature [11 references]
https://hal.archives-ouvertes.fr/hal-01191913
Contributor : Louis Korczowski Connect in order to contact the contributor
Submitted on : Thursday, September 3, 2015 - 5:16:29 PM
Last modification on : Tuesday, October 19, 2021 - 11:22:37 PM
Long-term archiving on: : Friday, December 4, 2015 - 10:27:28 AM
Contributor : Louis Korczowski Connect in order to contact the contributor
Submitted on : Thursday, September 3, 2015 - 5:16:29 PM
Last modification on : Tuesday, October 19, 2021 - 11:22:37 PM
Long-term archiving on: : Friday, December 4, 2015 - 10:27:28 AM
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