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Conference Papers Year : 2015

Data augmentation in Riemannian space for Brain-Computer Interfaces


Brain-Computer Interfaces (BCI) try to interpret brain signals , such as EEG , to issue some command or to characterize the cognitive states of the subjects. A strong limitation is that BCI tasks require a high concentration of the user , de facto limiting the length of experiment and the size of the dataset. Furthermore , several BCI paradigms depend on rare events , as for event-related potentials , also reducing the number of training examples available. A common strategy in machine learning when dealing with scarce data is called data augmentation ; new samples are generated by applying chosen transformations on the original dataset. In this contribution , we propose a scheme to adapt data augmentation in EEG-based BCI with a Riemannian standpoint : geometrical properties of EEG covariance matrix are taken into account to generate new training samples. Neural network are good candidates to benefit from such training scheme and a simple multi-layer perceptron offers good results . Experimental validation is conducted on two datasets : an SSVEP experiment with few training samples in each class and an error potential experiment with unbalanced classes (NER Kaggle competition) .
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hal-01351990 , version 1 (05-08-2016)


  • HAL Id : hal-01351990 , version 1


Emmanuel Kalunga, Sylvain Chevallier, Quentin Barthélemy. Data augmentation in Riemannian space for Brain-Computer Interfaces. STAMLINS, Jun 2015, Lille, France. ⟨hal-01351990⟩
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