Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based Brain-Computer Interfaces

Fabien Lotte 1, 2
2 Potioc - Popular interaction with 3d content
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
Abstract : One of the major limitations of Brain-Computer Interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a-priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose 3 different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from 3 different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.
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Article dans une revue
Proceedings of the IEEE, Institute of Electrical and Electronics Engineers, 2015, 103 (6), pp.871-890
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Dernière modification le : jeudi 11 janvier 2018 - 06:24:06
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Fabien Lotte. Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based Brain-Computer Interfaces. Proceedings of the IEEE, Institute of Electrical and Electronics Engineers, 2015, 103 (6), pp.871-890. 〈hal-01159171〉

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