Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain-Computer Interfaces

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 el...

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Veröffentlicht in:Proceedings of the IEEE Jg. 103; H. 6; S. 871 - 890
1. Verfasser: Lotte, Fabien
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
Band-pass filters
Biotechnology
Brain-computer interfaces
Brain-computer interfaces (BCI)
Calibration
Computer Science
Covariance matrices
Electroencephalography
electroencephalography (EEG)
Machine Learning
Signal and Image Processing
signal processing
Signal processing algorithms
small sample settings
Spatial filters
Statistics
Training data
electroencephalography (EEG)
small sample settings
Brain–computer interfaces (BCI)
machine learning
signal processing
calibration
ElectroEncephaloGraphy (EEG)
Index Terms—Brain-Computer Interfaces (BCI)
ISSN:0018-9219, 1558-2256
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Zusammenfassung: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 three 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 three different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2015.2404941