Abstract
Brain-Computer Interfaces (BCIs) are trained to distinguish between two (or more) mental states, e.g., left and right hand motor imagery, from the recorded brain signals. Common Spatial Patterns (CSP) is a popular method to optimally separate data from two motor imagery tasks under the assumption of an unimodal class distribution. In out of lab environments where users are distracted by additional noise sources this assumption may not hold. This paper systematically investigates BCI performance under such distractions and proposes two novel CSP variants, ensemble CSP and 2-step CSP, which can cope with multimodal class distributions. The proposed algorithms are evaluated using simulations and BCI data of 16 healthy participants performing motor imagery under 6 different types of distraction. Both methods are shown to significantly enhance the performance compared to the standard procedure.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3742-3747 |
Number of pages | 6 |
ISBN (Electronic) | 9781509018970 |
DOIs | |
Publication status | Published - 2017 Feb 6 |
Event | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary Duration: 2016 Oct 9 → 2016 Oct 12 |
Publication series
Name | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings |
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Other
Other | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 |
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Country/Territory | Hungary |
City | Budapest |
Period | 16/10/9 → 16/10/12 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Control and Optimization
- Human-Computer Interaction