Abstract
This paper is a compilation of the most recent machine learning methods used in the Berlin Brain-Computer Interface. In the field of Brain-Computer Interfacing, machine learning has been mainly used to extract meaningful features from noisy signals of large dimensionality and to classify them to transform them into computer commands. Recently, our group developed different methods to deal with noisy, non-stationary and high dimensional signals. These approaches can be seen as variants of the algorithm Common Spatial Patterns (CSP). All of them outperform CSP in the different conditions for which they were developed.
| Original language | English |
|---|---|
| Pages (from-to) | 447-452 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 28 |
| Issue number | 20 |
| DOIs | |
| Publication status | Published - 2015 Sep 1 |
Keywords
- Adaptive systems
- Brain-computer interfacing
- Electroencephalogram
- Motor imagery
- Multimodal analysis
- Non-stationary analysis
ASJC Scopus subject areas
- Control and Systems Engineering
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Cite this
Vidaurre, C., Sannelli, C., Samek, W., Dähne, S., & Muller, K. (2015). Machine learning methods of the Berlin brain-computer interface. IFAC-PapersOnLine, 28(20), 447-452. https://doi.org/10.1016/j.ifacol.2015.10.181