Abstract
Classification of non-stationary electroencephalogram (EEG) data are of utmost importance for brain-computer interface (BCI) technology. This paper proposes a robust multiclass motor imagery (MI) BCI data classification technique. It is based on filter bank common spatial patterns (FBCSP) and AdaBoost classification technique. The method is tested on the 4-class MI BCI competition IV dataset 2a and the results show superior performance compared to the current state-of-the-art performances. This paper also analyzes different frequency sub-bands for the MI EEG data, in order to find the best sub-band which contains the most significant features for distinguishing different MI tasks.
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
| Number of pages | 5 |
| Publisher | IEEE |
| Publication date | 2020 |
| Pages | 1275-1279 |
| Article number | 9283098 |
| ISBN (Electronic) | 9781728185262 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada Duration: 11 Oct 2020 → 14 Oct 2020 |
Conference
| Conference | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
|---|---|
| Country/Territory | Canada |
| City | Toronto |
| Period | 11/10/2020 → 14/10/2020 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT We gratefully acknowledge the support of NVIDIA® Corporation, for providing the Titan X™ GPU that is used for this research.
Publisher Copyright:
© 2020 IEEE.
Keywords
- Adaptive boosting (AdaBoost)
- Brain computer interface (BCI)
- filter-bank common spatial patterns (FBCSP)
- motor imagery (MI)