Conference paper
FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification: A Machine Learning Approach
Brain Computer Interface, Digital Health, Department of Health Technology, Technical University of Denmark1
Digital Health, Department of Health Technology, Technical University of Denmark2
Department of Health Technology, Technical University of Denmark3
Technical University of Denmark4
Copenhagen University Hospital Herlev and Gentofte5
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 subbands for the MI EEG data, in order to find the best sub-band which contains the most significant features for distinguishing different MI tasks.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2020 |
| Pages: | 1275-1279 |
| Proceedings: | 2020 IEEE International Conference on Systems, Man, and Cybernetics |
| ISBN: | 1728185262 , 1728185270 , 9781728185262 and 9781728185279 |
| ISSN: | 1062922x and 25771655 |
| Types: | Conference paper |
| DOI: | 10.1109/SMC42975.2020.9283098 |
| ORCIDs: | Das, Rig , Khan, Muhammad Ahmed and Puthusserypady, Sadasivan |
