FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification: A Machine Learning Approach

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification : A Machine Learning Approach. / Das, Rig; Lopez, Paula S.; Ahmed Khan, Muhammad; Iversen, Helle K.; Puthusserypady, Sadasivan.

2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. IEEE, 2020. p. 1275-1279 9283098.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Das, R, Lopez, PS, Ahmed Khan, M, Iversen, HK & Puthusserypady, S 2020, FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification: A Machine Learning Approach. in 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020., 9283098, IEEE, pp. 1275-1279, 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020, Toronto, Canada, 11/10/2020. https://doi.org/10.1109/SMC42975.2020.9283098

APA

Das, R., Lopez, P. S., Ahmed Khan, M., Iversen, H. K., & Puthusserypady, S. (2020). FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification: A Machine Learning Approach. In 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 (pp. 1275-1279). [9283098] IEEE. https://doi.org/10.1109/SMC42975.2020.9283098

Vancouver

Das R, Lopez PS, Ahmed Khan M, Iversen HK, Puthusserypady S. FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification: A Machine Learning Approach. In 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. IEEE. 2020. p. 1275-1279. 9283098 https://doi.org/10.1109/SMC42975.2020.9283098

Author

Das, Rig ; Lopez, Paula S. ; Ahmed Khan, Muhammad ; Iversen, Helle K. ; Puthusserypady, Sadasivan. / FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification : A Machine Learning Approach. 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. IEEE, 2020. pp. 1275-1279

Bibtex

@inproceedings{20c8fba159a84a20b3489dcc0a4bf430,
title = "FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification: A Machine Learning Approach",
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.",
keywords = "Adaptive boosting (AdaBoost), Brain computer interface (BCI), filter-bank common spatial patterns (FBCSP), motor imagery (MI)",
author = "Rig Das and Lopez, {Paula S.} and {Ahmed Khan}, Muhammad and Iversen, {Helle K.} and Sadasivan Puthusserypady",
note = "Funding Information: ACKNOWLEDGMENT We gratefully acknowledge the support of NVIDIA{\textregistered} Corporation, for providing the Titan X{\texttrademark} GPU that is used for this research. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 ; Conference date: 11-10-2020 Through 14-10-2020",
year = "2020",
doi = "10.1109/SMC42975.2020.9283098",
language = "English",
pages = "1275--1279",
booktitle = "2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - FBCSP and Adaptive Boosting for Multiclass Motor Imagery BCI Data Classification

T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020

AU - Das, Rig

AU - Lopez, Paula S.

AU - Ahmed Khan, Muhammad

AU - Iversen, Helle K.

AU - Puthusserypady, Sadasivan

N1 - 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.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Adaptive boosting (AdaBoost)

KW - Brain computer interface (BCI)

KW - filter-bank common spatial patterns (FBCSP)

KW - motor imagery (MI)

U2 - 10.1109/SMC42975.2020.9283098

DO - 10.1109/SMC42975.2020.9283098

M3 - Article in proceedings

AN - SCOPUS:85098885506

SP - 1275

EP - 1279

BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020

PB - IEEE

Y2 - 11 October 2020 through 14 October 2020

ER -

ID: 282089155