@inproceedings{d9320c38e6cf4420ba08bb536169b4d9,
title = "Brain computer interface approach using sensor covariance matrix with forced whitening",
abstract = "In this paper, we present a novel motor imagery classification method in electroencephalogmphy (EEG)-based Bmin-Computer lnterfaces (BCIs) using forced whitened sampIe covariance matdces as features. The proposed method performs a constant-forcing to the weaker sources of covadance matrices before a whitening process to prevent amplifications of noise sources which have small power relative to class relevant sources. Expedmental results show the improved accuracy in comparison with a classification without forced whitening process.",
keywords = "Brain-computer interface (BCI), Classification, Sensor covariance matrix, Supporting vector machine (SVM), Whitening matrix",
author = "Hyuksoo Shin and Wonzoo Chung",
year = "2017",
month = feb,
day = "16",
doi = "10.1109/IWW-BCI.2017.7858161",
language = "English",
series = "5th International Winter Conference on Brain-Computer Interface, BCI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "66--68",
booktitle = "5th International Winter Conference on Brain-Computer Interface, BCI 2017",
note = "5th International Winter Conference on Brain-Computer Interface, BCI 2017 ; Conference date: 09-01-2017 Through 11-01-2017",
}