TY - GEN
T1 - Classification of motor imagery for Ear-EEG based brain-computer interface
AU - Kim, Yong Jeong
AU - Kwak, No Sang
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-TC1603-02.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8% and 68.07%, respectively, using the ear-around EEG signals (cf. 92.40% and 91.64% using motor-area EEG signals).
AB - Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8% and 68.07%, respectively, using the ear-around EEG signals (cf. 92.40% and 91.64% using motor-area EEG signals).
KW - brain-computer interface
KW - ear-EEG
KW - motor imagery
UR - http://www.scopus.com/inward/record.url?scp=85050819916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050819916&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2018.8311517
DO - 10.1109/IWW-BCI.2018.8311517
M3 - Conference contribution
AN - SCOPUS:85050819916
T3 - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
SP - 1
EP - 2
BT - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Brain-Computer Interface, BCI 2018
Y2 - 15 January 2018 through 17 January 2018
ER -