TY - GEN
T1 - Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score
AU - Shin, Jinhyo
AU - Chung, Wonzoo
N1 - Funding Information:
Acknowledgements. This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development Of Non-invasive Integrated BCI SW Platform To Control Home Appliance And External Devices By User’s Thought Via AR/VR Interface), Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program(Korea University)).
Funding Information:
This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development Of Non-invasive Integrated BCI SW Platform To Control Home Appliance And External Devices By User’s Thought Via AR/VR Interface), Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Pro-gram(Korea University)).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this paper, we propose a novel motor imagery (MI) classification method in electroencephalogram (EEG)-based brain-computer interface (BCI) using local log Riemannian distance matrices (LRDM). The proposed method selects optimal local LRDM based on confusion area score which is designed to minimize the overlap between the class-dependent distributions of Riemannian distance between local covariance matrices that are generated from adjacent (local) channels centered on each channel. A feature vector is formed by concatenating vectorized selected local LRDM and used as input to support vector machine (SVM) in order to classify motor imagery. The performance of the proposed method is evaluated using BCI Competition III dataset IVa and BCI Competition IV dataset I. The results confirm performance improvement of the proposed method compared to existing MI classification methods.
AB - In this paper, we propose a novel motor imagery (MI) classification method in electroencephalogram (EEG)-based brain-computer interface (BCI) using local log Riemannian distance matrices (LRDM). The proposed method selects optimal local LRDM based on confusion area score which is designed to minimize the overlap between the class-dependent distributions of Riemannian distance between local covariance matrices that are generated from adjacent (local) channels centered on each channel. A feature vector is formed by concatenating vectorized selected local LRDM and used as input to support vector machine (SVM) in order to classify motor imagery. The performance of the proposed method is evaluated using BCI Competition III dataset IVa and BCI Competition IV dataset I. The results confirm performance improvement of the proposed method compared to existing MI classification methods.
KW - Brain-computer interface (BCI)
KW - Electroencephalogram (EEG)
KW - Local feature
KW - Log Riemannian distance matrix
KW - Motor imagery (MI)
UR - http://www.scopus.com/inward/record.url?scp=85130396570&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-02375-0_12
DO - 10.1007/978-3-031-02375-0_12
M3 - Conference contribution
AN - SCOPUS:85130396570
SN - 9783031023743
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 169
BT - Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers
A2 - Wallraven, Christian
A2 - Liu, Qingshan
A2 - Nagahara, Hajime
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th Asian Conference on Pattern Recognition, ACPR 2021
Y2 - 9 November 2021 through 12 November 2021
ER -