@inproceedings{59a940f93f954854bcddf98154fe1c63,
title = "Detecting voluntary gait initiation/termination intention using EEG",
abstract = "In this study, we employed a linear classifier to grasp the abstract features of electroencephalography (EEG) for recognizing voluntary gait intention and termination. We monitored Mu-band EEG to find gait intention and tried to detect a movement on/offset. Considerable gait-related (de) synchronization occurred hence, amplified by common spatial pattern (CSP). Performance of the classifier was evaluated in terms of classification success rates and false positive rates.",
keywords = "ASR, CSP, EEG, LDA, gait intention",
author = "Junhyuk Choi and Lee, {Song Joo} and Kim, {Seung Jong} and Lee, {Jong Min} and Hyungmin Kim",
note = "Funding Information: ACKNOWLEDGMENT This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2017-0-00432) Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th International Conference on Brain-Computer Interface, BCI 2018 ; Conference date: 15-01-2018 Through 17-01-2018",
year = "2018",
month = mar,
day = "9",
doi = "10.1109/IWW-BCI.2018.8311532",
language = "English",
series = "2018 6th International Conference on Brain-Computer Interface, BCI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--3",
booktitle = "2018 6th International Conference on Brain-Computer Interface, BCI 2018",
}