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.
| Original language | English |
|---|---|
| Title of host publication | 2018 6th International Conference on Brain-Computer Interface, BCI 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-3 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781538625743 |
| DOIs | |
| Publication status | Published - 2018 Mar 9 |
| Externally published | Yes |
| Event | 6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of Duration: 2018 Jan 15 → 2018 Jan 17 |
Publication series
| Name | 2018 6th International Conference on Brain-Computer Interface, BCI 2018 |
|---|---|
| Volume | 2018-January |
Other
| Other | 6th International Conference on Brain-Computer Interface, BCI 2018 |
|---|---|
| Country | Korea, Republic of |
| City | GangWon |
| Period | 18/1/15 → 18/1/17 |
Fingerprint
Keywords
- ASR
- CSP
- EEG
- gait intention
- LDA
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Behavioral Neuroscience
Cite this
Detecting voluntary gait initiation/termination intention using EEG. / Choi, Junhyuk; Lee, Song Joo; Kim, Seung-Jong; Lee, Jong Min; Kim, Hyungmin.
2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-3 (2018 6th International Conference on Brain-Computer Interface, BCI 2018; Vol. 2018-January).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detecting voluntary gait initiation/termination intention using EEG
AU - Choi, Junhyuk
AU - Lee, Song Joo
AU - Kim, Seung-Jong
AU - Lee, Jong Min
AU - Kim, Hyungmin
PY - 2018/3/9
Y1 - 2018/3/9
N2 - 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.
AB - 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.
KW - ASR
KW - CSP
KW - EEG
KW - gait intention
KW - LDA
UR - http://www.scopus.com/inward/record.url?scp=85050797909&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050797909&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2018.8311532
DO - 10.1109/IWW-BCI.2018.8311532
M3 - Conference contribution
T3 - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
SP - 1
EP - 3
BT - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -