Abstract
In this work, a new approach for joint blind source separation (BSS) of datasets at multiple time lags using canonical correlation analysis (CCA) is developed for removing muscular artifacts from electroencephalogram (EEG) recordings. The proposed approach jointly extracts sources from each dataset in a decreasing order of between-set source correlations. Muscular artifact sources that typically have lowest between-set correlations can then be removed. It is shown theoretically that the proposed use of CCA on multiple datasets at multiple time lags achieves better BSS under a more relaxed condition and hence offers better performance in removing muscular artifacts than the conventional CCA. This is further demonstrated by experiments on real EEG data.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
| Pages | 6792-6795 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2013 |
| Externally published | Yes |
| Event | 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan Duration: Jul 3 2013 → Jul 7 2013 |
Other
| Other | 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 |
|---|---|
| Country | Japan |
| City | Osaka |
| Period | 7/3/13 → 7/7/13 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics
Cite this
Multiple time-lag canonical correlation analysis for removing muscular artifacts in EEG. / Shen, Kaiquan; Yu, Ke; Bandla, Aishwarya; Sun, Yu; Thakor, Nitish V; Li, Xiaoping.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2013. p. 6792-6795 6611116.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiple time-lag canonical correlation analysis for removing muscular artifacts in EEG
AU - Shen, Kaiquan
AU - Yu, Ke
AU - Bandla, Aishwarya
AU - Sun, Yu
AU - Thakor, Nitish V
AU - Li, Xiaoping
PY - 2013
Y1 - 2013
N2 - In this work, a new approach for joint blind source separation (BSS) of datasets at multiple time lags using canonical correlation analysis (CCA) is developed for removing muscular artifacts from electroencephalogram (EEG) recordings. The proposed approach jointly extracts sources from each dataset in a decreasing order of between-set source correlations. Muscular artifact sources that typically have lowest between-set correlations can then be removed. It is shown theoretically that the proposed use of CCA on multiple datasets at multiple time lags achieves better BSS under a more relaxed condition and hence offers better performance in removing muscular artifacts than the conventional CCA. This is further demonstrated by experiments on real EEG data.
AB - In this work, a new approach for joint blind source separation (BSS) of datasets at multiple time lags using canonical correlation analysis (CCA) is developed for removing muscular artifacts from electroencephalogram (EEG) recordings. The proposed approach jointly extracts sources from each dataset in a decreasing order of between-set source correlations. Muscular artifact sources that typically have lowest between-set correlations can then be removed. It is shown theoretically that the proposed use of CCA on multiple datasets at multiple time lags achieves better BSS under a more relaxed condition and hence offers better performance in removing muscular artifacts than the conventional CCA. This is further demonstrated by experiments on real EEG data.
UR - http://www.scopus.com/inward/record.url?scp=84886514601&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886514601&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2013.6611116
DO - 10.1109/EMBC.2013.6611116
M3 - Conference contribution
C2 - 24111303
AN - SCOPUS:84886514601
SN - 9781457702167
SP - 6792
EP - 6795
BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ER -