Abstract
This paper proposes an efficient approach, based on independent component analysis (ICA) and wavelet denoising (WD), for automatic removal of ocular artifacts from EEG data. In the proposed approach, we employ the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the EEG data, use WD to remove any cerebral activity from extracted ICs to obtain artifact-only components, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as it is not necessary to identify all ICs. Computer experiments are carried out for long term EEG, which demonstrate the effectiveness of the proposed approach.
| Original language | English |
|---|---|
| Pages | 927-930 |
| Number of pages | 4 |
| Publication status | Published - Dec 1 2010 |
| Event | 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 - Biopolis, Singapore Duration: Dec 14 2010 → Dec 17 2010 |
Conference
| Conference | 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 |
|---|---|
| Country | Singapore |
| City | Biopolis |
| Period | 12/14/10 → 12/17/10 |
Fingerprint
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
Cite this
An efficient approach for automatic removal of ocular artifacts from multichannel EEG data. / Akhtar, Muhammad Tahir; Mitsuhashi, Wataru; James, Christopher J.
2010. 927-930 Paper presented at 2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010, Biopolis, Singapore.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - An efficient approach for automatic removal of ocular artifacts from multichannel EEG data
AU - Akhtar, Muhammad Tahir
AU - Mitsuhashi, Wataru
AU - James, Christopher J.
PY - 2010/12/1
Y1 - 2010/12/1
N2 - This paper proposes an efficient approach, based on independent component analysis (ICA) and wavelet denoising (WD), for automatic removal of ocular artifacts from EEG data. In the proposed approach, we employ the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the EEG data, use WD to remove any cerebral activity from extracted ICs to obtain artifact-only components, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as it is not necessary to identify all ICs. Computer experiments are carried out for long term EEG, which demonstrate the effectiveness of the proposed approach.
AB - This paper proposes an efficient approach, based on independent component analysis (ICA) and wavelet denoising (WD), for automatic removal of ocular artifacts from EEG data. In the proposed approach, we employ the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the EEG data, use WD to remove any cerebral activity from extracted ICs to obtain artifact-only components, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as it is not necessary to identify all ICs. Computer experiments are carried out for long term EEG, which demonstrate the effectiveness of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=79958112548&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79958112548&partnerID=8YFLogxK
M3 - Paper
SP - 927
EP - 930
ER -