Abstract
This paper concerns artifact removal from multichannel EEG data. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any cerebral activity from extracted artifacts, and Anally 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 all ICs are not identified. The computer experiments are carried out, which demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.
| Original language | English |
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| Title of host publication | 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 - Chengdu, China Duration: Jun 18 2010 → Jun 20 2010 |
Publication series
| Name | 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 |
|---|
Conference
| Conference | 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 |
|---|---|
| Country | China |
| City | Chengdu |
| Period | 6/18/10 → 6/20/10 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
Cite this
Modifying the spatially-constrained ICA for efficient removal of artifacts from EEG data. / Akhtar, Muhammad Tahir; James, Christopher J.; Mitsuhashi, Wataru.
2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010. 2010. 5515306 (2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Modifying the spatially-constrained ICA for efficient removal of artifacts from EEG data
AU - Akhtar, Muhammad Tahir
AU - James, Christopher J.
AU - Mitsuhashi, Wataru
PY - 2010
Y1 - 2010
N2 - This paper concerns artifact removal from multichannel EEG data. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any cerebral activity from extracted artifacts, and Anally 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 all ICs are not identified. The computer experiments are carried out, which demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.
AB - This paper concerns artifact removal from multichannel EEG data. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any cerebral activity from extracted artifacts, and Anally 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 all ICs are not identified. The computer experiments are carried out, which demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.
UR - http://www.scopus.com/inward/record.url?scp=77956146728&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956146728&partnerID=8YFLogxK
U2 - 10.1109/ICBBE.2010.5515306
DO - 10.1109/ICBBE.2010.5515306
M3 - Conference contribution
SN - 9781424447138
T3 - 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
BT - 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
ER -