The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach
Open accessAbstract
Independent component analysis (ICA) proves to be effective in the removing the ocular artifact from electroencephalogram recordings (EEG). While using ICA in ocular artifact correction, a crucial step is to correctly identify the artifact components among the decomposed independent components. In most previous works, this step of selecting the artifact components was manually implemented, which is time consuming and inconvenient when dealing with a large amount of EEG data. We present a new method which automatically selects the eye blink artifact components based on the pattern of their scalp topographies, which can be exemplified as a template matching approach. The feasibility of using a fixed template for singling out the eye blink component after ICA decomposition was validated by an experiment in which 18 subjects among the 21 subjects involved exhibited a highly consistent pattern of eye blink scalp topographies. Since only the spatial feature is employed for singling out the eye blink component, the proposed method is very efficient and easy to implement. Objective evaluation of the real results shows that the proposed algorithm can remove the eye blink artifact from the EEG while causing little distortion to the underlying brain activities.
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Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
A new method for off-line removal of ocular artifact
Gratton, G., Coles, M. G. H., & Donchin, E. (1983) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(83)90135-9 · Cited by 5,055 · cited 1×
“Among them the most popular approach is the regression-based method (Gratton et al 1983, Croft and Barry 2000a, 2000b), either in the time domain or in the frequency domain.”
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Cited by
Heart–brain interactions shape somatosensory perception and evoked potentials
Al, E., Iliopoulos, F., Forschack, N., Nierhaus, T., Grund, M., Motyka, P., Gaebler, M., Nikulin, V. V., & Villringer, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915629117 · Cited by 276
Deep learning based on Batch Normalization for P300 signal detection
Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., & Li, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.039 · Cited by 230
Self-citationDecoding Covert Speech From EEG-A Comprehensive Review
Panachakel, J. T., & Ramakrishnan, A. G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.642251 · Cited by 156
Signal Artifacts and Techniques for Artifacts and Noise Removal
Islam, M. K., Rastegarnia, A., & Sanei, S. (2021) · Intelligent Systems Reference Library · doi:10.1007/978-3-030-54932-9_2 · Cited by 54
Performance Enhancement of P300 Detection by Multiscale-CNN
Wang, H., Pei, Z., Xu, L., Xu, T., Bezerianos, A., Sun, Y., & Li, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3067943 · Cited by 35
Metadata
- Authors
- Li, Y., Ma, Z., Lu, W., & Li, Y.
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2006-03-14
- Volume
- 27
- Issue
- 4
- Pages
- 425-436
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2009782817
- Cited by
- 197
- References
- 2
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Li, Y., Ma, Z., Lu, W., & Li, Y. (2006). Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiological Measurement. https://doi.org/10.1088/0967-3334/27/4/008
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