The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal
Abstract
One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts - non-brain signals. Among existing automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and realtime capable, component-based method that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameter have not been evaluated and reported, especially on real EEG data. This study systematically validates ASR on ten EEG recordings in a simulated driving experiment. Independent component analysis (ICA) is applied to separate artifacts from brain signals to allow a quantitative assessment of ASR's effectiveness in removing various types of artifacts and preserving brain activities. Empirical results show that the optimal ASR parameter is between 10 and 100, which is small enough to remove activities from artifacts and eye-related components and large enough to retain signals from brain-related components. With the appropriate choice of the parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.
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Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Independent EEG Sources Are Dipolar
Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030135 · Cited by 810
Cited by
Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T.-P. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2930186 · Cited by 544
Self-citationŠkola, F., Tinková, S., & Liarokapis, F. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00329 · Cited by 102
Spontaneous dyadic behavior predicts the emergence of interpersonal neural synchrony
Koul, A., Ahmar, D., Iannetti, G. D., & Novembre, G. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120233 · Cited by 75
“Based n the comparative results from ( Chang et al., 2018 ), we used a threshld value of 10.”
BackgroundMethodInfluentialPrediction of gait intention from pre-movement EEG signals: a feasibility study
Shafiul Hasan, S. M., Siddiquee, M. R., Atri, R., Ramon, R., Marquez, J. S., & Bai, O. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00675-5 · Cited by 63
Asynchronous Prediction of Human Gait Intention in a Pseudo Online Paradigm Using Wavelet Transform
Hasan, S. M. S., Siddiquee, M. R., & Bai, O. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2998778 · Cited by 22
Metadata
- Authors
- Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T.-P.
- Venue
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2018-07
- Volume
- 2018
- Pages
- 1242-1245
- OpenAlex
- W2899414240
- Cited by
- 335
- References
- 2
- FWCI
- 58.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T.-P. (2018). Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2018.8512547
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