EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms
| Title: | EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
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استنادات
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| Author(s): | Zangeneh Soroush, M., Tahvilian, P., Nasirpour, M.H., (...), Ghazizadeh, A., Jafarnia Dabanloo, N. | |
| Published/Type: | 2022 (2022-8-24), Fetched: Dec 8, 2023 20:39:23 / Original Article | |
| Journal: | Frontiers in Physiology, 13,910368 | |
| Abstract:
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Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information... | |
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