Key result
Current EEG artifact removal methods lack a universal best strategy and require further accuracy improvements.
Why the study?
EEG signals have very low amplitude and are easily corrupted by artifacts like ECG, EOG, EMG, and motion, making signal analysis challenging.
This survey highlights various EEG artifact removal methods, emphasizing that no single technique is optimal for all artifact types and suggesting future integration with machine learning.
Current EEG artifact removal lacks a universal solution, warranting caution in applications; leaves open machine learning integration for improved accuracy.
An Electroencephalogram (EEG) is produced as a consequence of the electrical voltage of neurons in the brain. The EEG signal is crucial for detecting brain activity and attitude. Because this signal has very low amplitude, it is easily corrupted by different artefacts. The study and analysis of brain signals in the presence of these artifacts is a challenging task. ECG, EOG, EMG, and motion are the popular artifacts that induce disturbance to the EEG signal. This survey paper emphasizes the artifact elimination methods with their substantial parameters that must be considered during the study of published research on this trend.
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Anas Fouad Ahmed (2022) conducted a review in EEG artifacts. EEG artifact removal methods was evaluated.
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