A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording

Removing artifacts from electroencephalography (EEG) signals is a common technique. Although numerous algorithms have been proposed, most rely solely on EEG data. In this study, we introduce a novel approach utilizing a hybrid convolutional neural network-long short-term memory (CNN-LSTM) architecture alongside simultaneous recording of facial and neck EMG signals. This setup enables the precise elimination of artifacts from the EEG signal. To validate the method, we collected a dataset from 24 participants who were presented with a light-emitting diode (LED) stimulus that elicited steady-state visual evoked potentials (SSVEPs) while they performed strong jaw clenching, an action known to induce significant artifacts. We then assessed the algorithm's ability to remove artifacts while preserving SSVEP responses. The results were compared against other commonly used algorithms, such as independent component analysis and linear regression. The findings demonstrate that the proposed method exhibits excellent performance, effectively removing artifacts while retaining the EEG signal's useful components.