Artifacts removal from EEG signal: FLM optimization-based learning algorithm for neural network-enhanced adaptive filtering

Electroencephalogram (EEG) denotes a neurophysiologic measurement, which observes the electrical activity of the brain through making a record of the EEG signal from the electrodes positioned on the scalp. The EEG signal gets mixed with other biological signals, called artifacts. Few artifacts include electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). Removal of artifacts from the EEG signal poses a great challenge in the medical field. Hence, the FLM (Firefly + Levenberg Marquardt) optimization-based learning algorithm for neural network-enhanced adaptive filtering model is introduced to eliminate the artifacts from the EEG. Initially, the EEG signal was provided to the adaptive filter for yielding the optimal weights using the renowned optimization algorithms, called firefly algorithm and LM. These two algorithms are effectively hybridized and applied to the neural network to find the optimal weights for adaptive filtering. Then, the designed filtering process renders an improved system for artifacts removal from the EEG signal. Finally, the performance of the proposed model and the existing models regarding SNR, computation time, MSE and RMSE are analyzed. The results conclude that the proposed method achieves a high SNR of 42.042 dB.