The brain’s electrical activity and behavior is the neurophysiologic measurement by Electroencephalogram (EEG) by making a record of the EEG signal from the electrodes placed at the scalp. In most of the cases, the EEG signal gets muddle up with other biological signals and hence leads to artifacts. In the medical field, it is a challenging task to remove these artifacts from the EEG signal. This paper formulates a novel artifact removal model from multi-channel EEG data by hybridizing the Grey Wolf Optimization (GWO) and FireFly (FF) algorithm. Initially, the optimal weights of the EEG signal are achieved by feeding the input EEG signal to the proposed adaptive filtering. In the adaptive filtering technique, the clean EEG signal is recovered by means of subtracting the filtered output from the primary input. In the proposed adaptive filtering technique, the weights of NARX neural network need to be optimized to enhance the rejection accuracy. GWO and FF combine the weight of NARX neural network and hence optimize the weights. Finally, a performance-based evaluation is carried out between the proposed NN-GWOandFF and existing ICA, WICA, FICA, NNGWO, and NN-FF in terms of MSE and RMSE with different artifacts like ECG, EMG and EOG.
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