Cat Swarm Fractional Calculus optimization-based deep learning for artifact removal from EEG signal. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Anem, Jayalaxmi; Kumar, G. Sateesh; Madhu, R.
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 32:Issue 6(2020)
• Page Start: 939
• Page End: 958
• Publication Date: 2020-11-01
• Subjects: EEG signal -- artefacts -- wavelet transform -- deep learner -- LSTM network; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2019.1704438
• Languages: English
• ISSNs: 0952-813X

Abstract

ABSTRACT: Electroencephalogram (EEG) signals are commonly used in analysing the brain activity. The EEG signals have small amplitude and hence, they are often affected by the artefacts. For the efficient processing, it is necessary to remove the artefacts from the EEG signals. This paper develops a technique through deep learning scheme for removing the artefacts present in the EEG signal. Initially, the EEG signals are pre-processed and provided to the feature extraction process, where the wavelet features are extracted from the signal by applying the wavelet transform. The extracted features are provided to the proposed classifier, namely deep-ConvLSTM, for removing the artefacts from the EEG signal. Here, the deep learner is trained based on the proposed Cat Swarm Fractional Calculus Optimisation (CSFCO) algorithm, which is the integration of Cat Swarm Optimisation (CSO) and Fractional Calculus (FC). Experimentation of the proposed technique is carried out by introducing artefacts, such as ECG, EMG, EOG and random noise on the EEG signal. Simulation results carried out on the proposed deep-ConvLSTM depict that the proposed framework has better performance than the comparative techniques with the values of 3888.362, 62.356, and 69.939 dB, for the MSE, RMSE, and SNR, respectively.