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A Machine Learning Approach for Artifact Removal from Brain Signal

Sandhyalati Behera, Mihir Narayan Mohanty*

Department of Electronics and Communication Engineering, ITER, Sikhsha ‘O’Anusandhan (Deemed to be University), Bhubaneswar, 751030, India

* Corresponding Author: Mihir Narayan Mohanty. Email: email

Computer Systems Science and Engineering 2023, 45(2), 1455-1467. https://doi.org/10.32604/csse.2023.029649

Abstract

Electroencephalography (EEG), helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond range. To extract clean clinical information from EEG signals, it is essential to remove unwanted artifacts that are due to different causes including at the time of acquisition. In this piece of work, the authors considered the EEG signal contaminated with Electrocardiogram (ECG) artifacts that occurs mostly in cardiac patients. The clean EEG is taken from the openly available Mendeley database whereas the ECG signal is collected from the Physionet database to create artifacts in the EEG signal and verify the proposed algorithm. Being the artifactual signal is non-linear and non-stationary the Random Vector Functional Link Network (RVFLN) model is used in this case. The Machine Learning approach has taken a leading role in every field of current research and RVFLN is one of them. For the proof of adaptive nature, the model is designed with EEG as a reference and artifactual EEG as input. The peaks of ECG signals are evaluated for artifact estimation as the amplitude is higher than the EEG signal. To vary the weight and reduce the error, an exponentially weighted Recursive Least Square(RLS) algorithm is used to design the adaptive filter with the novel RVFLN model. The random vectors are considered in this model with a radial basis function to satisfy the required signal experimentation. It is found that the result is excellent in terms of Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Relative Error (RE), Gain in Signal to Artifact Ratio (GSAR), Signal Noise Ratio(SNR), Information Quantity (IQ), and Improvement in Normalized Power Spectrum (INPS). Also, the proposed method is compared with the earlier methods to show its efficacy.

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Cite This Article

APA Style
Behera, S., Mohanty, M.N. (2023). A machine learning approach for artifact removal from brain signal. Computer Systems Science and Engineering, 45(2), 1455-1467. https://doi.org/10.32604/csse.2023.029649
Vancouver Style
Behera S, Mohanty MN. A machine learning approach for artifact removal from brain signal. Comput Syst Sci Eng. 2023;45(2):1455-1467 https://doi.org/10.32604/csse.2023.029649
IEEE Style
S. Behera and M.N. Mohanty, "A Machine Learning Approach for Artifact Removal from Brain Signal," Comput. Syst. Sci. Eng., vol. 45, no. 2, pp. 1455-1467. 2023. https://doi.org/10.32604/csse.2023.029649



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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