Design of optimized adaptive filter for artifact removal from EEG signal

Behera, Sandhyalati; Mohanty, Mihir Narayan; Mohapatra, Saumendra Kumar

doi:10.47974/JSMS-959

Open Access Research Article

Design of optimized adaptive filter for artifact removal from EEG signal

^*Sandhyalati BeheraCorresponding authorsandhyalatibehera@soa.ac.inDepartment of Electronics and Communication EngineeringSiksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, OdishaInstitute of Technical Education and ResearchIndiaView full profile → , Mihir Narayan Mohantymihir.n.mohanty@gmail.comDepartment of Electronics and Communication EngineeringSiksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, OdishaInstitute of Technical Education and ResearchIndiaView full profile → , Saumendra Kumar Mohapatrasaumendrakumam.sse@saveetha.comDepartment of Information TechnologySikimSRM UniversityIndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 201–212Vol. 26Issue 1December 2022DOI: 10.47974/JSMS-959XML

Published Online:: 31 Dec 2022
Article type:: Research Article
Language:: EN
Article no.:: JSMS-959
Pages:: 201–212

Abstract

Removal of artifacts from the acquired biomedical signal is a major issue. Various signal processing techniques are developed by the researchers for some decades to extract the clinical information from EEG signals after the removal of unwanted artifacts during the time of its recording. In this work, an optimized weighted RLS filter is designed to suppress cardiac interference from the EEG signal. The cardiac artifact are estimated with the help of an optimized weighted RLS algorithm and eliminated from the EEG signal. Based on validation measures, Mean Square Error(MSE), Gain in Signal to Artifact Ratio(GSAR), Signal to Noise Ratio(SNR), Information Quantity(IQ), and INPS are used. The performance of the proposed method compared with the earlier methods like the Least square notch filter, ICA method are given in the result section. For the proposed method the MSE,GSAR,SNR,IQ, and INPS are found to be 0.1898 μV2 , 10.4807dB, 68.1117dB, 0.330, 10.91dB respectively. In every case, the performance of the proposed method is better than the existing method which proves its robustness.

Keywords

Information Quantity (IQ)Relative Error(RE)Cardiac interference Mean Square Error(MSE)Improvement in the Normalized Power Spectrum(INPS)

Subject Classifications

94A12

References

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