TY - GEN
T1 - Performance Analysis of Advanced Filtering Techniques for Removal of EEG Muscle Artifacts
AU - Raj, Vandana Akshath
AU - Nayak, Subramanya G.
AU - Thalengala, Ananthakrishna
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The 'electroencephalogram' (EEG) has become an essential technique for recording cerebral activity. EEG is extensively used in the field of clinical applications and BCI systems. However, these recorded brain signals are vulnerable to potential artifacts due to internal (physiological) and external factors. Though it is possible to limit the external factors affecting the actual brain signal, the internal factors are unavoidable. In the proposed work, two filtering models based on the normalized least mean square (NLMS) algorithm and Kalman filtering (KF) are applied to effectively remove the identified muscle artifact segments. The efficient stationary wavelet transform (SWT) was employed to identify the muscle artifact segments in the contaminated EEG signals. To test the models, EEGdenoiseNet, an open source dataset, was utilized. By varying the signal to noise ratio within the range of [-72] dB, ten contaminated EEG signals were generated. The results obtained show that the Kalman filter was more efficient in removing the high-frequency muscle artifact compared to the normalized LMS-based adaptive filter. The average correlation coefficient (CC) computed for Kalman filter was 0.82786, 2.7 % better than the normalized LMS-based filter. Results show that the output of the Kalman filter was more suitable to clean EEG signal with a 10.20 dB improvement in average SNR.
AB - The 'electroencephalogram' (EEG) has become an essential technique for recording cerebral activity. EEG is extensively used in the field of clinical applications and BCI systems. However, these recorded brain signals are vulnerable to potential artifacts due to internal (physiological) and external factors. Though it is possible to limit the external factors affecting the actual brain signal, the internal factors are unavoidable. In the proposed work, two filtering models based on the normalized least mean square (NLMS) algorithm and Kalman filtering (KF) are applied to effectively remove the identified muscle artifact segments. The efficient stationary wavelet transform (SWT) was employed to identify the muscle artifact segments in the contaminated EEG signals. To test the models, EEGdenoiseNet, an open source dataset, was utilized. By varying the signal to noise ratio within the range of [-72] dB, ten contaminated EEG signals were generated. The results obtained show that the Kalman filter was more efficient in removing the high-frequency muscle artifact compared to the normalized LMS-based adaptive filter. The average correlation coefficient (CC) computed for Kalman filter was 0.82786, 2.7 % better than the normalized LMS-based filter. Results show that the output of the Kalman filter was more suitable to clean EEG signal with a 10.20 dB improvement in average SNR.
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U2 - 10.1109/DISCOVER62353.2024.10750782
DO - 10.1109/DISCOVER62353.2024.10750782
M3 - Conference contribution
AN - SCOPUS:85211903185
T3 - 8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024 - Proceedings
SP - 422
EP - 427
BT - 8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024
Y2 - 18 October 2024 through 19 October 2024
ER -