Adaptive filtering of EEG/ERP through Bounded Range Artificial Bee Colony (BR-ABC) algorithm |
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| Affiliation: | 1. School of Information Science and Engineering, Shandong University, Jinan 250100, China;2. School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005, China;3. Suzhou Institute of Shandong University, Suzhou 215123, China;1. INSERM, U1099, Rennes, F-35000, France;2. Université de Rennes 1, Laboratoire Traitement du Signal et de l’Image, Rennes, F-35000, France;3. Sorbonne Universités, UPMC Univ Paris 06, UMRS-1158, Neurophysiologie Respiratoire Expérimentale et Clinique, Paris, F-75005, France;4. CHU Rennes, Pôle Médico-Chirurgical de Pédiatrie et de Génétique Clinique, Rennes, F-35000, France;5. INSERM, CIC-1414, Rennes, F-35000, France;1. Department of ECE, MVGR(A), Vizianagaram, 535005, India |
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| Abstract: | In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). ERP generated due to hand movement is filtered through Adaptive Noise Canceller (ANC) from the EEG signals. ANCs are also implemented with Least Mean Square (LMS) and Recursive Least Square (RLS) algorithm. Performance of the algorithms is evaluated in terms of Signal-to-Noise Ratio (SNR) in dB, correlation between resultant and template ERP, and mean value difference. Testing of their noise attenuation capability is done on contaminated ERP with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, RLS, and ABC algorithm is also made which reveals that ABC algorithm gives better performance in highly noisy environment. |
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| Keywords: | EEG/ERP Adaptive filter SNR LMS RLS ABC |
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