Adaptive filtering of EEG/ERP through bounded range artificial Bee Colony (BR-ABC) algorithm

Ahirwal, M.K. and Kumar, A. and Singh, G.K. (2014) Adaptive filtering of EEG/ERP through bounded range artificial Bee Colony (BR-ABC) algorithm. Digital Signal Processing, 25. pp. 164-172. ISSN 1051-2004

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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.

Item Type: Article
Uncontrolled Keywords: EEG/ERP; Adaptive filter; SNR; LMS; RLS; ABC
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Wati Yusuf
Date Deposited: 16 Jun 2014 09:21
Last Modified: 01 Oct 2018 03:50
URI: http://eprints.um.edu.my/id/eprint/10613

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