Performance Analysis of IIR & FIR Windowing Techniques in Electroencephalography Signal Processing
Anshul1, Dipali Bansal2, Rashima Mahajan3
1Anshul, PhD Scholar Department of Engineering and Communication Engineering, FET, Manav Rachna International Institute of Research and Studies, Faridabad, India.
2Dipali Bansal, Professor, Department of Engineering and Communication Engineering, FET, Manav Rachna International Institute of Research and Studies, Faridabad, India.
3Rashima Mahajan, Associate Professor, Department of Engineering and Communication Engineering, FET, Manav Rachna International Institute of Research and Studies, Faridabad, India.
Manuscript received on 14 August 2019 | Revised Manuscript received on 20 August 2019 | Manuscript published on 30 August 2019 | PP: 3564-3578 | Volume-8 Issue-10, August 2019 | Retrieval Number: J97710881019/19©BEIESP | DOI: 10.35940/ijitee.J9771.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A very small amplitude (μV) of the electroencephalography (EEG) signal is infected by diverse artifacts. These artifacts have an effect on the distinctiveness of the signal because of which medical psychoanalysis and data retrieval is difficult. Therefore, EEG signals are initially preprocessed to eliminate the artifacts to produce signals that can serve as a base for further processing and analysis. Different filters are implemented to eliminate the artifacts present in the EEG signal. Recent research shows that window technique Finite Impulse Response (FIR) filter is usually used. In this paper, digital Infinite Impulse Response (IIR) filter and different Finite Impulse Response (FIR) window filters (Hanning, Hamming, Kaiser, Blackman) of various orders are implemented to eradicate the random noise added to EEG signals. Their performance analysis has been done in Matlab (R2016a) by calculating the mean square error, mean absolute error, signal to noise ratio, peak signal to noise ratio and cross-correlation. The results show that Kaiser Window based finite impulse response filter outperforms in removing the noise from the electroencephalogram signal. This research focuses on eradicating random noise in electroencephalogram signals but this approach will be extended to a different source of electroencephalogram contamination.
Keywords: Blackman Window, FIR Filter, Hamming Window, Hanning Window, IIR Filter, Kaiser Window.
Scope of the Article: Digital Signal Processing Theory