Performance Evaluation of IIR and FIR Filters Using Windowing Techniques for EEG - Aurellio Indra Dewanta; Fillah Arhinzah; Muhammad Wildan Atho'Illah; Weny Indah Kusumawati; Ira Puspasari (MDP Student Conference, 2026)
Electroencephalogram (EEG) signals are highly susceptible to noise artifacts such as muscle activity, eye blinks, and power-line interference, which degrade signal quality and analysis accuracy. Digital filtering is therefore essential to preserve the EEG frequency band of interest (0.5–40 Hz) while suppressing unwanted components. This study presents a systematic comparison of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters for EEG signal denoising. FIR filters were implemented using Hamming, Hann, and Blackman window methods, whereas IIR filters included Butterworth, Chebyshev type I, Chebyshev type II, and Elliptic designs. All filters were applied to the same EEG signal under identical conditions. Performance was evaluated using visual inspection and Signal-to-Noise Ratio (SNR) analysis, followed by pole–zero stability assessment of the best-performing filters. The results show that FIR filtering achieved a higher SNR improvement (11.0 dB) than IIR filtering (8.61 dB), indicating superior noise suppression and stability for EEG signal processing.