Comparing windowing methods on Finite Impulse Response (FIR) filter algorithm in Electroencephalography (EEG) data processing

Electroencephalography (EEG) data contains electric signal activities on a cerebral cortex to record brain electrical activities. EEG signal has some characteristics such as non-periodic, non-standardized pattern, and small voltage amplitude. Hence, it is lightly heaped up to noise and difficult to recognize and extract meaningful information from EEG data. Finite Impulse Response (FIR) with various windowing methods has been widely used to mitigate noise and distortions. This paper compares and analyzes the windowing techniques in resulting the most optimal results in EEG filtration process. The experiment results show that Blackman Window gives the best result in term of the most negative value in stop-band attenuation, the widest transition bandwidth, and the highest cutoff frequency compares to Rectangular Window, Hamming Window, and Hann Window.