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 brainelectrical 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 extractmeaningful information from EEG data. FiniteImpulse Response (FIR) with various windowing methodshas been widely used to mitigate noise and distortions. This paper compares and analyzes the windowingtechniques in resulting the most optimal results in EEG filtration process. The experiment resultsshow thatBlackman Window gives the best result in term of the most negative value in stop-band attenuation, thewidest transition bandwidth, and the highest cutoff frequency compares to Rectangular Window, HammingWindow, and Hann Window