Performance Evaluation of FIR Low-Pass and High-Pass Filtering for Alpha–Beta EEG Signal Preprocessing in Stroke Patients
DOI:
https://doi.org/10.30871/jaic.v10i2.12131Keywords:
EEG, FIR Filter, Low Pass Filter, High Pass Filter, StrokeAbstract
Electroencephalography (EEG) signals are inherently non-stationary and highly susceptible to various noise sources, such as baseline drift, muscle artifacts, and electrical interference. These disturbances can significantly degrade EEG signal quality, particularly in clinical stroke analysis, thereby affecting the reliability of neurological interpretation. This study aims to evaluate the effectiveness of Finite Impulse Response (FIR) Low-Pass Filter (LPF) and High-Pass Filter (HPF) as preprocessing techniques for improving EEG signal quality in the Alpha–Beta frequency bands (8–30 Hz). The dataset used in this study consists of private clinical EEG recordings obtained from ischemic stroke patients. The preprocessing pipeline includes signal normalization, FIR filter design, and signal filtering. Quantitative evaluation is performed using Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE), supported by frequency-domain analysis using Power Spectral Density (PSD). The results indicate that FIR LPF with a cutoff frequency of 40 Hz achieves higher SNR and lower RMSE compared to FIR HPF at 1 Hz, demonstrating superior performance in suppressing high-frequency noise while preserving relevant Alpha–Beta EEG information. These findings suggest that FIR-based filtering is effective for enhancing EEG signal quality within the scope of this study. However, the results are limited to the investigated dataset and filter configurations, and further validation using larger datasets and alternative preprocessing methods is recommended.
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Copyright (c) 2026 MY Teguh Sulistyono, Annisa Rizqiyani, Nafhisa Mahardika Ayuniraeda, Imam Syafiq, Aripin Aripin
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