EEG Performance Signal Analysis for Diagnosing Autism Spectrum Disorder using Butterworth and Empirical Mode Decomposition

Imam Fathur Rahman; Melinda; Muhammad  Irhamsyah; Yunidar Yunidar; Yudha Nurdin; W.K. Wong; Lailatul Qadri Zakaria

doi:10.35882/jeeemi.v7i3.788

Imam Fathur Rahman Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia https://orcid.org/0009-0008-8068-994X
Melinda Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia https://orcid.org/0000-0001-9082-6639
Muhammad Irhamsyah Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia https://orcid.org/0000-0002-7726-6283
Yunidar Yunidar Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia https://orcid.org/0009-0008-4453-9215
Yudha Nurdin Universitas Syiah Kuala
W.K. Wong Department of Electrical and Computer engineering, Faculty of Engineering and sciences. Curtin University Malaysia, Sarawak, Malaysia https://orcid.org/0000-0001-6212-6096
Lailatul Qadri Zakaria Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia https://orcid.org/0000-0002-2098-5957

DOI: https://doi.org/10.35882/jeeemi.v7i3.788

Keywords: Autism Spectrum Disorder, Electroencephalography, Empirical Mode Decomposition, Butterworth Band-Pass Filter

Abstract

Electroencephalography (EEG) is a technique used to measure electrical activity in the brain by placing electrodes on the scalp. EEG plays an essential role in analyzing a variety of neurological conditions, including autism spectrum disorder (ASD). However, in the recording process, EEG signals are often contaminated by noise, hindering further analysis. Therefore, an effective signal processing method is needed to improve the data quality before feature extraction is performed. This study applied the Butterworth Band-Pass Filter (BPF) as a preprocessing method to reduce noise in EEG signals and then used the Empirical Mode Decomposition (EMD) method to extract relevant features. The performance of this method was evaluated using three main parameters, namely Mean Square Error (MSE), Mean Absolute Error (MAE), and Signal-to-Noise Ratio (SNR). The results showed that EMD was able to retain important information in EEG signals better than signals that only passed through the BPF filtration stage. EMD produces lower MAE and MSE values than Butterworth, suggesting that this method is more accurate in maintaining the original shape of the signal. In subject 3, EMD recorded the lowest MAE of 0.622 compared to Butterworth, which reached 20.0, and the MSE value of 0.655 compared to 771.5 for Butterworth. In addition, EMD also produced a higher SNR, with the highest value of 23,208 in subject 5, compared to Butterworth, which reached only 1,568. These results prove that the combination of BPF as a preprocessing method and EMD as a feature extraction method is more effective in maintaining EEG signal quality and improving analysis accuracy compared to the use of the Butterworth Band-Pass Filter alone.

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