Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders
Neurological disorders are among the leading causes of both physical and cognitive disabilities worldwide, affecting approximately 15% of the global population. This study explores the use of machine learning (ML) and deep learning (DL) techniques in processing Electroencephalography (EEG) signals t...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10848369/ |
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| author | Souhaila Khalfallah William Puech Mehdi Tlija Kais Bouallegue |
| author_facet | Souhaila Khalfallah William Puech Mehdi Tlija Kais Bouallegue |
| author_sort | Souhaila Khalfallah |
| collection | DOAJ |
| description | Neurological disorders are among the leading causes of both physical and cognitive disabilities worldwide, affecting approximately 15% of the global population. This study explores the use of machine learning (ML) and deep learning (DL) techniques in processing Electroencephalography (EEG) signals to detect various neurological disorders, including Epilepsy, Autism Spectrum Disorder (ASD), and Alzheimer’s disease. We present a detailed workflow that begins with EEG data acquisition using a headset, followed by data preprocessing with Finite Impulse Response (FIR) filters and Independent Component Analysis (ICA) to eliminate noise and artifacts. Furthermore, the data is segmented, allowing the extraction of key features such as Bandpower and Shannon entropy, which improve classification accuracy. These features are stored in an offline database for easy access during analysis, to be then applied for both ML and DL models, systematically testing their performance and comparing the results to prior studies. Hence, our findings show impressive accuracy, with the random forest model achieving 99.85% accuracy in classifying autism vs. healthy subjects and 100% accuracy in distinguishing healthy individuals from those with dementia using Support Vector Machines (SVM). Moreover, deep learning models, including Convolutional Neural Networks (CNN) and ChronoNet, demonstrated accuracy rates ranging from 92.5% to 100%. In conclusion, this research highlights the effectiveness of ML and DL techniques in EEG signal processing, offering valuable contributions to the field of brain-computer interfaces and advancing the potential for more accurate neurological disease classification and diagnosis. |
| format | Article |
| id | doaj-art-eb11bd314f2d43d2ad6c6ab2ffc11ef8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eb11bd314f2d43d2ad6c6ab2ffc11ef82025-01-31T00:01:37ZengIEEEIEEE Access2169-35362025-01-0113170021701510.1109/ACCESS.2025.353251510848369Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological DisordersSouhaila Khalfallah0https://orcid.org/0009-0002-8104-9932William Puech1https://orcid.org/0000-0001-9383-2401Mehdi Tlija2https://orcid.org/0000-0003-2661-5102Kais Bouallegue3https://orcid.org/0000-0002-3905-5653Department of Electrical Engineering, National School of Engineering of Sousse, Sousse, TunisiaLIRMM,CNRS, Université Montpellier, Montpellier, FranceIndustrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Electrical Engineering, Higher Institute of Applied Sciences and Technology of Sousse, Sousse, TunisiaNeurological disorders are among the leading causes of both physical and cognitive disabilities worldwide, affecting approximately 15% of the global population. This study explores the use of machine learning (ML) and deep learning (DL) techniques in processing Electroencephalography (EEG) signals to detect various neurological disorders, including Epilepsy, Autism Spectrum Disorder (ASD), and Alzheimer’s disease. We present a detailed workflow that begins with EEG data acquisition using a headset, followed by data preprocessing with Finite Impulse Response (FIR) filters and Independent Component Analysis (ICA) to eliminate noise and artifacts. Furthermore, the data is segmented, allowing the extraction of key features such as Bandpower and Shannon entropy, which improve classification accuracy. These features are stored in an offline database for easy access during analysis, to be then applied for both ML and DL models, systematically testing their performance and comparing the results to prior studies. Hence, our findings show impressive accuracy, with the random forest model achieving 99.85% accuracy in classifying autism vs. healthy subjects and 100% accuracy in distinguishing healthy individuals from those with dementia using Support Vector Machines (SVM). Moreover, deep learning models, including Convolutional Neural Networks (CNN) and ChronoNet, demonstrated accuracy rates ranging from 92.5% to 100%. In conclusion, this research highlights the effectiveness of ML and DL techniques in EEG signal processing, offering valuable contributions to the field of brain-computer interfaces and advancing the potential for more accurate neurological disease classification and diagnosis.https://ieeexplore.ieee.org/document/10848369/Electroencephalography (EEG)neurological disordersmachine learningdeep learning |
| spellingShingle | Souhaila Khalfallah William Puech Mehdi Tlija Kais Bouallegue Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders IEEE Access Electroencephalography (EEG) neurological disorders machine learning deep learning |
| title | Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders |
| title_full | Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders |
| title_fullStr | Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders |
| title_full_unstemmed | Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders |
| title_short | Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders |
| title_sort | exploring the effectiveness of machine learning and deep learning techniques for eeg signal classification in neurological disorders |
| topic | Electroencephalography (EEG) neurological disorders machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10848369/ |
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