CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approach
Electroencephalography (EEG) has been used for quite some time as a diagnostic technique in neurology. The goal of this publication is to serve as a resource for researchers interested in applying deep learning methods to EEG data. This paper proposes a unique Hybrid Machine-Deep Learning model tha...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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College of Education, Al-Iraqia University
2023-10-01
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| Series: | Iraqi Journal for Computer Science and Mathematics |
| Subjects: | |
| Online Access: | https://journal.esj.edu.iq/index.php/IJCM/article/view/591 |
| _version_ | 1827781507720151040 |
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| author | osama abdulaziz Olga A. Saltykova |
| author_facet | osama abdulaziz Olga A. Saltykova |
| author_sort | osama abdulaziz |
| collection | DOAJ |
| description |
Electroencephalography (EEG) has been used for quite some time as a diagnostic technique in
neurology. The goal of this publication is to serve as a resource for researchers interested in applying deep learning
methods to EEG data. This paper proposes a unique Hybrid Machine-Deep Learning model that can learn and classify
EEG signals on its own. This method allows the model to classify EEG signals of varied sampling frequencies and
durations automatically. The proposed model used feature extraction methods from artificial design and performed
extensive tests with EEG data collected at varying sample rates to determine how well our suggested model
performed. The results show that the Hybrid Machine-Deep Learning strategy significantly improves performance,
leading to a remarkable 99.97% classification accuracy. Notably, this method performs exceptionally well when
labeling lower-frequency EEG signals (less than 4 Hz). The proposed model has improved consistency and
robustness, as shown by this study.
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| first_indexed | 2024-03-11T15:19:40Z |
| format | Article |
| id | doaj.art-f8063312b5ba4ffaa34c50adb00d7b56 |
| institution | Directory Open Access Journal |
| issn | 2958-0544 2788-7421 |
| language | English |
| last_indexed | 2024-03-11T15:19:40Z |
| publishDate | 2023-10-01 |
| publisher | College of Education, Al-Iraqia University |
| record_format | Article |
| series | Iraqi Journal for Computer Science and Mathematics |
| spelling | doaj.art-f8063312b5ba4ffaa34c50adb00d7b562023-10-29T06:11:49ZengCollege of Education, Al-Iraqia UniversityIraqi Journal for Computer Science and Mathematics2958-05442788-74212023-10-014410.52866/ijcsm.2023.04.04.006CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approachosama abdulaziz0Olga A. Saltykova1engineerAssociate Professor, Ph.D. Electroencephalography (EEG) has been used for quite some time as a diagnostic technique in neurology. The goal of this publication is to serve as a resource for researchers interested in applying deep learning methods to EEG data. This paper proposes a unique Hybrid Machine-Deep Learning model that can learn and classify EEG signals on its own. This method allows the model to classify EEG signals of varied sampling frequencies and durations automatically. The proposed model used feature extraction methods from artificial design and performed extensive tests with EEG data collected at varying sample rates to determine how well our suggested model performed. The results show that the Hybrid Machine-Deep Learning strategy significantly improves performance, leading to a remarkable 99.97% classification accuracy. Notably, this method performs exceptionally well when labeling lower-frequency EEG signals (less than 4 Hz). The proposed model has improved consistency and robustness, as shown by this study. https://journal.esj.edu.iq/index.php/IJCM/article/view/591Electroencephalography(EEG), Machine Learning, Deep Learning |
| spellingShingle | osama abdulaziz Olga A. Saltykova CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approach Iraqi Journal for Computer Science and Mathematics Electroencephalography(EEG), Machine Learning, Deep Learning |
| title | CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approach |
| title_full | CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approach |
| title_fullStr | CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approach |
| title_full_unstemmed | CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approach |
| title_short | CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine - Deep Learning Approach |
| title_sort | cnn ps electroencephalogram classification of brain states using hybrid machine deep learning approach |
| topic | Electroencephalography(EEG), Machine Learning, Deep Learning |
| url | https://journal.esj.edu.iq/index.php/IJCM/article/view/591 |
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