Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces
Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain–computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2079-7737/14/2/210 |
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| author | Abeer Al-Nafjan Hadeel Alshehri Mashael Aldayel |
| author_facet | Abeer Al-Nafjan Hadeel Alshehri Mashael Aldayel |
| author_sort | Abeer Al-Nafjan |
| collection | DOAJ |
| description | Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain–computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising two main components: (1) pain/no-pain detection and (2) pain severity classification across three levels: low, moderate, and high. Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time–frequency domain analysis. Furthermore, we compared the performance of our system against conventional machine learning models, such as support vector machines and random forest classifiers. Our deep learning approach outperformed the baseline models, achieving accuracies of 91.84% for pain/no-pain detection and 87.94% for pain severity classification, respectively. |
| format | Article |
| id | doaj-art-129fb01e92df4ffa8142b4a788ff7923 |
| institution | DOAJ |
| issn | 2079-7737 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj-art-129fb01e92df4ffa8142b4a788ff79232025-08-20T02:44:31ZengMDPI AGBiology2079-77372025-02-0114221010.3390/biology14020210Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer InterfacesAbeer Al-Nafjan0Hadeel Alshehri1Mashael Aldayel2Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaObjective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain–computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising two main components: (1) pain/no-pain detection and (2) pain severity classification across three levels: low, moderate, and high. Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time–frequency domain analysis. Furthermore, we compared the performance of our system against conventional machine learning models, such as support vector machines and random forest classifiers. Our deep learning approach outperformed the baseline models, achieving accuracies of 91.84% for pain/no-pain detection and 87.94% for pain severity classification, respectively.https://www.mdpi.com/2079-7737/14/2/210brain–computer interface (BCI)electroencephalography (EEG)pain assessmentartificial intelligencedeep learning |
| spellingShingle | Abeer Al-Nafjan Hadeel Alshehri Mashael Aldayel Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces Biology brain–computer interface (BCI) electroencephalography (EEG) pain assessment artificial intelligence deep learning |
| title | Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces |
| title_full | Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces |
| title_fullStr | Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces |
| title_full_unstemmed | Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces |
| title_short | Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces |
| title_sort | objective pain assessment using deep learning through eeg based brain computer interfaces |
| topic | brain–computer interface (BCI) electroencephalography (EEG) pain assessment artificial intelligence deep learning |
| url | https://www.mdpi.com/2079-7737/14/2/210 |
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