Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease
While Parkinson’s disease (PD) is typically characterized by motor disorder, there is also evidence of diminished emotion perception in PD patients. This study examines the utility of electroencephalography (EEG) signals to understand emotional differences between PD and healthy controls (HCs), and...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2024-01-01
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| Series: | Intelligent Computing |
| Online Access: | https://spj.science.org/doi/10.34133/icomputing.0084 |
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| author | Ravikiran Parameshwara Soujanya Narayana Murugappan Murugappan Ibrahim Radwan Roland Goecke Ramanathan Subramanian |
| author_facet | Ravikiran Parameshwara Soujanya Narayana Murugappan Murugappan Ibrahim Radwan Roland Goecke Ramanathan Subramanian |
| author_sort | Ravikiran Parameshwara |
| collection | DOAJ |
| description | While Parkinson’s disease (PD) is typically characterized by motor disorder, there is also evidence of diminished emotion perception in PD patients. This study examines the utility of electroencephalography (EEG) signals to understand emotional differences between PD and healthy controls (HCs), and for automated PD detection. Employing traditional machine learning and deep learning methods on multiple EEG descriptors, we explore (a) dimensional and categorical emotion recognition and (b) PD versus HC classification from multiple descriptors characterizing emotional EEG signals. Our results reveal that PD patients comprehend arousal better than valence and, among emotion categories, fear, disgust, and surprise less accurately, and sadness most accurately. Mislabeling analyses confirm confounds among opposite-valence emotions for PD data. Emotional EEG responses also achieve near-perfect PD versus HC recognition. Cumulatively, our study demonstrates that (a) examining implicit responses alone enables (i) discovery of valence-related impairments in PD patients and (ii) differentiation of PD from HC and that (b) emotional EEG analysis is an ecologically valid, effective, practical, and sustainable tool for PD diagnosis vis-à-vis self-reports, expert assessments, and resting-state analysis. |
| format | Article |
| id | doaj-art-e5a43d276a5f4d2fb15098763266c0eb |
| institution | OA Journals |
| issn | 2771-5892 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Intelligent Computing |
| spelling | doaj-art-e5a43d276a5f4d2fb15098763266c0eb2025-08-20T02:17:09ZengAmerican Association for the Advancement of Science (AAAS)Intelligent Computing2771-58922024-01-01310.34133/icomputing.0084Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s DiseaseRavikiran Parameshwara0Soujanya Narayana1Murugappan Murugappan2Ibrahim Radwan3Roland Goecke4Ramanathan Subramanian5Human-Centred Technology Research Cluster, University of Canberra, Canberra, Australia.Human-Centred Technology Research Cluster, University of Canberra, Canberra, Australia.Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City, Kuwait.Human-Centred Technology Research Cluster, University of Canberra, Canberra, Australia.Human-Centred Technology Research Cluster, University of Canberra, Canberra, Australia.Human-Centred Technology Research Cluster, University of Canberra, Canberra, Australia.While Parkinson’s disease (PD) is typically characterized by motor disorder, there is also evidence of diminished emotion perception in PD patients. This study examines the utility of electroencephalography (EEG) signals to understand emotional differences between PD and healthy controls (HCs), and for automated PD detection. Employing traditional machine learning and deep learning methods on multiple EEG descriptors, we explore (a) dimensional and categorical emotion recognition and (b) PD versus HC classification from multiple descriptors characterizing emotional EEG signals. Our results reveal that PD patients comprehend arousal better than valence and, among emotion categories, fear, disgust, and surprise less accurately, and sadness most accurately. Mislabeling analyses confirm confounds among opposite-valence emotions for PD data. Emotional EEG responses also achieve near-perfect PD versus HC recognition. Cumulatively, our study demonstrates that (a) examining implicit responses alone enables (i) discovery of valence-related impairments in PD patients and (ii) differentiation of PD from HC and that (b) emotional EEG analysis is an ecologically valid, effective, practical, and sustainable tool for PD diagnosis vis-à-vis self-reports, expert assessments, and resting-state analysis.https://spj.science.org/doi/10.34133/icomputing.0084 |
| spellingShingle | Ravikiran Parameshwara Soujanya Narayana Murugappan Murugappan Ibrahim Radwan Roland Goecke Ramanathan Subramanian Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease Intelligent Computing |
| title | Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease |
| title_full | Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease |
| title_fullStr | Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease |
| title_full_unstemmed | Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease |
| title_short | Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease |
| title_sort | exploring electroencephalography based affective analysis and detection of parkinson s disease |
| url | https://spj.science.org/doi/10.34133/icomputing.0084 |
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