A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913651436290048 |
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| author | Zhao, Siqi Li, Wangyang Wang, Xiru Foglia, Stevie Tan, Hongzhao Zhang, Bohan Hamoodi, Ameer Nelson, Aimee Gao, Zhen |
| author_facet | Zhao, Siqi Li, Wangyang Wang, Xiru Foglia, Stevie Tan, Hongzhao Zhang, Bohan Hamoodi, Ameer Nelson, Aimee Gao, Zhen |
| contents | Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities can improve clinical decision-making by addressing complex tasks in clinical populations. This systematic literature review explores the use of multimodal EEG data in ML and DL models for clinical applications. A comprehensive search was conducted across PubMed, Web of Science, and Google Scholar, yielding 16 relevant studies after three rounds of filtering. These studies demonstrate the application of multimodal EEG data in addressing clinical challenges, including neuropsychiatric disorders, neurological conditions (e.g., seizure detection), neurodevelopmental disorders (e.g., autism spectrum disorder), and sleep stage classification. Data fusion occurred at three levels: signal, feature, and decision levels. The most commonly used ML models were support vector machines (SVM) and decision trees. Notably, 11 out of the 16 studies reported improvements in model accuracy with multimodal EEG data. This review highlights the potential of multimodal EEG-based ML models in enhancing clinical diagnostics and problem-solving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08585 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application Zhao, Siqi Li, Wangyang Wang, Xiru Foglia, Stevie Tan, Hongzhao Zhang, Bohan Hamoodi, Ameer Nelson, Aimee Gao, Zhen Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities can improve clinical decision-making by addressing complex tasks in clinical populations. This systematic literature review explores the use of multimodal EEG data in ML and DL models for clinical applications. A comprehensive search was conducted across PubMed, Web of Science, and Google Scholar, yielding 16 relevant studies after three rounds of filtering. These studies demonstrate the application of multimodal EEG data in addressing clinical challenges, including neuropsychiatric disorders, neurological conditions (e.g., seizure detection), neurodevelopmental disorders (e.g., autism spectrum disorder), and sleep stage classification. Data fusion occurred at three levels: signal, feature, and decision levels. The most commonly used ML models were support vector machines (SVM) and decision trees. Notably, 11 out of the 16 studies reported improvements in model accuracy with multimodal EEG data. This review highlights the potential of multimodal EEG-based ML models in enhancing clinical diagnostics and problem-solving. |
| title | A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application |
| topic | Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.08585 |