Using EEG Signals and AI to Predict Neurodegenerative Diseases Article Swipe
Juan Li
,
Dongyuan Zhang
,
Wei Lin
,
Wei Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3586363
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3586363
Diseases like Alzheimer’s and Parkinson’s pose significant challenges because of their complex and progressive characteristics. Addressing these conditions requires advanced diagnostic methods that support value-based care. This study proposes NeuroPredictNet, a deep learning-based predictive framework designed for early and accurate disease identification. NeuroPredictNet integrates multimodal data—EEG, neuroimaging, genetic, and clinical features—through an attention-based fusion mechanism that dynamically weights modality contributions. We introduce the Adaptive Knowledge Integration Strategy (AKIS) to enhance model robustness by addressing modality-specific noise, data imbalance, and temporal consistency. Experimental evaluations on four benchmark datasets demonstrate that our method achieves superior prediction accuracy and interpretability compared to state-of-the-art approaches. These results underscore the framework’s clinical potential in supporting personalized, value-based neurological care.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3586363
- OA Status
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- References
- 47
- Related Works
- 10
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https://openalex.org/W4412164381Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3586363Digital Object Identifier
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Using EEG Signals and AI to Predict Neurodegenerative DiseasesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Juan Li, Dongyuan Zhang, Wei Lin, Wei LiuList of authors in order
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https://doi.org/10.1109/access.2025.3586363Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3586363Direct OA link when available
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Electroencephalography, Computer science, Pattern recognition (psychology), Speech recognition, Artificial intelligence, Neuroscience, PsychologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.data—EEG, | 45 |
| abstract_inverted_index.interpretability | 96 |
| abstract_inverted_index.state-of-the-art | 99 |
| abstract_inverted_index.modality-specific | 74 |
| abstract_inverted_index.Alzheimer’s | 2 |
| abstract_inverted_index.Parkinson’s | 4 |
| abstract_inverted_index.framework’s | 105 |
| abstract_inverted_index.features—through | 50 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.28415071 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |