Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910911817580544 |
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| author | Rutkowski, Tomasz M. Narębski, Stanisław Otake-Matsuura, Mihoko Komendziński, Tomasz |
| author_facet | Rutkowski, Tomasz M. Narębski, Stanisław Otake-Matsuura, Mihoko Komendziński, Tomasz |
| contents | Early dementia diagnosis requires biomarkers sensitive to both structural and functional brain changes. While structural neuroimaging biomarkers have progressed significantly, objective functional biomarkers of early cognitive decline remain a critical unmet need. Current cognitive assessments often rely on behavioral responses, making them susceptible to factors like effort, practice effects, and educational background, thereby hindering early and accurate detection. This work introduces a novel approach, leveraging a lightweight convolutional neural network (CNN) to infer cognitive impairment levels directly from electroencephalography (EEG) data. Critically, this method employs a passive fast periodic visual stimulation (FPVS) paradigm, eliminating the need for explicit behavioral responses or task comprehension from the participant. This passive approach provides an objective measure of working memory function, independent of confounding factors inherent in active cognitive tasks, and offers a promising new avenue for early and unbiased detection of cognitive decline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10973 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version Rutkowski, Tomasz M. Narębski, Stanisław Otake-Matsuura, Mihoko Komendziński, Tomasz Neurons and Cognition Human-Computer Interaction Machine Learning J.3 Early dementia diagnosis requires biomarkers sensitive to both structural and functional brain changes. While structural neuroimaging biomarkers have progressed significantly, objective functional biomarkers of early cognitive decline remain a critical unmet need. Current cognitive assessments often rely on behavioral responses, making them susceptible to factors like effort, practice effects, and educational background, thereby hindering early and accurate detection. This work introduces a novel approach, leveraging a lightweight convolutional neural network (CNN) to infer cognitive impairment levels directly from electroencephalography (EEG) data. Critically, this method employs a passive fast periodic visual stimulation (FPVS) paradigm, eliminating the need for explicit behavioral responses or task comprehension from the participant. This passive approach provides an objective measure of working memory function, independent of confounding factors inherent in active cognitive tasks, and offers a promising new avenue for early and unbiased detection of cognitive decline. |
| title | Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version |
| topic | Neurons and Cognition Human-Computer Interaction Machine Learning J.3 |
| url | https://arxiv.org/abs/2504.10973 |