Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version

2025-04-15

AI Summary

To address the susceptibility of behavioral assessments to effort variability, practice effects, and educational background in early detection of cognitive impairment in older adults, this study proposes an objective, response-independent functional biomarker. We innovatively introduce a passive, fast periodic visual stimulation (FPVS) paradigm combined with high-density electroencephalography (EEG), establishing the first FPVS-EEG protocol specifically designed for quantitative working memory assessment. Leveraging a lightweight convolutional neural network (CNN) and high-temporal-resolution EEG signal processing, we achieve automated classification of cognitive states. Validated in an elderly cohort, the method demonstrates high accuracy in inferring cognitive impairment severity (AUC > 0.92). This approach markedly enhances objectivity, test–retest reliability, cross-population generalizability, and clinical translatability compared with conventional behavioral measures.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Detect early cognitive impairment in elderly objectively
• Overcome biases in current behavioral response-based assessments
• Provide passive EEG-based biomarker for dementia diagnosis

Innovation

• Passive FPVS-EEG BCI for cognitive assessment
• Lightweight CNN analyzes EEG data directly
• No behavioral responses needed for diagnosis