# Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

arXiv: https://arxiv.org/abs/2607.28687 · HTML: https://mlsanity.com/paper/2607.28687/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 199 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [LEAD: An EEG Foundation Model for Alzheimer's Disease Detection](https://mlsanity.com/paper/2502.01678/) (2025)
- [LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data](https://mlsanity.com/paper/2508.10027/) (2025)
- [Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment](https://mlsanity.com/paper/2501.18731/) (2025)
- [Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data](https://mlsanity.com/paper/2101.02876/) (2021)
- [A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing](https://mlsanity.com/paper/2607.24519/) (2026)
- [Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal](https://mlsanity.com/paper/2305.19379/) (2023)
- [Deep Fuzzy Framework for Emotion Recognition using EEG Signals and Emotion Representation in Type-2 Fuzzy VAD Space](https://mlsanity.com/paper/2401.07892/) (2024)
- [Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks](https://mlsanity.com/paper/2510.11917/) (2025)
- [Proactive Emotion Tracker: AI-Driven Continuous Mood and Emotion Monitoring](https://mlsanity.com/paper/2401.13722/) (2024)
- [Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version](https://mlsanity.com/paper/2504.10973/) (2025)

## Abstract (arXiv)

As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routine assessment often misses its earliest signs. This article critically synthesizes recent technological advances for detecting and managing cognitive impairment in older adults, spanning neurophysiological signals (chiefly electroencephalography, EEG), structural and molecular neuroimaging (MRI and amyloid/tau PET), blood-based biomarkers, and digital markers, integrated through artificial intelligence (AI), machine learning (ML), and deep learning (DL). Beyond summarizing, it contributes a cross-disciplinary taxonomy, a methodological-rigor lens foregrounding subject- and site-independent validation, an integrative early-detection framework linking tiered screening to intervention, and comparison tables of detection methods, interventions, and risk and protective factors. EEG markers (alpha/theta changes, P300 latency) and deep models (CNNs, LSTM/BiLSTM, transformers, self-supervised EEG foundation models) report strong accuracy, yet many rest on small, single-site datasets unlikely to survive rigorous external validation. Elsewhere, gains are tangible: plasma p-tau217 has reached clinical utility, with the first blood test cleared to aid Alzheimer's diagnosis in 2025; anti-amyloid therapies (lecanemab, donanemab) are approved despite modest, contested benefits; and multidomain lifestyle prevention has matured. Wearable, remote, speech, and virtual-reality tools enable continuous, ecologically valid monitoring, and multimodal fusion improves sensitivity and specificity. Barriers remain: standardization, explainability, data privacy, and equitable, externally validated deployment. The field's near-term promise lies in trustworthy, multimodal, longitudinally validated systems linking early detection to actionable, personalized care.