ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2019
A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 110 citations in OpenAlex.
- Biomarkers for Alzheimer's disease and mild cognitive impairment: Recent advances in task-based EEG.Journal of Alzheimer's disease : JAD · 2026Pooled it
- AI-Driven Digital Twin Architecture for Multimodal Prediction and Adaptive Intervention in Cognitive Aging.JMIR AI · 2026Article
- Wearable-Compatible Detection of Mild Cognitive Impairment Using Novel Features Based on Sleep Stage Dynamics.Brain sciences · 2026Article
- Neural features underlying high-accuracy classification of amnestic mild cognitive impairment using multi-sensory-evoked potentials.GeroScience · 2026Article
- Reducing Educational Bias in Cognitive Assessment via Dynamic Support Vector Machine Weighting: Validation Study on an Education-Stratified Dataset.JMIR rehabilitation and assistive technologies · 2026Article
- Wearable EEG devices in the detection of mild cognitive impairment: a systematic review.NPJ digital medicine · 2026Article
- Altered temporal dynamics of prefrontal ERP responses reflecting neural adaptation in patients with amnestic mild cognitive impairment.Alzheimer's research & therapy · 2026Article
- Modeling Working Memory in Neurodegeneration: A Focus on EEG Methods.Diagnostics (Basel, Switzerland) · 2025Review
- Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer's Disease and Mild Cognitive Impairment.Biosensors · 2025Article
- Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Electroencephalogram-based time-frequency analysis for Alzheimer's disease detection using machine learning.Journal of biological methods · 2025Article
- Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.Scientific reports · 2024Article
- Prefrontal intra-individual ERP variability and its asymmetry: exploring its biomarker potential in mild cognitive impairment.Alzheimer's research & therapy · 2024Article
- Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.Cognitive neurodynamics · 2024Article
- Inter-hospital moderate and advanced Alzheimer's disease detection through convolutional neural networks.Heliyon · 2024Article
- Connectivity changes in two-channel prefrontal ERP associated with early cognitive decline in the elderly population: beta band responses to the auditory oddball stimuli.Frontiers in aging neuroscience · 2024Article
- Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.Cognitive neurodynamics · 2023Article
- Prefrontal event-related potential markers in association with mild cognitive impairment.Frontiers in aging neuroscience · 2023Article
- Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer's disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this paper, we demonstrate a single-channel EEG-based MCI detection method, which is cost-effective and portable, and thus suitable for regular home-based patient monitoring. We collected the scalp EEG data from 23 subjects, while they were stimulated with five auditory speech signals. The cognitive state of the subjects was evaluated by the Montreal cognitive assessment test (MoCA). We extracted 590 features from the event-related potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with radial basis kernel (RBF) (sigma = 10/cost = 10
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.