Home › Research › Digital health tools
A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses
Khatun S, Morshed BI, Bidelman GM.. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2019-04-18
Topic: Risk, prevention and care in population studies
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 2 ). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI.
Key findings
- 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 2 ).
- Similar performances were also observed with logistic regression (LR), further validating the results.
- Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI.
Why it matters
A single-channel EEG method detected mild cognitive impairment with 87.9% accuracy using speech-evoked brain responses in 23 subjects. This is an early detection approach, not a treatment, cure or prevention for Alzheimer's disease.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Participants23
- Effect size87.9%; 84.8%
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society) |
| Journal's citation record | 2 / 4 | IEEE Transactions on Neural Systems and Rehabilitation Engineering: journal h-index 190 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 23 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 111 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.3 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 5.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 5 other studies in this library |
| Still being cited | 3 / 4 | Cited 50 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Gavin M. Bidelman: h-index 47 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Memphis: institution h-index 400 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30998476) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Khatun, S., Morshed, B. I., & Bidelman, G. M. (2019). A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/tnsre.2019.2911970
Vancouver
Khatun S, Morshed BI, Bidelman GM. A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 2019. doi:10.1109/tnsre.2019.2911970. PMID: 30998476.
BibTeX
@article{khatun2019-30998476,
author = {Khatun, S. and Morshed, B. I. and Bidelman, G. M.},
title = {{A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses}},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
year = {2019},
doi = {10.1109/tnsre.2019.2911970},
pmid = {30998476},
url = {https://doi.org/10.1109/tnsre.2019.2911970}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
5 studies in the alzhub library list this one in their references.