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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning
Hamed Azami, Mina Mirjalili, Tarek K. Rajji, Chien‐Te Wu, Anne Humeau‐Heurtier, Tzyy‐Ping Jung, Chun-Shu Wei, Thanh-Tung Trinh et al.. IEEE Journal of Selected Areas in Sensors, 2025-01-01
Topic: Risk, prevention and care in population studies
Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application.
Key findings
- Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia.
- Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness.
- These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes.
- By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care.
Why it matters
This review looks at EEG and ERP brain-activity tests combined with machine learning as ways to detect mild cognitive impairment, which can be an early stage before dementia. It is a review of methods, not a treatment or cure, and it notes that noise, differences between people, and difficulty interpreting the models still limit reliability.
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Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE Journal of Selected Areas in Sensors) |
| Journal's citation record | 0 / 4 | IEEE Journal of Selected Areas in Sensors: journal h-index 11 (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 | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 19 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 10.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 19 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Yi‐Hung Liu: h-index 24 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Toronto: institution h-index 1,923 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 9 institution(s) in 4 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 43 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1109/jsas.2025.3552546
- Original source →
- Free full text: IEEE Journal of Selected Areas in Sensors →
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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Azami, H., Mirjalili, M., Rajji, T. K., Wu, C., Humeau‐Heurtier, A., Jung, T., et al. (2025). Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning. IEEE Journal of Selected Areas in Sensors. https://doi.org/10.1109/jsas.2025.3552546
Vancouver
Azami H, Mirjalili M, Rajji TK, Wu C, Humeau‐Heurtier A, Jung T, et al. Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning. IEEE Journal of Selected Areas in Sensors. 2025. doi:10.1109/jsas.2025.3552546.
BibTeX
@article{azami2025-20a9890e,
author = {Azami, Hamed and Mirjalili, Mina and Rajji, Tarek K. and Wu, Chien‐Te and Humeau‐Heurtier, Anne and Jung, Tzyy‐Ping and Wei, Chun-Shu and Trinh, Thanh-Tung and others},
title = {{Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning}},
journal = {IEEE Journal of Selected Areas in Sensors},
year = {2025},
doi = {10.1109/jsas.2025.3552546},
url = {https://doi.org/10.1109/jsas.2025.3552546}
}
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