AI in EEG-Based BCI for the Diagnosis of Mild Cognitive Impairment: A Mini Review

Mild Cognitive Impairment (MCI) is a condition that often precedes dementia, making early diagnosis critical for delaying cognitive decline. Electroencephalography (EEG) has emerged as a non-invasive, cost-effective tool for monitoring brain activity and detecting MCI. This paper overviews recent ad...

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Vydáno v:The ... International Winter Conference on Brain-Computer Interface s. 1 - 6
Hlavní autoři: Zaitsev, Vasilii, Wei, Chun-Shu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 24.02.2025
Témata:
Brain modeling
Deep learning
Dementia
EEG
Electroencephalography
Feature extraction
Machine learning
MCI
Nearest neighbor methods
Recording
Reviews
Support vector machines
Transformers
ISSN:2572-7672
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Shrnutí:Mild Cognitive Impairment (MCI) is a condition that often precedes dementia, making early diagnosis critical for delaying cognitive decline. Electroencephalography (EEG) has emerged as a non-invasive, cost-effective tool for monitoring brain activity and detecting MCI. This paper overviews recent advancements in machine learning (ML) and deep learning (DL) models for EEG-based MCI diagnosis. Traditional ML approaches, such as support vector machines (SVM) and K-nearest neighbors (KNN), have been widely used but rely on manually extracted features and face challenges with the complex nature of EEG signals. In contrast, DL models like convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers have shown promise in automatically learning features and capturing temporal and spatial information from EEG data. Despite these advancements, issues such as small dataset sizes and variability in EEG recordings remain barriers to clinical application. This paper discusses these challenges and highlights potential future directions for improving the diagnosis of MCI.
ISSN:2572-7672
DOI:10.1109/BCI65088.2025.10931606