Advancements in EEG-based BCI for cognitive decline diagnosis using deep learning and Transformers

Abstract. Deep learning and Transformer models have revolutionized medical diagnostics, particularly in the advanced analysis of large datasets. Given the individual variability in cognitive decline status, the accuracy of diagnosing elderly cognitive decline diseases is crucial. Since it allows for the initiation of treatment and interventions at the earliest possible stage, potentially slowing the progression of the disease. In this paper, the author reviews and summarizes studies on the use of Transformers and deep learning in Electroencephalography (EEG) signal decoding, which is the most prevalent signal-detecting method in Brain-Computer Interface (BCI) systems for elderly cognitive decline. The review indicates that deep learning and transformer models outperform traditional methods in classifying cognitive decline, offering effective methods and improved accuracy by extracting and analyzing in-depth features from EEG data. Differently, deep learning models are proficient in single modality learning, but transformer models offer a unified approach to process and integrate information from diverse data sources and, with the increase in data volume, Transformers have the potential to further improve the accuracy of diagnosis.