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Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG

Authors :
Sibilano, Elena
Buongiorno, Domenico
Lassi, Michael
Grippo, Antonello
Bessi, Valentina
Sorbi, Sandro
Mazzoni, Alberto
Bevilacqua, Vitoantonio
Brunetti, Antonio
Source :
IEEE Journal of Biomedical and Health Informatics; 2024, Vol. 28 Issue: 6 p3422-3433, 12p
Publication Year :
2024

Abstract

The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

Details

Language :
English
ISSN :
21682194 and 21682208
Volume :
28
Issue :
6
Database :
Supplemental Index
Journal :
IEEE Journal of Biomedical and Health Informatics
Publication Type :
Periodical
Accession number :
ejs66651781
Full Text :
https://doi.org/10.1109/JBHI.2024.3390606