SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering
Source abstract
Abstract
The structural and cognitive functions of the brain undergo significant changes throughout an individual’s lifetime. The analysis of EEG background waves based on age groups will help reveal the correlation between human cognitive development ability and their age, and provide a new perspective for a deeper understanding of neurodegenerative diseases. Unfortunately, the available literature shows that, in recent years, the analysis of EEG signal background waves at different age groups has been extremely rare. To address the vacuum of this research, this paper introduces an innovative semi-supervised graph attention autoencoder method, SGAAE-AC, an age-based clustering method based on EEG background wave analysis. This method utilizes feedback from the labels generated by age-based clustering to guide the encoder in generating more accurate EEG graph embeddings. Furthermore, by adopting multi-objective optimization techniques, the accuracy and interpretability of EEG signal clustering are significantly improved. Our experimental outcomes elucidate the relationship and impact between human age and EEG background waves from perspectives such as comprehensive EEG spectral activity and frequency band attention, thereby uncovering the patterns of EEG background wave activity as they evolve with age.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Functional Brain Connectivity Studies · EEG and Brain-Computer Interfaces · Advanced Graph Neural Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China
Yunnan Key Lab of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650504, China
Funding
The Key Research and Development Program of Yunnan Province
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