EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation
This repository provides a tensorflow implementation of a submitted paper:
EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation
Anonymous Authors, Anonymous Institutions
Abstract: Recently, deep learning-based electroencephalogram (EEG) analysis has gained widespread attention to monitor a user’s clinical condition or identify his/her intention. Nevertheless, the existing methods represent EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, thus suffering from the complex spatio-spectro-temporal patterns as well as inter-subject variability. In this work, we propose novel EEG-oriented self-supervised learning methods to discover complex and diverse patterns of spatio-spectral characteristics and spatio-temporal dynamics for EEG analysis. Combined with the proposed self-supervised representation learning, we also devise a feature normalization strategy to resolve an inter-subject variability problem. We demonstrated the validity of the proposed framework on three publicly available datasets with state-of-the-art comparison methods. It is noteworthy that the same network architecture was applied to three different tasks and outperformed the competing methods, thus resolving the problem of task-dependent network architecture engineering.
Dependencies
Datasets
To download Sleep-EDF database
To download KU-MI database
To download TUH abnormal corpus database
Usage
network.py contains the proposed deep learning architectures, utils.py contains functions used for experimental procedures, and main.py contains the main experimental functions.
Acknowledgements
Anonymous institute
