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

• Python 3.8+
• TensorFlow 2.7.0+

Datasets

To download Sleep-EDF database

• https://physionet.org/content/sleep-edf/1.0.0/

To download KU-MI database

• http://gigadb.org/dataset/100542/

To download TUH abnormal corpus database

• https://isip.piconepress.com/projects/tuh_eeg/

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