This is the official repository for the research paper
Self-Supervised Representation Learning for
EEG-Based Detection of Neurodegenerative Diseases
Published in Applied Sciences.
In this work, we propose a pretraining approach that combines phase-swap data augmentation, which is designed to facilitate learning EEG phase-amplitude coupling, and a double-masking function that operates at the signal and transformer token levels. The pretraining approach is illustrated below.
To preprocess the selected datasets, we recommend reading and following the instructions in the docs file. These instructions explain how to download datasets from OpenNeuro and preprocess them with BIDSAlign.
If you find the codes and results useful for your research, Please consider citing our work. It would help us continue our research.
Contributors:
- Federico Del Pup
- Louis Fabrice Tshimanga
- Andrea Zanola
- Luca Taffarello
- Elisa Tentori
- Prof. Manfredo Atzori
The code is released under the MIT License