This is the official repository for the research paper
TransformEEG: Towards Improving Model Generalizability in
Deep Learning-based EEG Parkinson's Disease Detection
Submitted to Neurocomputing.
In this work, we propose TransformEEG, a hybrid Convolutional-Transformer designed for PD detection using EEG data. Unlike transformer models based on the EEGNet structure, TransformEEG incorporates a depthwise convolutional tokenizer. This tokenizer is specialized in generating tokens composed by channel-specific features, which enables more effective feature mixing within the self-attention layers of the transformer encoder.
The paper describes in the detail the experimental methodology. Here, we report a brief description of the key points.
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.
The architecture of the model is schematized in the following figure.
A pytorch implementation can be found in the AllFnc/models.py file.
To evaluate the proposed model, four public datasets comprising 290 subjects (140 PD patients, 150 healthy controls) were harmonized and aggregated. A 10-outer, 10-inner Nested-Leave-N-Subjects-Out (N-LNSO) cross-validation was performed to provide an unbiased comparison against seven other consolidated EEG deep learning models.
The list of EEG-DL models included in the comparison, sorter by their number of learnable parameters in ascending order, is reported below.
- xEEGNet
- EEGNet
- ShallowNet
- ATCNet
- EEGConformer
- DeepConvNet
- EEGResNet
The following figure schematizes the N-LNSO CV procedure.
Results are presented in the paper.
- Model comparison with a basic pipeline without data augmentation
- Model comparison with a pipeline that includes an optimal selected data augmentation
The scripts used to generate the results presented in the paper are available in this repository, which is derived from the following GitHub repo associated with another study we published.
Performance metrics of each trained model are collected and organized in the ResultsTable.csv file. Due to the large number of training instances, model weights and results are not directly stored in this repository. We plan to release them in zenodo.
If you find the codes and results useful for your research, Please consider citing our work. It would help us continue our research.
The current version of the paper is available on Arxiv.
Contributors:
- Federico Del Pup
- Riccardo Brun
- Filippo Iotti
- Edoardo Paccagnella
- Mattia Pezzato
- Sabrina Bertozzo
- Andrea Zanola
- Louis Fabrice Tshimanga
- Prof. Hennning Müller
- Prof. Manfredo Atzori
The code is released under the MIT License