EEGViT-TCNet on Cleaned EEGEyeNet Dataset

Overview

EEGViT is a hybrid Vision Transformer (ViT) incorporated with Depthwise Convolution in patch embedding layers. This work is based on Dosovitskiy, et al.'s "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale". After finetuning EEGViT pretrained on ImageNet, it achieves a considerable improvement over the SOTA on the Absolute Position task in EEGEyeNet dataset.

This repository consists of five models: ViT pretrained and non-pretrained; EEGViT pretrained and non-pretrained; EEGViT-TCNet pretrained. The pretrained weights of ViT layers are loaded from huggingface.co.

Dataset download

Download data for EEGEyeNet absolute position task

wget -O "./dataset/Position_task_with_dots_synchronised_min.npz" "https://osf.io/download/ge87t/"

For more details about EEGEyeNet dataset, please refer to "EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction" and OSF repository

Installation

Requirements

First install the general_requirements.txt

pip3 install -r general_requirements.txt 

Pytorch Requirements

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

For other installation details and different cuda versions, visit pytorch.org.

Remaining Appendix Citations for "Enhancing EEG Data Quality: A Comprehensive Review of Outlier Detection and Cleaning Methods"

• Mayeli, Ahmad, et al. "Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI." Journal of Neural Engineering 18, no. 4 (2021): 0460b4. Available at: https://iopscience.iop.org/article/10.1088/1741-2552/ac1037/meta

• Harishvijey, A., and J. Benadict Raja. "Automated technique for EEG signal processing to detect seizure with optimized Variable Gaussian Filter and Fuzzy RBFELM classifier." Biomedical Signal Processing and Control 74 (2022): 103450. Available at: https://www.sciencedirect.com/science/article/abs/pii/S1746809421010478

• Bajaj, Nikesh, et al. "Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks." Biomedical Signal Processing and Control 55 (2020): 101624. Available at: https://www.sciencedirect.com/science/article/abs/pii/S1746809419302058

• Haresign, I. Marriott, et al. "Automatic classification of ICA components from infant EEG using MARA." Developmental Cognitive Neuroscience 52 (2021): 101024. Available at: https://www.sciencedirect.com/science/article/pii/S1878929321001146

• Yasoda, K., et al. "Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA)." Soft Computing 24, no. 21 (2020): 16011-16019. Available at: https://link.springer.com/article/10.1007/s00500-020-04920-w

• Phadikar, Souvik, Nidul Sinha, and Rajdeep Ghosh. "Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder." IET Signal Processing 14, no. 6 (2020): 396-405. Available at: https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-spr.2020.0025

• Lopes, Fábio, et al. "Automatic electroencephalogram artifact removal using deep convolutional neural networks." IEEE Access 9 (2021): 149955-149970. Available at: https://ieeexplore.ieee.org/abstract/document/9605576

• Phadikar, Souvik, Nidul Sinha, and Rajdeep Ghosh. "Automatic eyeblink artifact removal from EEG signal using wavelet transform with heuristically optimized threshold." IEEE Journal of Biomedical and Health Informatics 25, no. 2 (2020): 475-484. Available at: https://ieeexplore.ieee.org/abstract/document/9095264

• Vidal, Marc, Mattia Rosso, and Ana M. Aguilera. "Bi-smoothed functional independent component analysis for EEG artifact removal." Mathematics 9, no. 11 (2021): 1243. Available at: https://www.mdpi.com/2227-7390/9/11/1243

• Ranjan, Rakesh, Bikash Chandra Sahana, and Ashish Kumar Bhandari. "Cardiac artifact noise removal from sleep EEG signals using hybrid denoising model." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1-10. Available at: https://ieeexplore.ieee.org/abstract/document/9855513

• Hwaidi, Jamal F., and Thomas M. Chen. "Classification of motor imagery EEG signals based on deep autoencoder and convolutional neural network approach." IEEE Access 10 (2022): 48071-48081. Available at: https://ieeexplore.ieee.org/abstract/document/9766103

• Martini, Michael L., et al. "Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings." Scientific Reports 11, no. 1 (2021): 7482. Available at: https://www.nature.com/articles/s41598-021-86891-y

• Mashhadi, Najmeh, et al. "Deep learning denoising for EOG artifacts removal from EEG signals." 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2020. Available at: https://ieeexplore.ieee.org/abstract/document/9342884

• Brophy, Eoin, et al. "Denoising EEG signals for real-world BCI applications using GANs." Frontiers in Neuroergonomics 2 (2022): 805573. Available at: https://www.frontiersin.org/articles/10.3389/fnrgo.2021.805573/full

• Çınar, Salim. "Design of an automatic hybrid system for removal of eye-blink artifacts from EEG recordings." Biomedical Signal Processing and Control 67 (2021): 102543. Available at: https://www.sciencedirect.com/science/article/abs/pii/S1746809421001403

• Rogasch, Nigel C., Mana Biabani, and Tuomas P. Mutanen. "Designing and comparing cleaning pipelines for TMS-EEG data: A theoretical overview and practical example." Journal of Neuroscience Methods 371 (2022): 109494. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0165027022000218

• Charupanit, Krit, et al. "Detection of anomalous high‐frequency events in human intracranial EEG." Epilepsia Open 5, no. 2 (2020): 263-273. Available at: https://onlinelibrary.wiley.com/doi/full/10.1002/epi4.12397

• Abdi-Sargezeh, Bahman, et al. "EEG artifact rejection by extracting spatial and spatio-spectral common components." Journal of Neuroscience Methods 358 (2021): 109182. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0165027021001175

• Lee, Sangmin S., Kiwon Lee, and Guiyeom Kang. "EEG artifact removal by Bayesian deep learning ,ICA." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine , Biology Society (EMBC). IEEE, 2020. Available at: https://ieeexplore.ieee.org/abstract/document/9175785

• Salo, Karita S-T., et al. "EEG artifact removal in TMS studies of cortical speech areas." Brain Topography 33 (2020): 1-9. Available at: https://link.springer.com/article/10.1007/s10548-019-00724-w

• Zangeneh Soroush, Morteza, et al. "EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms." Frontiers in Physiology 13 (2022): 910368. Available at: [https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2022.910368