cuijiancorbin / Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI
In this project, we implemented 7 interpretation techniques on two benchmark deep learning models "EEGNet" and "InterpretableCNN" for EEG-based BCI. The methods include: gradient×input, DeepLIFT, integrated gradient, layer-wise relevance propagation (LRP), saliency map, deconvolution, and guided backpropagation
☆15Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI
- SPD-CNN: A Plain CNN-Based Model Using the Symmetric Positive Definite Matrices for Cross-Subject EEG Classification with Meta-Transfer-L…☆16Updated 2 years ago
- Towards Domain Free Transformer for Generalized EEG Pre-training☆22Updated 10 months ago
- Temporal information enhanced EEGNet☆13Updated last year
- Benchmark of data augmentations for EEG (code from Rommel, Paillard, Moreau and Gramfort, "Data augmentation for learning predictive mode…☆39Updated 2 years ago
- CNN-Former model with EEG-ME for SSVEP classification☆11Updated 2 months ago
- Code to accompany our International Conference on Pattern Recognition (ICPR) paper entitled - Leveraging Synthetic Subject Invariant EEG …☆40Updated 5 months ago
- ☆11Updated 8 months ago
- This is the python implementation of Tensor-CSPNet and Graph-CSPNet.☆58Updated 7 months ago
- Code for processing EEG data with Riemannian and deep learning-based classifiers. Additionally provides methods for data augmentation inc…☆27Updated 4 years ago
- NeurIPS 2021 - Benchmarks for EEG Transfer Learning - cross-subject sleep stage decoding, cross-dataset motor imagery decoding☆12Updated 2 years ago
- High-East / Attention-based-spatio-temporal-spectral-feature-learning-for-subject-specific-EEG-classificationOfficial code for "Attention-Based Spatio-Temporal-Spectral Feature Learning for Subject-Specific EEG Classification" paper☆37Updated 3 years ago
- A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)☆36Updated 5 months ago
- ☆55Updated last year
- Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals☆38Updated 3 years ago
- bruAristimunha / Re-Deep-Convolution-Neural-Network-and-Autoencoders-Based-Unsupervised-Feature-Learning-of-EEG☆22Updated 2 years ago
- This is the PyTorch implementation of the FBMSNet architecture for EEG-MI classification.☆34Updated last year
- ☆12Updated last year
- ☆26Updated 2 months ago
- code for NeurIPS_competition☆27Updated 2 years ago
- ☆17Updated 4 months ago
- MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding☆13Updated 2 months ago
- Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification☆40Updated last year
- ☆14Updated last year
- Implementation of graph convolutional networks based on PyTorch Geometric to classify EEG signals.☆49Updated 3 years ago
- ☆22Updated 3 months ago
- source codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Indu…☆46Updated 2 years ago
- In AugmentBrain we investigate the performance of different data augmentation methods for the classification of Motor Imagery (MI) data u…☆22Updated 3 years ago
- Classification of EEG signals into silence vs listening☆19Updated 2 years ago
- ECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification …☆47Updated 3 years ago
- Repository of EEG-Simpleconv original paper☆33Updated 11 months ago