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
☆16Updated 2 years ago
Alternatives and similar repositories for Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI:
Users that are interested in Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI are comparing it to the libraries listed below
- Code for processing EEG data with Riemannian and deep learning-based classifiers. Additionally provides methods for data augmentation inc…☆27Updated 4 years ago
- An all-in-one EEG feature extraction toobox, including statistical features, Hjorth parameters, entropy, nonlinear features, power spectr…☆37Updated last year
- Code to accompany our International Conference on Pattern Recognition (ICPR) paper entitled - Leveraging Synthetic Subject Invariant EEG …☆41Updated 8 months ago
- This is the python implementation of Tensor-CSPNet and Graph-CSPNet.☆62Updated 3 months ago
- Towards Domain Free Transformer for Generalized EEG Pre-training☆26Updated last year
- 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☆38Updated 3 years ago
- ☆20Updated 3 months ago
- ☆13Updated last year
- SPD-CNN: A Plain CNN-Based Model Using the Symmetric Positive Definite Matrices for Cross-Subject EEG Classification with Meta-Transfer-L…☆21Updated 2 years ago
- source codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Indu…☆50Updated 2 years ago
- This is the PyTorch implementation of the FBMSNet architecture for EEG-MI classification.☆35Updated last year
- Implementation of graph convolutional networks based on PyTorch Geometric to classify EEG signals.☆51Updated 3 years ago
- Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals☆41Updated 3 months ago
- ☆58Updated last year
- ☆12Updated 2 years ago
- Temporal information enhanced EEGNet☆13Updated 2 years ago
- Offical repository of the paper "EEG-DG：A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification" .☆20Updated 5 months ago
- Benchmark of data augmentations for EEG (code from Rommel, Paillard, Moreau and Gramfort, "Data augmentation for learning predictive mode…☆42Updated 2 years ago
- Existing work in the field of BCI treats deep learning models as black-box classifiers. In this project, we develop a novel model named "…☆19Updated 2 years ago
- Construct a model, which contains channel-attention, CNN, LSTM, self-attention, to classify EEG data;☆10Updated 3 years ago
- Seeking to recreate the work demonstrated in A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification by…☆18Updated 3 years ago
- ☆9Updated 3 years ago
- ☆28Updated 7 months ago
- ECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification …☆48Updated 3 years ago
- ☆16Updated last year
- http://www.eecs.qmul.ac.uk/mmv/datasets/deap/☆28Updated 4 years ago
- code for NeurIPS_competition☆27Updated 3 years ago
- ☆18Updated 4 years ago
- code for LMDA☆72Updated last year