cuijiancorbin / A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG
In this project, we propose a CNN model to classify single-channel EEG for driver drowsiness detection. We use the Class Activation Map (CAM) method for visualization. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.…
☆27Updated 3 years ago
Alternatives and similar repositories for A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG
Users that are interested in A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG are comparing it to the libraries listed below
Sorting:
- 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 3 years ago
- PyTorch code for "Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training"☆16Updated last year
- Code for processing and managing data for EEG-based emotion recognition of individuals with and without Autism. EEG and other clinical da…☆48Updated 3 months ago
- ☆18Updated 4 years ago
- ☆25Updated 5 months ago
- Graph to Grid: Learning Deep Representations for Multimodal Emotion Recognition☆13Updated last year
- ☆30Updated 2 months ago
- EEG motor imagery classification using convolutional neural networks☆35Updated 2 years ago
- EEG Artifact Removal Using Deep Learning (source code, IEEE Journal of Biomedical and Health Informatics)☆34Updated 2 years ago
- bruAristimunha / Re-Deep-Convolution-Neural-Network-and-Autoencoders-Based-Unsupervised-Feature-Learning-of-EEG☆21Updated 2 years ago
- Deep learning model for EEG artifact removal☆70Updated 2 years ago
- ☆20Updated 2 years ago
- ☆14Updated last year
- ☆11Updated last year
- ☆30Updated 9 months ago
- ECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification …☆50Updated 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
- ☆23Updated 5 months ago
- Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals☆43Updated 5 months ago
- This is the PyTorch implementation of the FBMSNet architecture for EEG-MI classification.☆38Updated last year
- Official repository of EEG-Inception, a general-purpose and powerful deep convolutional neural network for EEG procesing☆43Updated last year
- [ACM MM 2023] Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels☆47Updated last month
- Code of paper MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition☆90Updated 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☆37Updated 3 years ago
- for the paper "V-3DCNN, emotion recognition based on EEG"☆13Updated 2 years ago
- [IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BC…☆72Updated 5 months ago
- Code for the paper "EEG Channel Interpolation Using Deep Encoder-decoder Networks"☆26Updated 3 years ago
- Implementation of graph convolutional networks based on PyTorch Geometric to classify EEG signals.☆52Updated 3 years ago
- FBCNet: An Efficient Multi-view Convolutional Neural Network for Brain-Computer Interface☆129Updated last year
- A toolbox for EEG signals processing. Welcome to join and build!☆11Updated 2 years ago