gzoumpourlis / BEETL_NeurIPS_2021
Submission to the Motor Imagery track of BEETL competition, organized within NeurIPS 2021
☆11Updated 2 years ago
Alternatives and similar repositories for BEETL_NeurIPS_2021:
Users that are interested in BEETL_NeurIPS_2021 are comparing it to the libraries listed below
- Open implementation and code from the publication "EEGSym: Overcoming Intersubject Variability in Motor Imagery Based BCIs with Deep Lear…☆23Updated last year
- PyTorch code for "Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training"☆14Updated last year
- Resources for the paper titled "Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizab…☆21Updated last year
- ☆15Updated 3 months ago
- 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
- CNN-Former model with EEG-ME for SSVEP classification (TNSRE)☆12Updated 6 months ago
- Classification of EEG signals into silence vs listening☆20Updated 2 years ago
- Benchmark of data augmentations for EEG (code from Rommel, Paillard, Moreau and Gramfort, "Data augmentation for learning predictive mode…☆42Updated 2 years ago
- ☆59Updated last year
- ☆24Updated 2 months ago
- code for NeurIPS_competition☆27Updated 3 years ago
- Temporal information enhanced EEGNet☆13Updated 2 years ago
- This repository is the official implementation of “Different Set Domain Adaptation for Brain Computer Interfaces: A Label Alignment Appro…☆21Updated 3 years ago
- Athena is a library that comprises many different bci frameworks that perform classification on a set of eeg data.☆20Updated 2 years ago
- ☆11Updated last year
- ☆11Updated last year
- ☆13Updated last year
- META-EEG: Meta-learning-based Class-relevant EEG Representation Learning for Zero-calibration Brain-computer Interfaces☆15Updated 8 months ago
- Self-supervised learning for EEG☆24Updated 4 years ago
- ☆18Updated 4 years ago
- Code for processing EEG data with Riemannian and deep learning-based classifiers. Additionally provides methods for data augmentation inc…☆28Updated 4 years ago
- ☆10Updated last year
- selfEEG: a Python library for Self-Supervised Learning on Electroencephalography (EEG) data☆51Updated last month
- 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
- ☆27Updated last week
- How EEG preprocessing shapes decoding performance☆12Updated last week
- Using GAN to create synthetic and partially synthetic EEG data to augment training sets for motor imagery interaction tasks☆12Updated 5 years ago
- Code for the paper "EEG Channel Interpolation Using Deep Encoder-decoder Networks"☆25Updated 2 years ago
- ☆9Updated 3 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