Statistics for topic eeg
RepositoryStats tracks 633,451 Github repositories, of these 139 are tagged with the eeg topic. The most common primary language for repositories using this topic is Python (81). Other languages include: Jupyter Notebook (18), MATLAB (16)
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NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
[ICLR 2024] M/EEG-based image decoding with contrastive learning. i. Propose a contrastive learning framework to align image and eeg. ii. Resolving brain activity for biological plausibility.
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
Attention temporal convolutional network for EEG-based motor imagery classification
[ICLR 2024] M/EEG-based image decoding with contrastive learning. i. Propose a contrastive learning framework to align image and eeg. ii. Resolving brain activity for biological plausibility.
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
Attention temporal convolutional network for EEG-based motor imagery classification
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
Attention temporal convolutional network for EEG-based motor imagery classification
i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (c...
[IEEE BHI 2022] Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning
[TNNLS-2025] This is the pytorch implementation of EmT, a graph-transformer for EEG emotion recognition.
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
[ICLR 2024] M/EEG-based image decoding with contrastive learning. i. Propose a contrastive learning framework to align image and eeg. ii. Resolving brain activity for biological plausibility.
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
Deep learning software to decode EEG, ECG or MEG signals
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
[TNNLS-2025] This is the pytorch implementation of EmT, a graph-transformer for EEG emotion recognition.
[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
GGN model for seizure classification (datasets: TUH EEG seizure TUSZ 1.5.2)
[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
[BIBM2024] MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
[TNNLS-2025] This is the pytorch implementation of EmT, a graph-transformer for EEG emotion recognition.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Deep learning software to decode EEG, ECG or MEG signals
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
[ICLR 2024] M/EEG-based image decoding with contrastive learning. i. Propose a contrastive learning framework to align image and eeg. ii. Resolving brain activity for biological plausibility.
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
