Trending repositories for topic eeg
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
A list of openly available datasets in (mostly human) electrophysiology.
A curated list of awesome neuroscience libraries, software and any content related to the domain.
A list of openly available datasets in (mostly human) electrophysiology.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
A curated list of awesome neuroscience libraries, software and any content related to the domain.
Deep learning software to decode EEG, ECG or MEG signals
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
A curated list of awesome neuroscience libraries, software and any content related to the domain.
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
A list of openly available datasets in (mostly human) electrophysiology.
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Multimodal Dataset of Freezing of Gait in Parkinson's Disease
Source Code for "Adaptive Transfer Learning with Deep CNN for EEG 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.
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
AntroPy: entropy and complexity of (EEG) time-series in Python
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
Multimodal Dataset of Freezing of Gait in Parkinson's Disease
Source Code for "Adaptive Transfer Learning with Deep CNN for EEG 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.
A list of openly available datasets in (mostly human) electrophysiology.
Deep learning software to decode EEG, ECG or MEG signals
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
AntroPy: entropy and complexity of (EEG) time-series in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
A curated list of awesome neuroscience libraries, software and any content related to the domain.
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
Deep learning software to decode EEG, ECG or MEG signals
A curated list of awesome neuroscience libraries, software and any content related to the domain.
A list of openly available datasets in (mostly human) electrophysiology.
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
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
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
Attention temporal convolutional network for EEG-based motor imagery classification
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
[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.
Brainstorm software: MEG, EEG, fNIRS, ECoG, sEEG and electrophysiology
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semanti...
[TNNLS-2025] This is the pytorch implementation of EmT, a graph-transformer for EEG emotion recognition.
Neuroimaging (EEG, fMRI, pupil ...) regression analysis in Julia
[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
[IEEE JBHI] "MultiChannelSleepNet: A Transformer-Based Model for Automatic Sleep Stage Classification With PSG"
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
[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.
Feature extraction and prediction of human neural signals, using chaos theory, dynamical systems theory, various physics/maths theories, RNN, CNN, transformers
[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 is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
A curated list of awesome neuroscience libraries, software and any content related to the domain.
A list of openly available datasets in (mostly human) electrophysiology.
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
Attention temporal convolutional network for EEG-based motor imagery classification
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.
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
[BIBM2024] MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning
[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semanti...
[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.
This project demonstrates various signal processing techniques, such as signal generation, window functions, filtering, downsampling, zero-padding, and the application of time-frequency analysis using...
A trusted repository for groundbreaking EEG research code. Some peer-reviewed algorithms (such as EEG data augmentation techniques, EEG classification models) to push the boundaries of neuroscience.
This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer...
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
GGN model for seizure classification (datasets: TUH EEG seizure TUSZ 1.5.2)
selfEEG: a Python library for Self-Supervised Learning on Electroencephalography (EEG) data
Feature extraction and prediction of human neural signals, using chaos theory, dynamical systems theory, various physics/maths theories, RNN, CNN, transformers
Attention temporal convolutional network for EEG-based motor imagery classification