Trending repositories for topic eeg
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
A list of openly available datasets in (mostly human) electrophysiology.
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
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
source codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Industrial Informatics)
[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
BioAmp EXG Pill is a small and elegant Analog Front End (AFE) board for BioPotential signal acquisition.
Deep learning software to decode EEG, ECG or MEG signals
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
source codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Industrial Informatics)
A list of openly available datasets in (mostly human) electrophysiology.
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
BioAmp EXG Pill is a small and elegant Analog Front End (AFE) board for BioPotential signal acquisition.
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
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
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
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
A list of openly available datasets in (mostly human) electrophysiology.
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
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
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
source codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Industrial Informatics)
Interactive Brain Playground - Browser based tutorials on EEG with webbluetooth and muse
[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
BioAmp EXG Pill is a small and elegant Analog Front End (AFE) board for BioPotential signal acquisition.
source codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Industrial Informatics)
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
A list of openly available datasets in (mostly human) electrophysiology.
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
Interactive Brain Playground - Browser based tutorials on EEG with webbluetooth and muse
[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Deep learning software to decode EEG, ECG or MEG signals
BioAmp EXG Pill is a small and elegant Analog Front End (AFE) board for BioPotential signal acquisition.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
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
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
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
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
A curated list of awesome neuroscience libraries, software and any content related to the domain.
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
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.
Deep learning software to decode EEG, ECG or MEG signals
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
Attention temporal convolutional network for EEG-based motor imagery classification
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
AntroPy: entropy and complexity of (EEG) time-series in Python
Not supported. Measure 8 EEG channels with Shield PiEEG and RaspberryPi in C library
[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with 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.
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
Code for the paper "Multi-Task CNN Model for Emotion Recognition from EEG Brain Maps". DEAP dataset. Python/Keras/Tensorflow 2 Impementation.
selfEEG: a Python library for Self-Supervised Learning on Electroencephalography (EEG) data
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Brainconn - an R package for visualising brain connectivity data in 2D and interactive 3D
source codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Industrial Informatics)
A list of openly available datasets in (mostly human) electrophysiology.
Attention temporal convolutional network for EEG-based motor imagery classification
A research repository of deep learning on electroencephalographic (EEG) for Motor imagery(MI), including eeg data processing(visualization & analysis), papers(research and summary), deep learning mode...
A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
PyPREP: A Python implementation of the Preprocessing Pipeline (PREP) for EEG data
CTNet: A Convolutional Transformer 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.
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Developed the Neuro Vision Transformer, an innovative machine learning model utilizing Vision Transformer architecture for EEG signal classification, focusing on distinguishing ADHD from healthy parti...
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.
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
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.
Not supported. Measure 8 EEG channels with Shield PiEEG and RaspberryPi in C library
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
[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
A list of openly available datasets in (mostly human) electrophysiology.
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
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...
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
Local-first behavior tracking and brain recording app. Discord: https://discord.gg/sqsuzXWE3K
A research repository of deep learning on electroencephalographic (EEG) for Motor imagery(MI), including eeg data processing(visualization & analysis), papers(research and summary), deep learning mode...
[IEEE JBHI] "MultiChannelSleepNet: A Transformer-Based Model for Automatic Sleep Stage Classification With PSG"
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Attention temporal convolutional network for EEG-based motor imagery classification
Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction
GGN model for seizure classification (datasets: TUH EEG seizure TUSZ 1.5.2)
Exploration of diffuison-based generative model to sychronizing brain dynamics from semantic language input.
Textbook for NESC 3505, Neural Data Science, at Dalhousie University
Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals
PyTorch and Loihi implementation of the Spiking Neural Network for decoding EEG on Neuromorphic Hardware
Code for the paper "Multi-Task CNN Model for Emotion Recognition from EEG Brain Maps". DEAP dataset. Python/Keras/Tensorflow 2 Impementation.
Analysis of Transformer attention in EEG signal 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...
Multimodal Dataset of Freezing of Gait in Parkinson's Disease