Trending repositories for topic eeg
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.
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
A curated list of awesome neuroscience libraries, software and any content related to the domain.
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
A curated list of awesome neuroscience libraries, software and any content related to the domain.
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
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Brainconn - an R package for visualising brain connectivity data in 2D and interactive 3D
MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.
Attention temporal convolutional network for EEG-based motor imagery classification
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Brainconn - an R package for visualising brain connectivity data in 2D and interactive 3D
MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.
A curated list of awesome neuroscience libraries, software and any content related to the domain.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
Attention temporal convolutional network for EEG-based motor imagery classification
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
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
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.
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
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
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
A list of openly available datasets in (mostly human) electrophysiology.
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
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
Attention temporal convolutional network for EEG-based motor imagery classification
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
[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.
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting (CHIL 2022)
BioAmp EXG Pill is a small and elegant Analog Front End (AFE) board for BioPotential signal acquisition.
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Multimodal Dataset of Freezing of Gait in Parkinson's Disease
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting (CHIL 2022)
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
[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.
GGN model for seizure classification (datasets: TUH EEG seizure TUSZ 1.5.2)
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
Feature extraction and prediction of human neural signals, using chaos theory, dynamical systems theory, various physics/maths theories, RNN, CNN, transformers
[DEAP] Attention-Based Temporal Learner With Dynamical Graph Neural Network for EEG Emotion Recognition.
Attention temporal convolutional network for EEG-based motor imagery classification
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...
Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
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.
[DEAP] Attention-Based Temporal Learner With Dynamical Graph Neural Network for EEG Emotion Recognition.
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
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...
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
Deep learning software to decode EEG, ECG or MEG signals
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.
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
A list of openly available datasets in (mostly human) electrophysiology.
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
[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.
Attention temporal convolutional network for EEG-based motor imagery classification
[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
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
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...
Not supported. Measure 8 EEG channels with Shield PiEEG and RaspberryPi in C library
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
[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 focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
selfEEG: a Python library for Self-Supervised Learning on Electroencephalography (EEG) data
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...
Feature extraction and prediction of human neural signals, using chaos theory, dynamical systems theory, various physics/maths theories, RNN, CNN, transformers
GGN model for seizure classification (datasets: TUH EEG seizure TUSZ 1.5.2)
Attention temporal convolutional network for EEG-based motor imagery classification
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction
PyTorch and Loihi implementation of the Spiking Neural Network for decoding EEG on Neuromorphic Hardware
Textbook for NESC 3505, Neural Data Science, at Dalhousie University
[IEEE JBHI] "MultiChannelSleepNet: A Transformer-Based Model for Automatic Sleep Stage Classification With PSG"
Local-first behavior tracking and brain recording app. Discord: https://discord.gg/ndcf3b5cyU