Trending repositories for topic eeg
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
Deep learning software to decode EEG, ECG or MEG signals
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
[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 the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with...
Attention temporal convolutional network for EEG-based motor imagery classification
Parameterizing neural power spectra into periodic & aperiodic components.
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
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
[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 the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with...
Attention temporal convolutional network for EEG-based motor imagery classification
Parameterizing neural power spectra into periodic & aperiodic components.
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.
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
A curated list of awesome neuroscience libraries, software and any content related to the domain.
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.
[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.
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...
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
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with...
Attention temporal convolutional network for EEG-based motor imagery classification
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.
AntroPy: entropy and complexity of (EEG) time-series in Python
Parameterizing neural power spectra into periodic & aperiodic components.
Brainstorm software: MEG, EEG, fNIRS, ECoG, sEEG and electrophysiology
[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
[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.
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...
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with...
Attention temporal convolutional network for EEG-based motor imagery classification
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
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.
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
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
A curated list of awesome neuroscience libraries, software and any content related to the domain.
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Parameterizing neural power spectra into periodic & aperiodic components.
Brainstorm software: MEG, EEG, fNIRS, ECoG, sEEG and electrophysiology
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
[DEAP] Attention-Based Temporal Learner With Dynamical Graph Neural Network for EEG Emotion Recognition.
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
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
[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.
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...
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
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
[DEAP] Attention-Based Temporal Learner With Dynamical Graph Neural Network for EEG Emotion Recognition.
[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
selfEEG: a Python library for Self-Supervised Learning on Electroencephalography (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.
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
A multitaper spectral estimation toolbox implemented in Matlab, Python, and R
[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.
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)
Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction
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...
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 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...
TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG by Akara Supratak and Yike Guo from The Faculty of ICT, Mahidol University and Imperial College L...
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...
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.
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.
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...
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Not supported. Measure 8 EEG channels with Shield PiEEG and RaspberryPi in C library
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
[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.
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...
Local-first behavior tracking and brain recording app. Discord: https://discord.gg/ndcf3b5cyU
[IEEE JBHI] "MultiChannelSleepNet: A Transformer-Based Model for Automatic Sleep Stage Classification With PSG"
GGN model for seizure classification (datasets: TUH EEG seizure TUSZ 1.5.2)
Textbook for NESC 3505, Neural Data Science, at Dalhousie University
Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction
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.
PyTorch and Loihi implementation of the Spiking Neural Network for decoding EEG on Neuromorphic Hardware
Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals
[IEEE BHI 2022] Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications