Statistics for topic eeg
RepositoryStats tracks 598,732 Github repositories, of these 131 are tagged with the eeg topic. The most common primary language for repositories using this topic is Python (74). Other languages include: Jupyter Notebook (18), MATLAB (15)
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MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting (CHIL 2022)
[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.
python implementations of Analyzing Neural Time Series Textbook
Attention temporal convolutional network for EEG-based motor imagery classification
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting (CHIL 2022)
[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.
python implementations of Analyzing Neural Time Series Textbook
Attention temporal convolutional network for EEG-based motor imagery classification
BioAmp EXG Pill is a small and elegant Analog Front End (AFE) board for BioPotential signal acquisition.
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting (CHIL 2022)
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.
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.
Deep learning software to decode EEG, ECG or MEG signals
[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)
[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.
[Arxiv] 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
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
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
[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...