Statistics for topic eeg

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Mother of All BCI Benchmarks

🧠 EEG Processing pipeline that annotates continuous data

SPM (Statistical Parametric Mapping) - Development Version

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 Processing pipeline that annotates continuous data

SPM (Statistical Parametric Mapping) - Development Version

Mother of All BCI Benchmarks

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

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...

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Mother of All BCI Benchmarks

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

Residual and bidirectional LSTM for epileptic seizure detection

BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors

Residual and bidirectional LSTM for epileptic seizure detection

Neuroimaging (EEG, fMRI, pupil ...) regression analysis in Julia

🧠 EEG Processing pipeline that annotates continuous data

[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

Residual and bidirectional LSTM for epileptic seizure detection

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

EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.

Residual and bidirectional LSTM for epileptic seizure detection

BioLAb in home to measure EEG, EMG, EKG, EOG, PPG

[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

[TNNLS-2025] This is the pytorch implementation of EmT, a graph-transformer for EEG emotion recognition.

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 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

Residual and bidirectional LSTM for epileptic seizure detection

[TNNLS-2025] This is the pytorch implementation of EmT, a graph-transformer for EEG emotion recognition.

An open source tool for large-scale EEG datasets processing

BioLAb in home to measure EEG, EMG, EKG, EOG, PPG

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 unified multi-task time series model.

Deep learning software to decode EEG, ECG or MEG signals

[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

[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)

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...

This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.