Popular GitHub Repositories for Ear Eeg

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

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

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.

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

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

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 (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.

CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification

Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

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

code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"

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 an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

This is my reading list for my PhD in AI, NLP, Deep Learning and more.

NeurIPS2023 - A generic biosignal learning framework. Large EEG pre-trained models.

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 London respectively

[NeurIPS 2025]PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

[TAFFC-2022] PyTorch implementation of TSception v2

Python Brain-Computer Interface Software