eeg-classification - 技术专题深度解读

vlawhern / arl-eegmodels

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 tensorflow keras eeg convolutional-neural-networks brain-computer-interface event-related-potentials time-series-classification eeg-classification sensory-motor-rhythm

Updated May 2, 2022
Python

SuperBruceJia / EEG-DL

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

Updated Jul 20, 2025
Python

pieeg-club / ironbci

Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application

stm32 eeg eeg-signals eeg-data bci eeg-headset bci-systems eeg-classification eeg-signals-processing ads1299 bci-homework ironbci

Updated Sep 23, 2026
Python

snailpt / CTNet

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

cnn transformer attention bci brain-computer-interface eeg-analysis motor-imagery eeg-classification ctnet eeg-transformer mi-eeg eeg-decoding mi-eeg-classification conv-transformer

Updated Mar 21, 2026
Jupyter Notebook

aliasvishnu / EEGNet

[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf

pytorch convolutional-neural-networks bci speller brain-computer-interface p300 eeg-analysis eeg-classification eegnet

Updated Jul 10, 2019
Jupyter Notebook

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.

deep-learning eeg transformer attention vit attention-mechanism physiological-signals common-spatial-pattern eeg-classification

Updated Mar 23, 2023
Python

emadeldeen24 / AttnSleep

[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

pytorch eeg attention eeg-classification sleep-staging self-attention sleep-stage-scoring sleep-stage-classification

Updated Sep 26, 2023
Python

SuperBruceJia / EEG-Motor-Imagery-Classification-CNNs-TensorFlow

EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow

python tensorflow matlab eeg eeg-signals esi tensorflow-experiments convolutional-neural-networks eeg-data brain-computer-interface motor-imagery-classification tensorflow-models motor-imagery-training cnns eeg-analysis motor-imagery eeg-classification brain-com motor-imagery-tasks

Updated May 20, 2024
Python

MikeWangWZHL / EEG-To-Text

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

language sentiment-analysis bart eeg-signals language-model brain-computer-interface bert eeg-classification

Updated Jun 30, 2024
Python

snailpt / MSCFormer

Multi-Scale Convolutional Transformer Network for Motor Imagery Brain-Computer Interface

motor-imagery-classification eeg-classification transformer-architecture multi-scale-convolutional-neural-network

Updated Oct 2, 2026
Jupyter Notebook

Piyush-Bhardwaj / EEG-based-emotion-analysis-using-DEAP-dataset-for-Supervised-Machine-Learning

This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K - Nearest Neighbor.

machine-learning supervised-learning svm-classifier knn-classification eeg-classification deap-dataset

Updated Mar 1, 2019
Python

zwcolin / EEG-Transformer

A ViT based transformer applied on multi-channel time-series EEG data for motor imagery classification

transformer vit bci eeg-classification

Updated May 10, 2025
Python

neerajwagh / eeg-gcnn

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.