美国陆军研究实验室（ARL）EEGModels 项目：一组用于 EEG 信号分类的卷积神经网络（CNN）模型，基于 Keras 实现
Braindecode：用于解码 EEG、ECG、MEG 信号的深度学习软件
Time Series Feature Extraction Library (TSFEL): 60+ statistical, temporal, spectral and fractal features for time series classification, regression and signal processing in Python
MOABB：脑机接口基准评测「集大成者」
pyRiemann：借助正定矩阵的黎曼几何对多变量数据进行机器学习的 Python 库
EEGLearn：基于「EEG 图像」思想、对 EEG 心理状态进行有监督特征学习/分类的函数集
[TNSRE 23] EEG Conformer：用于 EEG 解码的卷积 Transformer，并提出类激活地形图新型可视化
IronBCI：可穿戴（BLE）脑机接口，基于 ADS1299 与 STM32，含移动应用 SDK
[IJCAI-21]「基于时序与上下文对比的时间序列表征学习」（TS-TCC）
CTNet：用于 EEG 运动想象分类的卷积 Transformer 网络
EEGNet 的 PyTorch 实现（旧版）：面向 EEG 脑机接口的紧凑卷积网络
AntroPy：计算（EEG）时间序列熵与复杂度的 Python 库
[ICLR 2025] CBraMod：用于 EEG 解码的交叉注意力脑基础模型
EEG-ATCNet：用于 EEG 运动想象分类的注意力时间卷积网络
EEG-Transformer：Transformer（ViT）在二维生理信号（EEG）分类上的实用应用，也可用于 EMG、EOG、ECG 等
（已过时，请见新版 braindecode）
《基于深度学习的 EEG 脑机接口：表征、算法与应用》配套资源
DeepEEG：用 TensorFlow 对 EEG MNE Epoch 对象进行深度学习
[TNSRE 2021] AttnSleep：基于注意力机制、使用单通道 EEG 的睡眠分期深度学习方法
SleepEEGNet：基于序列到序列深度学习的自动睡眠分期
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"
[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.
A Python Toolbox for Multimode Neural Data Representation Analysis - A Representational Analysis Toolbox for Neuroscience, including Representational Similarity Analysis (RSA), & Inter-Subject Correla
A ViT based transformer applied on multi-channel time-series EEG data for motor imagery classification
[NeurIPS 2025] PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation
EEG Sleep stage classification using CNN with Keras
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
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
This is my reading list for my PhD in AI, NLP, Deep Learning and more.
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
Residual and bidirectional LSTM for epileptic seizure detection
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks an
:bar_chart: EEG signal processing and machine learning in JavaScript
EntroPy: complexity of time-series in Python (DEPRECATED)
EEG-based emotion classification using DEAP dataset
Textbook for NESC 3505, Neural Data Science, at Dalhousie University
[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)