Pinned Repositories
BDCNN
the source code of paper, "Block Division Convolutional Network with Implicit Deep Features Augmentation for Micro-Expression Recognition."
Blind-ECG-Restoration-by-Operational-Cycle-GANs
candock
使用多种神经网络结构(如LSTM，RESNET，DFCNN等)对EEG等生理信号或其他一维数据进行分析及分类
Challenge-condition-FER-dataset
This is our collected datasets for challenge condition facial expression recognition
Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
DEAP-JRP-Emotion-Classification
Emotional Classification with the DEAP dataset using EEGLAB, matlab and python. Currently in the status of developing a more efficient and high accuracy method for emotion classification using EEG data regardless of number of channels.
EEG
EEG-P300Speller-Toolkit
Implemented an EEG processing toolkit; an Ensemble SVM; a stacked RNN and CNN.
EEGLearn-Pytorch
Emotion-Recognition
Emotion recognition from EEG and physiological signals using deep neural networks
MM-YU's Repositories
MM-YU/EEG-P300Speller-Toolkit
Implemented an EEG processing toolkit; an Ensemble SVM; a stacked RNN and CNN.
MM-YU/Emotion-Recognition
Emotion recognition from EEG and physiological signals using deep neural networks
MM-YU/BDCNN
the source code of paper, "Block Division Convolutional Network with Implicit Deep Features Augmentation for Micro-Expression Recognition."
MM-YU/Blind-ECG-Restoration-by-Operational-Cycle-GANs
MM-YU/candock
使用多种神经网络结构(如LSTM，RESNET，DFCNN等)对EEG等生理信号或其他一维数据进行分析及分类
MM-YU/Challenge-condition-FER-dataset
This is our collected datasets for challenge condition facial expression recognition
MM-YU/Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
MM-YU/DEAP-JRP-Emotion-Classification
Emotional Classification with the DEAP dataset using EEGLAB, matlab and python. Currently in the status of developing a more efficient and high accuracy method for emotion classification using EEG data regardless of number of channels.
MM-YU/EEG
MM-YU/EEGLearn-Pytorch
MM-YU/EEGResearchHub
A trusted repository for groundbreaking EEG research code. Some peer-reviewed algorithms (such as EEG data augmentation techniques, EEG classification models) to push the boundaries of neuroscience.
MM-YU/Federated-Transfer-Learning-for-EEG
This is the code of the paper "Federated Transfer Learning for EEG Signal Classification" published in IEEE EMBS 2020 (42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society July 20-24, 2020 via the EMBS Virtual Academy)
MM-YU/github-slideshow
A robot powered training repository :robot:
MM-YU/hello-world
study a new repository on GitHub
MM-YU/LGG
[TNNLS-2023] This is the PyTorch implementation of LGGNet.
MM-YU/Micro-Expression-with-Deep-Learning
Experimentation of deep learning on the subjects of micro-expression spotting and recognition.
MM-YU/PGCN
PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition
MM-YU/pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
MM-YU/scikit-learn
scikit-learn: machine learning in Python
MM-YU/SFAMNet
SFAMNet: A Scene Flow Attention-based Micro-expression Network
MM-YU/SSPNet
SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images.
MM-YU/STRCN-MicroExpressionRec
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions
MM-YU/test
test repository
MM-YU/TRAR-VQA
This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task
MM-YU/TSception
PyTorch implementation of TSception