YYlying (Bule-study-y)

Bule-study-y

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Company:Universary

Location:UM

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YYlying's repositories

EEG-based-Emotion-Recognition-with-Feature-Fusion-Networks

EEG-based Emotion Recognition with Feature Fusion Networks

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SST-EmotionNet

SST-EmotionNet: Spatial-Spectral-Temporal based Attention 3D Dense Network for EEG Emotion Recognition

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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

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BigGAN-PyTorch

The author's officially unofficial PyTorch BigGAN implementation.

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deap-eeg-classification

EEG-based emotion classification using DEAP dataset

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Deep-Forest

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

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deep-transfer-learning

A collection of implementations of deep domain adaptation algorithms

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documentDownloader

download document from book118 for free

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EEG-Datasets

A list of all public EEG-datasets

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EEG-emotions

Application prepares data to learning process. Including preprocessing, cleaning, reformating, feature extraction using PyEEG library and learning using Sklearn tool.

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eeg-feeling-emotions-LSTM

EEG Feeling Emotions Classification using LSTM

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eeg-processing-toolbox

Matlab code for proccesing EEG signals.

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EEGRAPH

EEGraph: Convert EEGs to graphs with frequency and time-frequency domain connectivity measures.

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emokit

Open source driver for accessing raw data from the Emotiv EPOC EEG headset

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emotion-classifcation-eeg-seed-ensemble

Using Deep Learning for Emotion Classification on EEG signals (SEED Dataset). CNN, RNN, Hybrid model, and Ensemble

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GazeToolkit

Toolkit for processing eye movement data, fixation filtering, smoothing, etc.

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ijcnn

This repository contains the tensorflow implementation for the paper: "Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network"

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JDA

Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity

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Machine-Learning

机器学习

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ML-NLP

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。

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MLF-CapsNet

Multi-channel EEG-based Emotion Recognition via a Multi-level Features Guided Capsule Network

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models

Pre-trained and Reproduced Deep Learning Models (『飞桨』官方模型库,包含多种学术前沿和工业场景验证的深度学习模型)

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mRMR

Max Relevance and Min Redundancy

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pub-CVAE-MRI-ASD

Code and materials for paper "Contrastive machine learning reveals the structure of neuroanatomical variation within Autism"

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pylds

some tools for gaussian linear dynamical systems

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pymrmr

Python3 binding to mRMR Feature Selection algorithm (currently not maintained)

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Self-Attention-GAN

Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)

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seq2seq

Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch

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smoothfit

Smooth data fitting in N dimensions.

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transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

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