Louu2001
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EEGraph: Convert EEGs to graphs with frequency and time-frequency domain connectivity measures.
Example code for calculating and analyzing co-fluctuation (edge) time series.
A minimal, responsive, and feature-rich Jekyll theme for technical writing.
mini-spring是简化版的spring框架，能帮助你快速熟悉spring源码和掌握spring的核心原理。抽取了spring的核心逻辑，代码极度简化，保留spring的核心功能，如IoC和AOP、资源加载器、事件监听器、类型转换、容器扩展点、bean生命周期和作用域、应用上下文等核心功能。
[TAFFC-2022] PyTorch implementation of TSception v2
This is the PyTorch implementation of the FBSTCNet-M architecture for EEG-based emotion classification.
A Library for Advanced Deep Time Series Models.
Pytorch🍊🍉 is delicious, just eat it! 😋😋
Code Implementation of paper: Transformers for EEG-Based Emotion Recognition: A Hierarchical Spatial Information Learning Model
My master thesis about emotion recognition from EEG data with a time series transformer
The repository contains trials that apply model transformer to the emotion recognition (classification) task based on electroencephalography(EEG).
pytorch implementation of EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
residual deep cnn and lstm for classifying SEED data
Multivariate LSTM Fully Convolutional Networks for Time Series Classification
Emotion recognition can be achieved by obtaining signals from the brain by EEG . This test records the activity of the brain in form of waves. We have used DEAP dataset on which we are classifying …
[IEEE J-BHI-2024] The PyTorch implementation of MASA-TCN
A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
Repository of Transformer based PyTorch Time Series Models
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
TeachYourselfCS 的中文翻译 | A Chinese translation of TeachYourselfCS
We propose a time-aware sampling network (TAS-Net) using deep reinforcement learning (DRL) for unsupervised emotion recognition, which is able to detect key emotion moments and disregard irrelevant…