Pinned Repositories
A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG
In this project, we propose a CNN model to classify single-channel EEG for driver drowsiness detection. We use the Class Activation Map (CAM) method for visualization. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
AttnSleep
[IEEE TNSRE] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
awesome-AI-for-time-series-papers
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
BCICIV2a-FBCSP
Implementation of filter bank common spatial pattern (FBCSP) for MI-based BCI in python
CfC
Closed-form Continuous-time Neural Networks
DS-DDPM
Implementation of Domain Specific Denoising Diffusion Probabilistic Models for Brain Dynamics/EEG Signals
EEG-based-Cross-Subject-Driver-Drowsiness-Recognition-with-an-Interpretable-CNN
Existing work in the field of BCI treats deep learning models as black-box classifiers. In this project, we develop a novel model named "InterpretableCNN" that allows sample wise analysis of important features for classification. The model not only achieves SOTA classification accuracy of EEG signals but also reveals meaningful features from EEG.
eeg-dqn
The repo contains the code for the paper "EEG-based Drowsiness Estimation for Safety Driving using Deep Q-Learning" submitted to TETCI and currently under revision
Survey
This is a repository contains materials for future survey submission
xinliangzhou's Repositories
xinliangzhou/Survey
This is a repository contains materials for future survey submission
xinliangzhou/awesome-AI-for-time-series-papers
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
xinliangzhou/A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG
In this project, we propose a CNN model to classify single-channel EEG for driver drowsiness detection. We use the Class Activation Map (CAM) method for visualization. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.
xinliangzhou/academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
xinliangzhou/AttnSleep
[IEEE TNSRE] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
xinliangzhou/BCICIV2a-FBCSP
Implementation of filter bank common spatial pattern (FBCSP) for MI-based BCI in python
xinliangzhou/CfC
Closed-form Continuous-time Neural Networks
xinliangzhou/DS-DDPM
Implementation of Domain Specific Denoising Diffusion Probabilistic Models for Brain Dynamics/EEG Signals
xinliangzhou/EEG-based-Cross-Subject-Driver-Drowsiness-Recognition-with-an-Interpretable-CNN
Existing work in the field of BCI treats deep learning models as black-box classifiers. In this project, we develop a novel model named "InterpretableCNN" that allows sample wise analysis of important features for classification. The model not only achieves SOTA classification accuracy of EEG signals but also reveals meaningful features from EEG.
xinliangzhou/eeg-dqn
The repo contains the code for the paper "EEG-based Drowsiness Estimation for Safety Driving using Deep Q-Learning" submitted to TETCI and currently under revision
xinliangzhou/FBCNet
FBCNet: An Efficient Multi-view Convolutional Neural Network for Brain-Computer Interface
xinliangzhou/FEDformer
xinliangzhou/image
xinliangzhou/LIFT-CAM
Code for the ICCV 2021 paper: "Towards Better Explanations of Class Activation Mapping"
xinliangzhou/mind-vis
Code base for MinD-Vis
xinliangzhou/Python
最良心的 Python 教程：
xinliangzhou/Raindrop
Graph Neural Networks for Irregular Time Series
xinliangzhou/sleep-stage-classification-papers
This repo contains a list of papers for sleep stage classification using deep learning based techniques starting from 2017-afterwards. I classified them generally according to the proposed technique.
xinliangzhou/TS-TCC
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
xinliangzhou/tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
xinliangzhou/xinliangzhou.github.io
xinliangzhou/yasa
YASA (Yet Another Spindle Algorithm): a Python package to analyze polysomnographic sleep recordings.