PyTorch implementation of TSception V2 using DEAP dataset

Overview

TSception

This is the PyTorch implementation of TSception V2 using DEAP dataset in our paper:

Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai Guan, "TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition", under review of IEEE Transactions on Affective Computing, preprint available at arXiv

It is an end-to-end multi-scale convolutional neural network to do classification from raw EEG signals. Previous version of TSception(IJCNN'20) can be found at website

Prepare the python virtual environment

Please create an anaconda virtual environment by:

$ conda create --name TSception

Activate the virtual environment by:

$ conda activate TSception

Install the requirements by:

$ pip3 install -r requirements.txt

Run the code

Please download the DEAP dataset at website. Please place the "data_preprocessed_python" folder at the same location of the script (./code/). To run the code for arousal dimension, please type the following command in terminal:

$ python3 main-DEAP.py --data-path './data_preprocessed_python/' --label-type 'A'

To run the experiments for valance please set the --label-type 'V'. The results will be saved into "result.txt" located at the same place as the script.

Reproduce the results

We highly suggest to run the code on a Ubuntu 18.04 or above machine using anaconda with the provided requirements to reproduce the results. You can also download the saved model at website to reproduce the results in the paper. After extracting the downloaded "save.zip", please place it at the same location of the scripts, run the code by:

$ python3 main-DEAP.py --data-path './data_preprocessed_python/' --label-type 'A' --reproduce True

Acknowledgment

The author would like to thank Su Zhang, Quihao Zeng and Tushar Chouhan for checking the code

Cite

Please cite our paper if you use our code in your own work:

@misc{ding2021tsception,
      title={TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition}, 
      author={Yi Ding and Neethu Robinson and Su Zhang and Qiuhao Zeng and Cuntai Guan},
      year={2021},
      eprint={2104.02935},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

OR

@INPROCEEDINGS{9206750,
  author={Y. {Ding} and N. {Robinson} and Q. {Zeng} and D. {Chen} and A. A. {Phyo Wai} and T. -S. {Lee} and C. {Guan}},
  booktitle={2020 International Joint Conference on Neural Networks (IJCNN)}, 
  title={TSception:A Deep Learning Framework for Emotion Detection Using EEG}, 
  year={2020},
  volume={},
  number={},
  pages={1-7},
  doi={10.1109/IJCNN48605.2020.9206750}}
Owner
Yi Ding
Ph.D. candidate in Computer Science and Engineering. Research interests: deep/machine learning, brain-computer interface, artificial intelligence
Yi Ding
Implementation of Deep Deterministic Policy Gradiet Algorithm in Tensorflow

ddpg-aigym Deep Deterministic Policy Gradient Implementation of Deep Deterministic Policy Gradiet Algorithm (Lillicrap et al.arXiv:1509.02971.) in Ten

Steven Spielberg P 247 Dec 07, 2022
Target Propagation via Regularized Inversion

Target Propagation via Regularized Inversion The present code implements an ideal formulation of target propagation using regularized inverses compute

Vincent Roulet 0 Dec 02, 2021
Code for Robust Contrastive Learning against Noisy Views

Robust Contrastive Learning against Noisy Views This repository provides a PyTorch implementation of the Robust InfoNCE loss proposed in paper Robust

Ching-Yao Chuang 53 Jan 08, 2023
StorSeismic: An approach to pre-train a neural network to store seismic data features

StorSeismic: An approach to pre-train a neural network to store seismic data features This repository contains codes and resources to reproduce experi

Seismic Wave Analysis Group 11 Dec 05, 2022
Cooperative Driving Dataset: a dataset for multi-agent driving scenarios

Cooperative Driving Dataset (CODD) The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple

Eduardo Henrique Arnold 124 Dec 28, 2022
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

Junction Tree Variational Autoencoder for Molecular Graph Generation Official implementation of our Junction Tree Variational Autoencoder https://arxi

Wengong Jin 418 Jan 07, 2023
The implementation of "Bootstrapping Semantic Segmentation with Regional Contrast".

ReCo - Regional Contrast This repository contains the source code of ReCo and baselines from the paper, Bootstrapping Semantic Segmentation with Regio

Shikun Liu 128 Dec 30, 2022
Unofficial PyTorch code for BasicVSR

Dependencies and Installation The code is based on BasicSR, Please install the BasicSR framework first. Pytorch=1.51 Training cd ./code CUDA_VISIBLE_

Long 59 Dec 06, 2022
BackgroundRemover lets you Remove Background from images and video with a simple command line interface

BackgroundRemover BackgroundRemover is a command line tool to remove background from video and image, made by nadermx to power https://BackgroundRemov

Johnathan Nader 1.7k Dec 30, 2022
A simplified framework and utilities for PyTorch

Here is Poutyne. Poutyne is a simplified framework for PyTorch and handles much of the boilerplating code needed to train neural networks. Use Poutyne

GRAAL/GRAIL 534 Dec 17, 2022
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning

H-Transformer-1D Implementation of H-Transformer-1D, Transformer using hierarchical Attention for sequence learning with subquadratic costs. For now,

Phil Wang 123 Nov 17, 2022
Official code of paper "PGT: A Progressive Method for Training Models on Long Videos" on CVPR2021

PGT Code for paper PGT: A Progressive Method for Training Models on Long Videos. Install Run pip install -r requirements.txt. Run python setup.py buil

Bo Pang 27 Mar 30, 2022
[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects YouTube | arXiv Prerequisites Kaolin is available here:

Denys Rozumnyi 107 Dec 26, 2022
Code from Daniel Lemire, A Better Alternative to Piecewise Linear Time Series Segmentation

PiecewiseLinearTimeSeriesApproximation code from Daniel Lemire, A Better Alternative to Piecewise Linear Time Series Segmentation, SIAM Data Mining 20

Daniel Lemire 21 Oct 27, 2022
An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

Achraf Rahouti 3 Nov 30, 2021
Harmonic Memory Networks for Graph Completion

HMemNetworks Code and documentation for Harmonic Memory Networks, a series of models for compositionally assembling representations of graph elements

mlalisse 0 Oct 27, 2021
SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers This repo contains our codes for the paper "No Parameters Left Behind: Sensitivity Gu

Chen Liang 23 Nov 07, 2022
Deep Q Learning with OpenAI Gym and Pokemon Showdown

pokemon-deep-learning An openAI gym project for pokemon involving deep q learning. Made by myself, Sam Little, and Layton Webber. This code captures g

2 Dec 22, 2021
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

RLMeta rlmeta - a flexible lightweight research framework for Distributed Reinforcement Learning based on PyTorch and moolib Installation To build fro

Meta Research 281 Dec 22, 2022
This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios"

TinyWeaklyIsolationForest This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised a

2 Mar 21, 2022