This repository contains the tensorflow implementation for our ICONIP-2018 paper: "Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition"

• Title: Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition
• Authors: Yilong Yang, Qingfeng Wu, YazhenFu, Xiaowei Chen
• Institution: Xiamen University
• Published in: 2018 International Conference on Neural Information Processing (ICONIP)

• Before running the code, please download the DEAP dataset, unzip it and place it into the right directory. The dataset can be found here.
• Please run the get_1D_data.py to compute the Differential Entropy for each original .mat file. DE features of each .mat file will be stored in 1D_dataset folder.
• 1D_to_3D.py is used to transform the 1-dimentional data into 3-dimentional format, which will be used to train the proposed model.
• Using cnn.py to train and test the model (10-fold cross-validation), result of each fold will be saved in a .xls file (you can find these .xls files in ./result folder).
• count_accuracy.py is used to summarize the final accuracy of the model. The generated .xls files can be found in ./result/summary folder.

• Pyhton 3
• scipy
• numpy
• pandas
• sk-learn
• tensorflow (1.4 version)
• import xlrd
• import xlwt

If you have any questions, please contact [email protected]