（论文加代码）基于DEAP数据集的脑电情绪识别，使用了连续卷积神经网络（CNN）模型，提取了脑电微分熵特征

# Code for ICONIP 2018 submission This repository contains the tensorflow implementation for our ICONIP-2018 paper: "[Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition](https://link.springer.com/chapter/10.1007/978-3-030-04239-4_39)" ## About the paper * Title: [Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition](https://link.springer.com/chapter/10.1007/978-3-030-04239-4_39) * Authors: [Yilong Yang](https://ynulonger.github.io/), Qingfeng Wu, YazhenFu, Xiaowei Chen * Institution: Xiamen University * Published in: 2018 International Conference on Neural Information Processing (ICONIP) ## Instructions * Before running the code, please download the DEAP dataset, unzip it and place it into the right directory. The dataset can be found [here](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html). * 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. ## Requirements + Pyhton 3 + scipy + numpy + pandas + sk-learn + tensorflow (1.4 version) + import xlrd + import xlwt If you have any questions, please contact yilongyang@stu.xmu.edu.cn