ACRNN

Code for paper: EEG-based Emotion Recognition via Channel-wise Attention and Self Attention

About the paper

• Title: EEG-based Emotion Recognition via Channel-wise Attention and Self Attention
• Authors: Wei Tao, Chang Li, Rencheng Song, Juan Cheng, Yu Liu, Feng Wan and Xun Chen
• Institution: Hefei University of Technology
• Published in: IEEE Transactions on Affective Computing

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. Each .mat data file contains the EEG signals and consponding labels of a subject. There are 2 arrays in the file: data and labels. The shape of data is (40, 40, 8064). The shape of label is (40,4).
• Please run the deap_pre_process.py to Load the origin .mat data file and transform it into .pkl file.
• Using ACRNN.py to train and test the model (10-fold cross-validation), result of 10 folds will be saved in a .xls file.
• ACRNN.py is used to calculate the final accuracy of the model.
• The usage on DREAMER dataset is the same as above. The DREAMER dataset can be found here.

Requirements

• Pyhton3.5
• tensorflow-gpu (1.4.1 version)

If you have any questions, please contact yc07466@umac.mo

Reference

• ynulonger/ijcnn