Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9, and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65%, which is even…

• NN
  National Natural Science Foundation of China

  Award: 61272248
• SA
  Science and Technology Commission of Shanghai Municipality

  Award: 13511500200
• NK
  National Key Research and Development Program of China

  Award: 2013CB329401