Emotion Recognition based on EEG using LSTM Recurrent Neural Network

Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user’s emotion who interacts with it in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. Recently, emotion recognition based on EEG signals have attracted many researchers and many methods were reported. Different types of features were extracted from EEG signals then different types of classifiers were applied to these features. In this paper, a deep learning method is proposed to recognize emotion from raw EEG signals. Long-Short Term Memory (LSTM) is used to learn features from EEG signals then the dense layer classifies these features into low/high arousal, valence, and liking. DEAP dataset is used to verify this method which gives an average accuracy of 85.65%, 85.45%, and 87.99% with arousal, valence, and liking classes, respectively. The proposed method introduced high average accuracy in comparison with the traditional techniques.

@article{Alhagry2017,
  title     = {Emotion Recognition based on EEG using LSTM Recurrent Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {10},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Salma Alhagry and Aly Aly Fahmy and Reda A. El-Khoribi},
  doi       = {10.14569/IJACSA.2017.081046},
  url       = {https://doi.org/10.14569/IJACSA.2017.081046}
}