Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography

  • Chuanqi Tan
  • Fuchun Sun
  • Wenchang Zhang
  • Jianhua Chen
  • Chunfang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Electroencephalography (EEG) has become the most significant input signal for brain computer interface (BCI) based systems. However, it is very difficult to obtain satisfactory classification accuracy due to traditional methods can not fully exploit multimodal information. Herein, we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem, which is designed to preserve the multimodal information of EEG. In addition, optical flow is introduced to represent the variant information of EEG. We train a deep neural network (DNN) with convolutional neural network (CNN) and recurrent neural network (RNN) for the EEG classification task by using EEG video and optical flow. The experiments demonstrate that our approach has many advantages, such as more robustness and more accuracy in EEG classification tasks. According to our approach, we designed a mixed BCI-based rehabilitation support system to help stroke patients perform some basic operations.

Keywords

Multimodal EEG classification Optical flow Deep learning CNN RNN 

Notes

Acknowledgments

This work was supported by the National Natural Fund: 91420302 and 91520201. Thanks to the contributors of the open source software used in our system.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Chuanqi Tan
    • 1
  • Fuchun Sun
    • 1
  • Wenchang Zhang
    • 1
  • Jianhua Chen
    • 1
  • Chunfang Liu
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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