Advertisement

Intent Recognition in Smart Living Through Deep Recurrent Neural Networks

  • Xiang Zhang
  • Lina Yao
  • Chaoran Huang
  • Quan Z. Sheng
  • Xianzhi Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).

Keywords

Intent recognition Deep learning EEG Smart home 

References

  1. 1.
    Muhammad, G., Alhamid, M.F., Hossain, M.S., et al.: Enhanced living by assessing voice pathology using a co-occurrence matrix. Sensors 17(2), 267 (2017)CrossRefGoogle Scholar
  2. 2.
    Kumar, S.: Ubiquitous smart home system using android application. arXiv preprint arXiv:1402.2114 (2014)
  3. 3.
    Alomari, M.H., Abubaker, A., Turani, A., Baniyounes, A.M., Manasreh, A.: EEG mouse: a machine learning-based brain computer interface. Int. J. Adv. Res. Comput. Sci. Appl. 5, 1–6 (2014)Google Scholar
  4. 4.
    Major, T.C., Conrad, J.M.: The effects of pre-filtering and individualizing components for electroencephalography neural network classification. In: SoutheastCon, 2017. IEEE (2017)Google Scholar
  5. 5.
    Sun, L., et al.: Classification of imagery motor EEG data with wavelet denoising and features selection. In: 2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) (2016)Google Scholar
  6. 6.
    Shenoy, H.V., Vinod, A., Guan, C.: Shrinkage estimator based regularization for EEG motor imagery classification. In: 2015 10th International Conference on Information, Communications and Signal Processing (ICICS). IEEE (2015)Google Scholar
  7. 7.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  8. 8.
    Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Interspeech (2014)Google Scholar
  9. 9.
    Page, A., Sagedy, C., Smith, E., Attaran, N., Oates, T., Mohsenin, T.: A flexible multichannel EEG feature extractor and classifier for seizure detection. IEEE Trans. Circ. Syst. II Express Briefs 62, 109–113 (2015)Google Scholar
  10. 10.
    Albert, B., Zhang, J., Noyvirt, A., Setchi, R., Sjaaheim, H., Velikova, S., Strisland, F.: Automatic EEG processing for the early diagnosis of traumatic brain injury. In: World Automation Congress (WAC) (2016)Google Scholar
  11. 11.
    Al-Kaysi, A.M., Al-Ani, A., Loo, C.K., et al.: Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification. J. Affect. Disord. 208, 597–603 (2017)CrossRefGoogle Scholar
  12. 12.
    An, X., Kuang, D., Guo, X., Zhao, Y., He, L.: A deep learning method for classification of EEG data based on motor imagery. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 203–210. Springer, Cham (2014). doi: 10.1007/978-3-319-09330-7_25 Google Scholar
  13. 13.
    Ward, C., Picone, J., Obeid, I.: Applications of UBMS and I-vectors in EEG subject verification. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) (2016)Google Scholar
  14. 14.
    Tabar, Y.R., Halici, U.: A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. 14, 016003 (2016)CrossRefGoogle Scholar
  15. 15.
    Mu, Z., Yin, J., Hu, J.: Design of smart home system using EEG signal. Metall. Min. Ind. 2015(6), 436–441 (2015)Google Scholar
  16. 16.
    Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
  17. 17.
    Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  18. 18.
    Taguchi, G.: System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs. UNIPUB/Kraus International Publications, White Plains (1987)Google Scholar
  19. 19.
    Tolić, M., Jović, F.: Classification of wavelet transformed EEG signals with neural network for imagined mental and motor tasks. Kineziologija 45(1), 130–138 (2013)Google Scholar
  20. 20.
    Pinheiro, O.R., Alves, L.R., Romero, M., de Souza, J.R.: Wheelchair simulator game for training people with severe disabilities. In: International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW). IEEE (2016)Google Scholar
  21. 21.
    Yao, L., Nie, F., Sheng, Q.Z., et al.: Learning from less for better: semi-supervised activity recognition via shared structure discovery. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xiang Zhang
    • 1
  • Lina Yao
    • 1
  • Chaoran Huang
    • 1
  • Quan Z. Sheng
    • 2
  • Xianzhi Wang
    • 3
  1. 1.University of New South WalesSydneyAustralia
  2. 2.Macquarie UniversitySydneyAustralia
  3. 3.Singapore Management UniversitySingaporeSingapore

Personalised recommendations