Skip to main content
SpringerLink
Log in

Design of Multiple-Input Single-Output System for EEG Signals

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Existing electroencephalogram (EEG) collection devices primarily include EEG collection systems for medical scientific research and BrainLink or earphones for commercial advertising. The EEG collection systems employ multiple-input multiple-output connections, making the circuit and wiring complex. However, the BrainLink or earphones are usually designed for entertainment mainly collecting one or two EEG locations. To address these problems, in this paper, a multiple-input single-output collection device (MISOCD) is developed that can obtain a single-channel mixed signal, and a multiple-input single-output blind separation (MISOBS) algorithm is proposed to separate multiple-channel signals from a single-channel mixed signal by introducing an improved parallel dual generative adversarial network that can build one-to-multiple mapping. An experiment is conducted on synthetic signals to illustrate the separation performance of the proposed MISOBS algorithm, which significantly outperforms the state-of-the-art algorithms. The real signals collected by the MISOCD are used for gesture recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data Availability

The data will be made available from the author upon reasonable request.

References

  1. M. Arjovsky, S. Chintala, L. Bottou, Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)

  2. A. Arsalan, M. Majid, A.R. Butt et al., Classification of perceived mental stress using a commercially available EEG headband. IEEE J. Biomed. Health Inform. 23(6), 2257–2264 (2019)

    Article  Google Scholar 

  3. F. Abdollahi, A. Motie-Nasrabadi, Combination of frequency bands in EEG for feature reduction in mental task classification, in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 1146–1149 (2006)

  4. J. Benesty, J. Chen, Y. Huang, On the importance of the Pearson correlation coefficient in noise reduction. IEEE Trans. Audio Speech Lang. Process. 16(4), 757–765 (2008)

    Article  Google Scholar 

  5. R. Butler, P.M. Bernier, G.W. Mierzwinski et al., Cortical distance, not cancellation, dominates inter-subject EEG gamma rhythm amplitude. Neuroimage 192, 156–165 (2019)

    Article  Google Scholar 

  6. P. Chen, U. Kirk, S. Dikker, Trait mindfulness predicts inter-brain coupling during naturalistic face-to-face interactions. bioRxiv:2021.06.28.448432 (2021)

  7. O.H. Cho, S.D. Hong, Real-time 3D fluid simulation digital art using BCI. J. Real-Time Image Process. 13(3), 419–429 (2017)

    Article  Google Scholar 

  8. Z.C. Fan, Y.L. Lai, J.S.R. Jang, SVSGAN: singing voice separation via generative adversarial network, in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 726–730 (2018)

  9. R.S. Fisher, C. Acevedo, A. Arzimanoglou et al., ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55(4), 475–482 (2014)

    Article  Google Scholar 

  10. S.M. Fernandez-Fraga, M.A. Aceves-Fernandez, J.C. Pedraza-Ortega, EEG data collection using visual evoked, steady state visual evoked and motor image task, designed to brain computer interfaces (BCI) development. Data Brief 25, 103871 (2019)

    Article  Google Scholar 

  11. Y. Guo, S. Huang, Y. Li, Single-mixture source separation using dimensionality reduction of ensemble empirical mode decomposition and independent component analysis. Circuits Syst. Signal Process. 31(6), 2047–2060 (2012)

    Article  MathSciNet  Google Scholar 

  12. B. Gao, W.L. Woo, S.S. Dlay, Single-channel source separation using EMD-subband variable regularized sparse features. IEEE Trans. Audio Speech Lang. Process. 19(4), 961–976 (2011)

    Article  Google Scholar 

  13. G. Gargiulo, R.A. Calvo, P. Bifulco et al., A new EEG recording system for passive dry electrodes. Clin. Neurophysiol. 121(5), 686–693 (2010)

    Article  Google Scholar 

  14. P. González-Castro, C. Rodríguez, Á. López et al., Attention deficit hyperactivity disorder, differential diagnosis with blood oxygenation, beta/theta ratio, and attention measures. Int. J. Clin. Health Psychol. 13(2), 101–109 (2013)

    Article  Google Scholar 

  15. D. He, Y. Xia, T. Qin et al., Dual learning for machine translation, in Proceedings of the 30st International Conference on Neural Information Processing Systems (NIPS), pp. 820–828 (2016)

  16. S. Jabbari, Source separation from single-channel abdominal phonocardiographic signals based on independent component analysis. Biomed. Eng. Lett. 11(1), 55–67 (2021)

    Article  Google Scholar 

  17. Q. Kong, Y. Xu, W. Wang et al., Single-channel signal separation and deconvolution with generative adversarial networks, in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2747–2753 (2019)

  18. N. Kannathal, L.C. Min, U.R. Acharya et al., Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80(3), 187–194 (2005)

    Article  Google Scholar 

  19. Q. Luo, G.H. Glover, Influence of dense-array EEG cap on fMRI signal. Magn. Reson. Med. 68(3), 807–815 (2012)

    Article  Google Scholar 

  20. T. Liu, W. Wang, X. Zhang et al., One to multiple mapping dual learning: Learning multiple sources from one mixed signal. arXiv preprint arXiv:2110.06568 (2021)

  21. M. Y. Liu, T. Breuel, J. Kautz, Unsupervised image-to-image translation networks, in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS), pp. 700–708 (2017)

  22. B. Mijović, M. De Vos, I. Gligorijević et al., Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Trans. Biomed. Eng. 57(9), 2188–2196 (2010)

    Article  Google Scholar 

  23. A.S. Oliveira, B.R. Schlink, H.W. David et al., Proposing metrics for benchmarking novel EEG technologies towards real-world measurements. Front. Hum. Neurosci. 10, 188 (2016)

    Article  Google Scholar 

  24. D.V. Poltavski, The use of single-electrode wireless EEG in biobehavioral investigations, in Mobile Health Technologies. Methods in Molecular Biology, vol. 1256, ed. by A. Rasooly, K. Herold (Springer, Berlin, 2015), pp.375–390

    Chapter  Google Scholar 

  25. F. Rohit, V. Kulathumani, R. Kavi et al., Real-time drowsiness detection using wearable, lightweight brain sensing headbands. IET Intell. Transp. Syst. 11(5), 255–263 (1994)

    Article  Google Scholar 

  26. O. Ronneberger, P. Fischer, T. Brox, U-net: convolutional networks for biomedical image segmentation, in 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention (MACCAI), pp. 234–241 (2015)

  27. G.N. Rajesh, E.P. Kumar, A novalized VLSI design and implementation of EEG signal acquisition system, in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1955–1959 (2016)

  28. Y. C. Subakan, P. Smaragdis, Generative adversarial source separation, in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 26–30 (2018)

  29. Y. Sun, W. Wang, J. Chambers et al., Two-stage monaural source separation in reverberant room environments using deep neural networks. IEEE/ACM Trans. Audio Speech Lang. Process. 27(1), 125–139 (2019)

    Article  Google Scholar 

  30. M. Story, R.G. Congalton, Accuracy assessment: a users perspective. Photogramm. Eng. Remote Sens. 52(3), 397–399 (1986)

    Google Scholar 

  31. Z. Yi, H. Zhang, P. Tan et al., DualGAN: unsupervised dual learning for image-to-image translation, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2868–2876 (2017)

  32. W. Yuan, B. Dong, S. Wang et al., Evolving multi-resolution pooling CNN for monaural singing voice separation. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 807–822 (2021)

    Article  Google Scholar 

  33. L. Zou, X. Chen, G. Dang et al., Removing muscle artifacts from EEG data via underdetermined joint blind source separation: a simulation study. IEEE Trans. Circuits Syst. II Express Briefs 67(1), 187–191 (2019)

    Google Scholar 

  34. L.S. Zhan, D.S. Huang, C.H. Zheng, Blind inversion of wiener system for single source using nonlinear blind source separation, in Proceedings of the International Joint Conference on Neural Networks (IJCNN) vol. 2, pp. 1235–1238 (2005)

Download references

Acknowledgements

This study was supported by National Natural Science Foundation of China under Grant 62271341, China Scholarship Council under Grant [2020]1417, Natural Science Foundation for Young Scientists of Shanxi Province under Grant 201901D211313, Key Research and Development Project of Shanxi Province under Grant 201803D421035, Shanxi Scholarship Council of China under Grant HGKY2019080, Shanxi Province Postgraduate Excellent Innovation Project Plan under Grant 2021Y679. The authors would like to thank the anonymous reviewers for their constructive comments on improving this article and NES (http://www.nesediting.com) for English language editing.

Author information

Authors and Affiliations

  1. Department of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Ting Liu, Zhenying Gong, Xiaofei Zhang & Yina Guo

Authors
  1. Ting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenying Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yina Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yina Guo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, T., Gong, Z., Zhang, X. et al. Design of Multiple-Input Single-Output System for EEG Signals. Circuits Syst Signal Process 42, 2215–2234 (2023). https://doi.org/10.1007/s00034-022-02202-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-022-02202-4

Keywords

Search

Navigation