Skip to main content
SpringerLink
Log in

Using a novel modular continuous restricted Boltzmann machine to SSVEP-based BCIs for amyotrophic lateral sclerosis

  • Technical Paper
  • Published:
Microsystem Technologies Aims and scope Submit manuscript

Abstract

The communication of a patient with amyotrophic lateral sclerosis is limited and then the quality of lives would be greatly reduced. The patients still maintain the cognitive ability, thus developing an assistive communication interface would greatly help them in daily live. Recently, steady state visually evoked potential (SSVEP) based brain computer interfaces (BCIs) had been successfully developed to help patients. Increasing the accuracy of SSVEP-based BCIs is able to realize the assistive communication interfaces in practical applications. In this study, a modular continuous restricted Boltzmann machine (MCRBM) is proposed to improve the performance of SSVEP-based BCIs. To precisely represent the characteristics of elicited signals of SSVEP, the frequency magnitude, the coefficients of canonical correlation analysis, and the correlations of magnitude square coherence are selected as the features. In the first layer of MCRBM, the continuous restricted Boltzmann machine based neural networks are used as the basic units and applied to accurately estimate by using different types of features. In the second layer of MCRBM, a CRBM is then designed to fuse the decisions and find the final results. The experimental results showed that MCRBM produce higher accuracy compared to CRBM. Therefore, the proposed approach can be adopted in practical applications and then help patients in communicating with others.

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

Similar content being viewed by others

References

  • Anila M, Radhika P (2017) Lip contour detection based AAC device using Morse code. In: 2017 international conference on wireless communications, signal processing and networking (WiSPNET), 2017, pp 1182–1187

  • Chang SJ, Duan BG, Hsiao CH, Liu CW, Young SJ (2014) UV enhanced emission performance of low temperature grown Ga-doped ZnO nanorods. IEEE Photonics Technol Lett 26(1):66–69

    Article  Google Scholar 

  • Chen YJ, Wu JL (2016) A computer-aided articulation learning system for subjects with articulation disorders. Eng Computat 33(7):2185–2197

    Article  Google Scholar 

  • Chen YJ, Chen SC, Zaeni IAE, Wu CM (2016) Fuzzy tracking and control algorithm for an SSVEP-based BCI system. Appl Sci 6(10):270. https://doi.org/10.3390/app6100270

    Article  Google Scholar 

  • Chen SC, Chen YJ, Zaeni IAE, Wu CM (2017) A single channel SSVEP based BCI with a fuzzy feature threshold algorithm in a maze game. Int J Fuzzy Syst 19(2):553–565

    Article  Google Scholar 

  • Chen SC, Wu CM, Zaeni IAE, Chen YJ (2018) Applying fuzzy decision for a single channel SSVEP-based BCI on automatic feeding robot. Microsyst Technol 24(1):199–207

    Article  Google Scholar 

  • Coninck ED, Bohez S, Leroux S, Verbelen T, Vankeirsbilck B, Simoens P, Dhoedt B (2018) DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure. J Syst Softw 141:52–65

    Article  Google Scholar 

  • Feng J, Yin E, Jin J, Saab R, Wang Daly L, Hu X, Cichocki DA (2018) Towards correlation-based time window selection method for motor imagery BCIs. Neural Netw 102:87–95

    Article  Google Scholar 

  • Guy V, Soriani MH, Bruno M, Papadopoulo T, Desnuelle C, Clerc M (2018) Brain computer interface with the P300 speller: usability for disabled people with amyotrophic lateral sclerosis. Ann Phys Rehabil Med 61(1):5–11

    Article  Google Scholar 

  • Hornero G, Conde D, Quílez M, Domingo S, Rodríguez MP, Romero B, Casas O (2015) A wireless augmentative and alternative communication system for people with speech disabilities. IEEE Access 3:1288–1297

    Article  Google Scholar 

  • Illa A, Patel D, Yamini BK, Meera SS, Shivashankar N, Veeramani PK, Vengalii S, Polavarapui K, Nashi S, Nalini A, Ghosh PK (2018) Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects. In: 2018 IEEE international conference on acoustics, speech and signal processing, 2018, pp 6014–6018

  • Janbakhshi P, Kodrasi I, Bourlard H (2019) Pathological speech intelligibility assessment based on the short-time objective intelligibility measure. In: 2019 IEEE international conference on acoustics, speech and signal processing, 2019, pp. 6405–6409

  • Jiao Z, Gao X, Wang Y, Li J, Xu H (2018) Deep convolutional neural networks for mental load classification based on EEG data. Pattern Recogn 76:582–595

    Article  Google Scholar 

  • Lee CC, Chuang CC, Yeng CH, Chen YJ, Lin BS (2017) Noise suppression by minima controlled recursive averaging for SSVEP-based BCIs with single channel. IEEE Signal Process Lett 24(12):1783–1787

    Article  Google Scholar 

  • Li LL, Lin YH, Yang HM, Chen YJ, Wu JL (2018) Tone production and perception and intelligibility of produced speech in Mandarin-speaking cochlear implanted children. Int J Audiol 57(2):135–142

    Article  Google Scholar 

  • Lin ZD, Young SJ, Chang SJ (2015) CO2 gas sensors based on carbon nanotube thin films using a simple transfer method on flexible substrate. IEEE Sens J 15(12):7017–7020

    Article  Google Scholar 

  • Maye A, Zhang D, Engel AK (2017) Utilizing retinotopic mapping for a multi-target SSVEP BCI with a single flicker frequency. IEEE Trans Neural Syst Rehabil Eng 25(7):1026–1036

    Article  Google Scholar 

  • Öztürk S, Devecioğlu I, Beygi M, Atasoy A, Mutlu S, Özkan M, Güçlü B (2019) Real-time performance of a tactile neuroprosthesis on awake behaving rats. IEEE Trans Neural Syst Rehabil Eng 27(5):1053–1062

    Article  Google Scholar 

  • Radici E, Bonacina S, Leo GD (2016) Design and development of an AAC app based on a speech-to-symbol technology. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2016, pp 2574–2577

  • Rojas R (2013) Neural networks: a systematic introduction. Springer Science and Business Media, Berlin

    MATH  Google Scholar 

  • Tantisatirapong S, Dechwechprasit P, Senavongse W, Phothisonothai M (2017) Time-frequency based coherence analysis of red and green flickering visual stimuli for EEG-controlled applications. In: 2017 9th international conference on knowledge and smart technology (KST), 2017, pp 279–283

  • Usanova LD, Usanova AD, Skripal AV (2012) Analysis of effect of audiovisual stimulation on parameters of encephalogram and rate of pulsation wave propagation in humans. Biomed Eng 46(1):25–28

    Article  Google Scholar 

  • Wang JH, Tang CT, Chen H (2017) An adaptable continuous restricted boltzmann machine in VLSI for fusing the sensory data of an electronic nose. IEEE Trans Neural Netw Learn Syst 28(4):961–974

    Article  Google Scholar 

  • Watanabe C, Hiramatsu K, Kashino K (2018) Modular representation of layered neural networks. Neural Networks 97:62–73

    Article  Google Scholar 

  • Yarushev SA, Averkin AN (2018) Time series analysis based on modular architectures of neural networks. Proc Comput Sci 123:562–567

    Article  Google Scholar 

  • Yin E, Zhou Z, Jiang J, Yu Y, Hu D (2015) A dynamically optimized SSVEP brain-computer interface (BCI) speller. IEEE Trans Biomed Eng 62(6):1447–1456

    Article  Google Scholar 

  • Young SJ, Lin ZD (2018) Ammonia gas sensors with Au-decorated carbon nanotubes. Microsyst Technol 24(10):4207–4210

    Article  Google Scholar 

  • Young SJ, Tang WL (2019) Wireless zinc oxide Based pH sensor system. J Electrochem Soc 166(9):B3047–B3050

    Article  Google Scholar 

  • Young SJ, Liiu YH, Hsiao CH, Chang SJ, Wang BC, Kao TH, Tsai KS, Wu SL (2014) ZnO-based ultraviolet photodetectors with novel nanosheet structures. IEEE Trans Nanotechnol 13(2):238–244

    Article  Google Scholar 

  • Zeng Z, Li X (2019) Application of human computing in image captioning under deep learning. Microsyst Technol. https://doi.org/10.1007/s00542-019-04473-5

    Article  Google Scholar 

  • Zhang Y, Wang Y, Zhou G, Jin J, Wang B, Wang X, Cichocki A (2018) Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Syst Appl 96:302–310

    Article  Google Scholar 

  • Zheng Q, Zhu F, Qin J, Heng PA (2018) Multiclass support matrix machine for single trial EEG classification. Neurocomputing 275:869–880

    Article  Google Scholar 

Download references

Acknowledgements

The current authors gratefully acknowledge the support provided to this study by the Ministry of Science and Technology, Taiwan, Republic of China, under Contract MOST 107-2637-E-218-005 and Higher Education Sprout Project, Ministry of Education, Taiwan, Republic of China, under Contract 1300-107P735.

Author information

Authors and Affiliations

  1. Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Tainan, Taiwan

    Chien-Ching Lee & Bor-Shyh Lin

  2. Department of Anesthesia, An Nan Hospital, China Medical University, Tainan, Taiwan

    Chien-Ching Lee, Chia-Chun Chuang & Edmund-Cheung So

  3. Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Chia-Hong Yeng & Yeou-Jiunn Chen

  4. Department of Anesthesia, China Medical University, Taichung, Taiwan

    Edmund-Cheung So

  5. Allied AI Biomedical Research Center, Tainan, Taiwan

    Yeou-Jiunn Chen

Authors
  1. Chien-Ching Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chia-Chun Chuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chia-Hong Yeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Edmund-Cheung So
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bor-Shyh Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yeou-Jiunn Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C-CL and C-CC contributed equally to this paper. Y-JC analyzed data and wrote the main paper; B-SL collected all data and provided statistical analysis; C-HY took parts in surveillance of performing the experiment and study design; ECS dedicated his effort to study design and interpretive analysis; C-CL and C-CC designed the study and directed the experiment. All authors discussed the results and implications and commented on the manuscript at all stages.

Corresponding author

Correspondence to Yeou-Jiunn Chen.

Ethics declarations

Conflicts of interest

The authors declare 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, CC., Chuang, CC., Yeng, CH. et al. Using a novel modular continuous restricted Boltzmann machine to SSVEP-based BCIs for amyotrophic lateral sclerosis. Microsyst Technol 28, 221–227 (2022). https://doi.org/10.1007/s00542-019-04589-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00542-019-04589-8

Search

Navigation