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Soft dry electroophthalmogram electrodes for human machine interaction

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Abstract

Current soft surface electrodes have attracted more and more attentions owing to their potential applications in biological signal monitoring, human-machine interaction (HMI) and Internet of Things (IoT). The paper presents that soft dry electrode based on polydimethylsiloxane-carbon black (PDMS-CB) conductive polymer is designed and fabricated to continuous, long-term, stable electroophthalmogram (EOG) signal recordings for HMI applications. The features corresponding to the different eye motions are extracted from the EOG data via the soft dry electrodes. Linear discriminant analysis (LDA) recognition algorithms are proposed to recognize eye motion behaviors for controlling the motion of the mobile robots. Experiment results have been demonstrated that LDA recognition algorithm achieves a relatively high recognition accuracy of 90.63% for recognizing four eye movements (‘Up’, ‘Down’, ‘Right’, and ‘Left’). The control commands are generated with different eye motions and transmitted to the mobile robot through WiFi communication unit, which the mobile robot is successfully controlled. The soft dry electrodes have the potential in a comfortable, simple, wearable and wireless control of rehabilitation devices.

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References

  • H. Almahasneh, W.-T. Chooi, N. Kamel, A.S. Malik, Deep in thought while driving: An EEG study on drivers’ cognitive distraction. Transport. Res. F: Traffic Psychol. Behav. 26, 218–226 (2014)

    Article  Google Scholar 

  • A. Al-Rahayfeh, M. Faezipour, Eye tracking and head movement detection: A state-of-art survey. IEEE Journal of Translational Engineering in Health and Medicine 1, 2100212–2100212 (2013)

    Article  Google Scholar 

  • S.K. Ameri, R. Ho, H. Jang, L. Tao, N. Lu, Graphene electronic tattoo sensors. ACS Nano 11, 7634–7641 (2017)

    Article  Google Scholar 

  • S. K. Ameri, M. Kim, I. A. Kuang, W. K. Perera, M. Alshiekh, H. Jeong, et al., "Imperceptible electrooculography graphene sensor system for human–robot interface," npj 2D Materials and Applications, vol. 2, pp. 19-, 2018

  • H.J. Baek, J.L. Hong, G.L. Yong, K.S. Park, Conductive polymer foam surface improves the performance of a capacitive EEG electrode. IEEE Trans. Biomed. Eng. 59, 3422–3431 (2012)

    Article  Google Scholar 

  • W. Dong, X. Cheng, T. Xiong, and X. Wang, "Stretchable bio-potential electrode with self-similar serpentine structure for continuous, long-term, stable ECG recordings," Biomedical Microdevices, vol. 21, p. 6, 2019/01/03 2019

  • P. Ebrahim, W. Stolzmann, and B. Yang, "Eye Movement Detection for Assessing Driver Drowsiness by Electrooculography," pp. 4142–4148, 2013

  • C.T. Freeman, Electrode array-based electrical stimulation using ILC with restricted input subspace. Control. Eng. Pract. 23, 32–43 (2014)

    Article  Google Scholar 

  • N. Gandhi, C. Khe, D. Chung, Y. M. Chi, and G. Cauwenberghs, "Properties of dry and non-contact electrodes for wearable physiological sensors," pp. 107–112, 2011a

  • N. Gandhi, C. Khe, D. Chung, Y. M. Chi, and G. Cauwenberghs, "Properties of dry and non-contact electrodes for wearable physiological sensors," in Body Sensor Networks (BSN), 2011 International Conference on, 2011b, pp. 107–112

  • Q. Huang, S. He, Q. Wang, Z. Gu, N. Peng, K. Li, et al., "an EOG-Based Human-Machine Interface for Wheelchair Control," IEEE Trans Biomed Eng, Jul 27 2017

    Google Scholar 

  • Y. Huang, W. Dong, C. Zhu, L. Xiao, Electromechanical Design of Self-Similar Inspired Surface Electrodes for human-machine interaction. Complexity 2018, 14 (2018)

    Google Scholar 

  • X. Q. Huo, W. L. Zheng, and B. L. Lu, "Driving Fatigue Detection with Fusion of EEG and Forehead EOG," in International Joint Conference on Neural Networks, 2016

    Google Scholar 

  • B. Jammes, H. Sharabty, and D. Esteve, "Automatic EOG analysis: A first step toward automatic drowsiness scoring during wake-sleep transitions," Somnologie - Schlafforschung und Schlafmedizin, vol. 12, pp. 227–232, September 01 2008

  • M. Jiaxin, Z. Yu, C. Andrzej, M. Fumitoshi, A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: Application to robot control. IEEE Trans. Biomed. Eng. 62, 876–889 (2015)

    Article  Google Scholar 

  • L. Jin, H. Xian, Y. Jiang, Q. Niu, M. Xu, D. Yang, Research on evaluation model for secondary task driving safety based on driver eye movements. Advances in Mechanical Engineering 6, 624561 (2015)

    Article  Google Scholar 

  • H.-C. Jung, J.-H. Moon, D.-H. Baek, J.-H. Lee, Y.-Y. Choi, J.-S. Hong, et al., CNT/PDMS composite flexible dry electrodesfor long-term ECG monitoring. IEEE Trans. Biomed. Eng. 59, 1472–1479 (2012)

    Article  Google Scholar 

  • M.S. Kaiser, Z.I. Chowdhury, S.A. Mamun, A. Hussain, M. Mahmud, A Neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. Cogn. Comput., 1–9 (2016)

  • S.J. Kim, K.W. Cho, H.R. Cho, L. Wang, S.Y. Park, S.E. Lee, et al., Stretchable electronics: Stretchable and transparent biointerface using cell-sheet–Graphene hybrid for electrophysiology and therapy of skeletal muscle (Adv. Funct. Mater. 19/2016). Adv. Funct. Mater. 26, 3182–3182 (2016)

    Article  Google Scholar 

  • W. Li, X. Hu, R. Gravina, G. Fortino, A Neuro-fuzzy fatigue-tracking and classification system for wheelchair users. IEEE Access 5, 19420–19431 (2017)

    Article  Google Scholar 

  • Y. Liang, J.D. Lee, A hybrid Bayesian network approach to detect driver cognitive distraction. Transportation Research Part C: Emerging Technologies 38, 146–155 (2014)

    Article  Google Scholar 

  • A. Lopez, P.C. Richardson, Capacitive electrocardiographic and bioelectric electrodes. IEEE Trans. Biomed. Eng., 99–99 (1969)

  • Y. Lu, C. Zhang, B.Y. Zhou, X.P. Gao, Z. Lv, A dual model approach to EOG-based human activity recognition. Biomedical Signal Processing & Control 45, 50–57 (2018)

    Article  Google Scholar 

  • Z. Lv, X.P. Wu, M. Li, D. Zhang, A novel eye movement detection algorithm for EOG driven human computer interface. Pattern Recogn. Lett. 31, 1041–1047 (2010)

    Article  Google Scholar 

  • T. Matsuda and M. Makikawa, "ECG monitoring of a car driver using capacitively-coupled electrodes," in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 1315–1318

  • S. Mishra, J. J. S. Norton, Y. Lee, D. S. Lee, N. Agee, Y. Chen, et al., "Soft, conformal bioelectronics for a wireless human-wheelchair interface," Biosens. Bioelectron., vol. 91, pp. 796–803, May 15 2017

  • B. Ning, M.-j. Li, T. Liu, H.-m. Shen, L. Hu, and X. Fu, "Human Brain Control of Electric Wheelchair with Eye-BlinkElectrooculogram Signal." Intelligent Robotics and Applications. Springer Berlin Heidelberg (2012)

  • J.J. Norton, D.S. Lee, J.W. Lee, W. Lee, O. Kwon, P. Won, et al., Soft, curved electrode systems capable of integration on the auricle as a persistent brain-computer interface. Proc. Natl. Acad. Sci. U. S. A. 112, 3920–3925 (Mar 31 2015)

    Article  Google Scholar 

  • J.L. Orquin, S. Mueller Loose, Attention and choice: A review on eye movements in decision making. Acta Psychol. 144, 190–206 (2013)

    Article  Google Scholar 

  • H.-L. Peng, J.-Q. Liu, H.-C. Tian, B. Xu, Y.-Z. Dong, B. Yang, et al., Flexible dry electrode based on carbon nanotube/polymer hybrid micropillars for biopotential recording. Sensors Actuators A Phys. 235, 48–56 (2015)

    Article  Google Scholar 

  • L. Polansky, G. Wittemyer, A framework for understanding the architecture of collective movements using pairwise analyses of animal movement data. J. R. Soc. Interface 8, 322–333 (2011)

    Article  Google Scholar 

  • B.J. Polk, A. Stelzenmuller, G. Mijares, W. MacCrehan, M. Gaitan, Ag/AgCl microelectrodes with improved stability for microfluidics. Sensors Actuators B Chem. 114, 239–247 (2006)

    Article  Google Scholar 

  • I. Rigas, E. Abdulin, O. Komogortsev, Towards a multi-source fusion approach for eye movement-driven recognition. Information Fusion 32, 13–25 (2016)

    Article  Google Scholar 

  • B. N. Soundari, M. Nandakumar, R. Nivetha, and K. Rajakumari, "Extension of Desktop Control to Robot Control by Eye Blinks Using Support Vector Machine (SVM)," in International Conference on Recent Trends in Information Technology, 2011

    Google Scholar 

  • Y. Sun, X.B. Yu, Capacitive biopotential measurement for electrophysiological signal acquisition: A review. IEEE Sensors J. 16, 2832–2853 (2016)

    Article  Google Scholar 

  • E. Tivesten, M. Dozza, Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving. Transport. Res. F: Traffic Psychol. Behav. 26, 258–272 (2014)

    Article  Google Scholar 

  • L. Wang and N. Lu, "Conformability of a Thin Elastic Membrane Laminated on a Soft Substrate With Slightly Wavy Surface," Journal of Applied Mechanics, vol. 83, 2016

  • L.-F. Wang, J.-Q. Liu, B. Yang, C.-S. Yang, PDMS-based low cost flexible dry electrode for long-term EEG measurement. IEEE Sensors J. 12, 2898–2904 (2012)

    Article  Google Scholar 

  • M.A. Yokus, J.S. Jur, Fabric-based wearable dry electrodes for body surface biopotential recording. IEEE Trans. Biomed. Eng. 63, 423 (2016)

    Article  Google Scholar 

  • Q. Zhong, J. Zhong, X. Cheng, X. Yao, B. Wang, W. Li, et al., Paper-based active tactile sensor Array. Adv. Mater. 27, 7130–7136 (2015)

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge supports from the National Natural Science Foundation of China (51705376), Natural Science Foundation of Jiangxi province (20192BAB216025), Youth Project of Jiangxi Education Department (GJJ180360). The authors would like to thank Key Laboratory of Advanced Control & Optimization of Jiangxi Province for providing the measurement instruments for electrical performance test of the soft PDMS-CB dry electrodes.

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Authors and Affiliations

  1. Rail Transportation Technology Innovation Center, East China Jiaotong University, Nanchang, 330013, China

    Xiao Cheng & Wentao Dong

  2. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

    Chongzhi Bao

  3. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China

    Wentao Dong

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  1. Xiao Cheng
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  2. Chongzhi Bao
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  3. Wentao Dong
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Correspondence to Wentao Dong.

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Cheng, X., Bao, C. & Dong, W. Soft dry electroophthalmogram electrodes for human machine interaction. Biomed Microdevices 21, 103 (2019). https://doi.org/10.1007/s10544-019-0458-x

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