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A Hybrid Brain-Computer Interface Fusing P300 ERP and Electrooculography

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XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 (MEDICON 2019)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 76))

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An Electrooculography-based method is used to correct misclassification of P300 event related potentials in a Lateral Character Speller (LSC) Brain-Computer Interface (BCI). The LSC speller’s circular layout allows us to combine P300 detection with the detection of eye movements to improve symbol detection reliability. We separately classify the vertical and horizontal components of Electrooculography signals from shifts in user gaze during intertrial intervals, determining the quadrant of the character the participant will focus on in the next trial. A P300 EEG-based classification decision can then be corrected using quadrant information, selecting the character with the highest probability on that quadrant. This paper focuses on the implementation of the EOG quadrant detector. Preliminary results show good lateral identification but a lower selection accuracy. Empirically, it was possible to conclude that a relatively high percentage of P300 classification errors were corrected using lateral information alone, significantly increasing LSC character selection accuracy.

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  1. Ahn, S., Jun, S.C.: Multi-modal integration of EEG-fNIRS for brain-computer interfaces-current limitations and future directions. Front. Hum. Neurosci. 11, 503 (2017)

    Article  Google Scholar 

  2. Bai, L., Yu, T., Li, Y.: A brain computer interface-based explorer. J. Neurosci. Methods 244, 2–7 (2015)

    Article  Google Scholar 

  3. Barbosa, S., Pires, G., Nunes, U.: Toward a reliable gaze-independent hybrid BCI combining visual and natural auditory stimuli. J. Neurosci. Methods 261, 47–61 (2016)

    Article  Google Scholar 

  4. Cruz, A., Pires, G., Nunes, U.J.: Double ErrP detection for automatic error correction in an ERP-based BCI speller. IEEE Trans. Neural Syst. Rehabil. Eng. 26(1), 26–36 (2018)

    Article  Google Scholar 

  5. Hong, K.S., Khan, M.J.: Hybrid brain-computer interface techniques for improved classification accuracy and increased number of commands: a review. Front. Neurorobotics 11, 35 (2017)

    Article  Google Scholar 

  6. Hortal, E., Iáñez, E., Úbeda, A., Perez-Vidal, C., Azorín, J.M.: Combining a brain-machine interface and an electrooculography interface to perform pick and place tasks with a robotic arm. Robot. Auton. Syst. 72, 181–188 (2015)

    Article  Google Scholar 

  7. Leeb, R., Sagha, H., Chavarriaga, R., del R Millán, J.: A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities. J. Neural Eng. 8(2), 025011 (2011)

    Article  Google Scholar 

  8. Mak, J.N., Wolpaw, J.R.: Clinical applications of brain-computer interfaces: current state and future prospects. IEEE Rev. Biomed. Eng. 2, 187–199 (2009)

    Article  Google Scholar 

  9. Müller-Putz, G., Leeb, R., Tangermann, M., Höhne, J., Kübler, A., Cincotti, F., Mattia, D., Rupp, R., Müller, K.R., Millán, J.d.R.: Towards noninvasive hybrid brain-computer interfaces: framework, practice, clinical application, and beyond. Proc. IEEE 103(6), 926–943 (2015)

    Article  Google Scholar 

  10. Perdiz, J., Garrote, L., Pires, G., Nunes, U.J.: Measuring the impact of reinforcement learning on an electrooculography-only computer game. In: 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH), pp. 1–8, May 2018

    Google Scholar 

  11. Pires, G., Nunes, U., Castelo-Branco, M.: Statistical spatial filtering for a P300-based BCI: tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. J. Neurosci. Methods 195(2), 270–281 (2011)

    Article  Google Scholar 

  12. Pires, G., Nunes, U., Castelo-Branco, M.: Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients. Clin. Neurophysiol. 123(6), 1168–1181 (2012)

    Article  Google Scholar 

  13. Riccio, A., Leotta, F., Bianchi, L., Aloise, F., Zickler, C., Hoogerwerf, E., Kübler, A., Mattia, D., Cincotti, F.: Workload measurement in a communication application operated through a P300-based brain-computer interface. J. Neural Eng. 8(2), 025028 (2011)

    Article  Google Scholar 

  14. Taher, F.B., Amor, N.B., Jallouli, M.: A multimodal wheelchair control system based on EEG signals and eye tracking fusion. In: 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), pp. 1–8 IEEE (2015)

    Google Scholar 

  15. Usakli, A., Gurkan, S., Aloise, F., Vecchiato, G., Babiloni, F.: A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 543–546. IEEE (2009)

    Google Scholar 

  16. Wang, H., Li, Y., Long, J., Yu, T., Gu, Z.: An asynchronous wheelchair control by hybrid EEG-EOG brain-computer interface. Cogn. Neurodyn. 8(5), 399–409 (2014)

    Article  Google Scholar 

  17. Yin, E., Zeyl, T., Saab, R., Chau, T., Hu, D., Zhou, Z.: A hybrid brain-computer interface based on the fusion of P300 and SSVEP scores. IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 693–701 (2015)

    Article  Google Scholar 

  18. Zhang, R., Li, Y., Yan, Y., Zhang, H., Wu, S., Yu, T., Gu, Z.: Control of a wheelchair in an indoor environment based on a brain-computer interface and automated navigation. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 128–139 (2016)

    Article  Google Scholar 

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This work was partially funded by the Portuguese Foundation for Science and Technology (FCT) under the Ph.D. Scholarship SFRH/BD/ 104985/2014 of João Perdiz and Ph.D. Scholarship SFRH/BD/ 111473/2015 of Aniana Cruz and was developed at the Institute of Systems and Robotics – University of Coimbra. This work was partially supported by Project B-RELIABLE: SAICT/30935/ 2017, with FEDER/FNR/OE funding through programs CENTRO2020 and FCT, and by UID/EEA/ 00048/2013 with FEDER funding, through programs QREN and COMPETE.

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Correspondence to João Perdiz .

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Perdiz, J., Cruz, A., Nunes, U.J., Pires, G. (2020). A Hybrid Brain-Computer Interface Fusing P300 ERP and Electrooculography. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham.

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