Brain Computer Interface Based Communicator for Persons in Locked-in State

Vlahinić, Saša; Batistić, Luka; Jadav, Guruprasad Madhale; Vrankić, Miroslav

doi:10.15388/Informatica.2019.229

Informatica

Brain Computer Interface Based Communicator for Persons in Locked-in State

Volume 30, Issue 4 (2019), pp. 781–798

Saša Vlahinić Luka Batistić Guruprasad Madhale Jadav Miroslav Vrankić

https://doi.org/10.15388/Informatica.2019.229

Pub. online: 1 January 2019 Type: Research Article

Open Access

Received
1 October 2017

Accepted
1 May 2019

Published
1 January 2019

Abstract

Brain Computer Interfaces (BCI) are devices that use brain signals for control or communication. Since they don’t require movement of any part of the body, BCI are the natural choice for assisted communication when a person is unable to move.

In this article, BCI based communicator for persons in locked-in state is described. It is based on P300 brain response of the user, thus does not require prior training, movement or imagination of movement. Auditory paradigm is selected in order to apply the communicator in cases where visual ability is also impaired. The communicator was designed to prove also whether low cost hardware with reduced electrode set could be used efficiently in everyday environment, without the need for expert personnel.

The design of the communicator is described first, followed by detailed analyses of the performance when used by either healthy or disabled subjects. It is shown that auditory paradigm is the primary factor that limits the accuracy of communication. Hardware characteristics and reduced electrode set influence the accuracy in a negative way as well, while different questions and answer types produce no major differences.

References

Albera, L., Ferréol, A., Cosandier-Rimélé, D., Merlet, I., Wendling, F. (2008). Brain source localization using a fourth order deflation scheme. IEEE Transactions on Biomedical Engineering, 55(2), 490–501.

Badcock, N.A., Mousikou, P., Mahajan, Y., de Lissa, P., Thie, J., McArthur, G. (2013). Validation of the Emotiv EPOC EEG gaming system for measuring research quality auditory ERPs. PeerJ, 1, 1–17.

Bayliss, J.D., Ballard, D.H. (1999). Single trial p300 recognition in a virtual environment. Proceedings of Soft Computing in Biomedicine. In: CIMA’99, Rochester, USA.

Birbaumer, N., Kübler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., Iversen, I., Kotchoubey, B., Neumann, N., Flor, H. (2000). The thought translation device (TTD) for completely paralyzed patients. IEEE Transactions on Rehabilitation Engineering, 8(2), 190–193.

Birbaumer, N., Cohen, L.G. (2007). Brain-computer interfaces: communication and restoration of movements in paralysis. The Journal of Physiology, 579(3), 621–636.

Farwell, L.A., Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70, 510–523.

Guger, G., Spataro, R., Pellas, F., Allison, B.Z., Heilinger, A., Ortner, R., Cho, W., Xu, R., La Bella, V., Edlinger, G., Annen, J., Mandalá, G., Chatelle, C., Laureys, S. (2018). Assessing command-following and communication with vibro-tactile P300 brain-computer interface tools in patients with unresponsive wakefulness syndrome. Frontiers in Neuroscience, 12(423), 1–9.

Halder, S., Rea, M., Andreoni, R., Nijboer, F., Hammer, E.M., Kleih, S.C., Birbaumer, N., Kübler, A. (2010). An auditory oddball brain-computer interface for binary choices. CClinical Neurophysiology, 121(4), 516–523.

Hill, N.J., Lal, T.N., Bierig, K., Birbaumer, N., Scholkopf, B. (2004). Attentional modulation of auditory event-related potentials in a brain-computer interface. In: Proceedings of the IEEE Internacional Workshop on Biomedical Circuits and Systems, Singapore, pp. 17–19.

Hill, N.J., Lal, T.N., Bierig, K., Birbaumer, N., Scholkopf, B.N. (2005). An auditory paradigm for brain-computer interfaces. In: Advances in Neural Information Processing Systems. MIT Press, USA, pp. 569–576.

Hoffmann, U., Vesin, J.M., Ebrahimi, T., Diserens, K. (2008). An efficient p300-based brain-computer interface for disabled subjects. Journal of Neuroscience Methods, 167(1), 115–125.

James, C.J., Hesse, C.W. (2005). Independent component analysis for biomedical signals. Physiological Measurement, 26, 15–39.

Kachenoura, A., Albera, L., Senhadji, L., Comon, P. (2008). ICA: a potential tool for BCI systems. IEEE Signal Processing Magazine, 25(1), 57–68.

Käthner, I., Kübler, A., Halder, S. (2015). Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: a case study with a participant in the locked-in state. Journal of NeuroEngineering and Rehabilitation, 12(76), 1–11.

Kevric, J., Subasi, A. (2017). Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomedical Signal Processing and Control, 31, 398–406.

Klobassa, D.S., Vaughan, T.M., Brunner, P., Schwartz, N.E., Wolpaw, J.R., Neuper, C., Sellers, E.W. (2009). Toward a high-throughput auditory P300-based brain-computer interface. Clinical Neurophysiology, 120(7), 1252–1261.

Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R. (2008). Toward enhanced P300 speller performance. Journal of Neuroscience Methods, 167, 15–21.

Lesenfants, D., Habbal, D., Chatelle, C., Schnakers, C., Laureys, S., Noirhomme, Q. (2016). Electromyographic decoding of response to command in disorders of consciousness. Neurology, 87(20), 2099–2107.

Lopez-Gordo, M.A., Fernandez, E., Romero, S., Pelayo, F., Prieto, A. (2012). An auditory brain-computer interface evoked by natural speech. Journal of Neural Engineering, 9, 1–9.

Minguillon, J., Lopez-Gordo, M.A., Pelayo, F. (2017). Trends in EEG-BCI for daily-life: requirements for artifact removal. Biomedical Signal Processing and Control, 31, 407–418.

Nguyen, T., Khosravi, A., Creighton, D., Nahavandi, S. (2015). Fuzzy system with tabu search learning for classification of motor imagery data. Biomedical Signal Processing and Control, 20, 61–70.

Ortner, R., Allison, B.Z., Pichler, G., Heilinger, A., Sabathiel, N., Guger, C. (2017). Assessment and communication for people with disorders of consciousness. Journal of Visualized Experiments, 126, 1–8.

Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lé, A. (2010). OpenViBE: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence: Teleoperators and Virtual Enviornments, 19(1), 35–53.

Rivet, B., Souloumiac, A., Attina, V., Gibert, G. (2009). xDAWN algorithm to enhance evoked potentials: application to brain computer interface. IEEE Transactions on Biomedical Engineering, 56(8), 2035–2043.

Rivet, B., Souloumiac, A. (2013). Optimal linear spatial filters for event-related potentials based on a spatio-temporal model: asymptotical performance analysis. Signal Processing, 93(2), 387–398.

Simon, N., Käthner, I., Ruf, C.A., Pasqualotto, E., Kübler, A., Halder, S. (2015). An auditory multiclass brain-computer interface with natural stimuli: usability evaluation with healthy participants and a motor impaired end user. Frontiers in Human Neuroscience, 8(1), 1–14.

Schnakers, C., Perrin, F., Schabus, M., Majerus, S., Ledoux, D., Damas, P., Boly, M., Vanhaudenhuyse, A., Bruno, M.A., Moonen, G. et al. (2008). Voluntary brain processing in disorders of consciousness. Neurology, 71, 1614–1620.

Sellers, E.W., Donchin, E. (2006). A P300-based brain-computer interface: initial tests by ALS patients. Clinical Neurophysiology, 117(3), 538–548.

Sellers, E.W., Ryan, D.B., Hauser, C.K. (2014). Noninvasive brain computer interface enables communication after brainstem stroke. Science Translational Medicine, 6, 257–272.

Shen, J., Liang, J., Shi, J., Wang, Y. (2015). A dynamic submatrix-based P300 online brain-computer interface. Biomedical Signal Processing and Control, 15, 27–32.

Wang, W., James., C. (2006). Enhancing evoked responses for BCI through advanced ICA techniques. In: MEDSIP 06, 3rd International Conference on Advances in Medical, Signal and Information Processing, Glasgow, UK, pp. 38–41.

Wolpaw, J.R., Ramoser, H˜.N., McFarland, D.J., Pfurtscheller, G. (1998). EEG-based communication: improved accuracy by response verification. IEEE Transactions on Rehabilitation Engineering, 6(3), 326–333.

Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791.

Xu, N., Gao, X., Hong, B., Miao, X., Gao, S., Yang, F. (2004). BCI competition 2003-data set iib: Enhancing p300 wave detection using ICA based subspace projections for BCI applications. IEEE Transactions on Biomedical Engineering, 51(6), 1067–1072.

Yuan, P., Gao, X., Allison, B., Wang, Y., Bin, G., Gao, S. (2013). A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces. Journal of Neural Engineering, 10(2), 1–11.

Biographies

Vlahinić Saša

sasa.vlahinic@riteh.hr

S. Vlahinić received the BSc and PhD degrees in electronic engineering from University of Trieste, Trieste, Italy, in 1998 and 2003, respectively. Since 2000 he had been young researcher at the Department of Automation, Electronics and Computing, Faculty of Engineering, University of Rijeka, Rijeka, Croatia. Currently, he is a full professor at the same faculty and head of Department of Automation and Electronics. His current research interests are concerned with BCI technologies and EEG signal processing.

Batistić Luka

L. Batistić received his bachelor’s and master’s degrees in computer engineering (years 2014 and 2017, respectively) from Faculty of Engineering, University of Rijeka. Since 2016, he is a junior researcher in Department of Automation and Electronics, Faculty of Engineering, University of Rijeka. Currently he is also a teaching assistant and a PhD student at the same faculty.

Jadav Guruprasad Madhale

G. Madhale Jadav received his BTech in electronics and instrumentation technology from the Visvesvaraya Technological University, Belgaum, Karnataka, India, in 2013. Since 2014 he has been pursuing his PhD as a young researcher at the Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Rijeka, Croatia. His current field of research deal with BCI technology and EEG signal processing.

Vrankić Miroslav

M. Vrankić received his diploma degree, as well as MS and PhD degrees from University of Zagreb, Faculty of Electrical Engineering and Computing. Since 1999 he has been with the Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia. Since 2003 he has been with the Faculty of Engineering, University of Rijeka, Croatia, where he is currently an associated professor.

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Open access article under the CC BY license.

Keywords

BCI communicator P300 ERP auditory stimuli

Funding

This work was supported by the Croatian Agency HAMAG-BICRO, grant number POC6-2-32.

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