Abstract
Brain-computer interfaces (BCIs) can be used to improve human-machine interactions (HMIs) by providing implicit information about the mental state. We introduce a brain activity, perturbation-evoked potentials (PEPs), that was not yet investigated in the context of BCIs although it has the required properties. An experimental setup for studying PEPs is proposed and validated and two possible use cases for this brain activity are introduced.
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References
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.
Müller-Putz, G. R., Scherer, R., Pfurtscheller, G., & Rupp, R. (2005). EEG-based neuroprosthesis control: A step towards clinical practice. Neuroscience Letters, 382(1–2), 169–174.
Leeb, R., Friedman, D., Müller-Putz, G. R., Scherer, R., Slater, M., & Pfurtscheller, G. (2007). Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: A case study with a tetraplegic. Computational Intelligence and Neuroscience.
Pfurtscheller, G., Müller-Putz, G. R., Scherer, R., & Neuper, C. (2008). Rehabilitation with brain-computer interface systems. Computer, 41(10), 58–65.
Müller-Putz, G. R., Riedl, R., & Wriessnegger, S. C. (2015). Electroencephalography (EEG) as a research tool in the information systems discipline: Foundations, measurement, and applications. CAIS, 37, 46.
Bauernfeind, G., Wriessnegger, S., & Müller-Putz, G. (2014). Using near-infrared spectroscopy (NIRS) for brain-computer interface (BCI) systems. In Human Cognitive Neurophysiology.
Weiskopf, N., Mathiak, K., Bock, S. W., Scharnowski, F., Veit, R., Grodd, W., et al. (2004). Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Transactions on Biomedical Engineering, 51(6), 966–970.
Brunner, P., Ritaccio, A. L., Emrich, J. F., Bischof, H., & Schalk, G. (2011). Rapid communication with a “P300” matrix speller using electrocorticographic signals (ECoG). Frontiers in Neuroscience, 5, 5.
Wodlinger, B., Downey, J. E., Tyler-Kabara, E. C., Schwartz, A. B., Boninger, M. L., & Collinger, J. L. (2014). Ten-dimensional anthropomorphic arm control in a human brain−machine interface: Difficulties, solutions, and limitations. Journal of Neural Engineering, 12(1), 016011.
Fernández, E., Greger, B., House, P. A., Aranda, I., Botella, C., Albisua, J., et al. (2014). Acute human brain responses to intracortical microelectrode arrays: Challenges and future prospects. Frontiers in Neuroengineering, 7, 24.
Sellers, E. W., & Donchin, E. (2006). A P300-based brain–computer interface: Initial tests by ALS patients. Clinical Neurophysiology, 117(3), 538–548.
Pereira, J., Ofner, P., Schwarz, A., Sburlea, A. I., & Müller-Putz, G. R. (2017). EEG neural correlates of goal-directed movement intention. Neuroimage, 149, 129–140.
Müller-Putz, G. R., Scherer, R., Neuper, C., & Pfurtscheller, G. (2006). Steady-state somatosensory evoked potentials: Suitable brain signals for brain-computer interfaces? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(1), 30–37.
Zander, T. O., Kothe, C. A., Welke, S., & Rötting, M. (2008). Enhancing human–machine systems with secondary input from passive brain–computer interfaces. In Proceedings of the 4th International Brain–Computer Interface Workshop & Training Course (pp. 144–149). Graz, Austria: Verlag der Technischen Universität Graz.
Zander, T. O., Krol, L. R., Birbaumer, N. P., & Gramann, K. (2016). Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity. Proceedings of the National Academy of Sciences, 113(52), 14898–14903.
Parra, L. C., Spence, C. D., Gerson, A. D., & Sajda, P. (2003). Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 173–177.
Bos, D. P. O., Reuderink, B., van de Laar, B., Gürkök, H., Mühl, C., & Poel, M., et al. (2010). Brain-computer interfacing and games. In Brain-computer interfaces (pp. 149–178). London: Springer.
Holroyd, C. B., & Coles, M. G. (2002). The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109(4), 679.
Varghese, J. P., McIlroy, R. E., & Barnett-Cowan, M. (2017). Perturbation-evoked potentials: Significance and application in balance control research. Neuroscience and Biobehavioral Reviews, 83, 267–280.
Dietz, V., Quintern, J., & Berger, W. (1984). Cerebral evoked potentials associated with the compensatory reactions following stance and gait perturbation. Neuroscience Letters, 50(1–3), 181–186.
Duckrow, R. B., Abu-Hasaballah, K., Whipple, R., & Wolfson, L. (1999). Stance perturbation-evoked potentials in old people with poor gait and balance. Clinical Neurophysiology, 110(12), 2026–2032.
Adkin, A. L., Quant, S., Maki, B. E., & McIlroy, W. E. (2006). Cortical responses associated with predictable and unpredictable compensatory balance reactions. Experimental Brain Research, 172(1), 85.
Mochizuki, G., Sibley, K. M., Cheung, H. J., Camilleri, J. M., & McIlroy, W. E. (2009). Generalizability of perturbation-evoked cortical potentials: Independence from sensory, motor and overall postural state. Neuroscience Letters, 451(1), 40–44.
Quant, S., Adkin, A. L., Staines, W. R., & McIlroy, W. E. (2004). Cortical activation following a balance disturbance. Experimental Brain Research, 155(3), 393–400.
Marlin, A., Mochizuki, G., Staines, W. R., & McIlroy, W. E. (2014). Localizing evoked cortical activity associated with balance reactions: Does the anterior cingulate play a role? American Journal of Physiology-Heart and Circulatory Physiology.
Staines, R. W., McIlroy, W. E., & Brooke, J. D. (2001). Cortical representation of whole-body movement is modulated by proprioceptive discharge in humans. Experimental Brain Research, 138(2), 235–242.
Dietz, V., Quintern, J., Berger, W., & Schenck, E. (1985). Cerebral potentials and leg muscle emg responses associated with stance perturbation. Experimental Brain Research, 57(2), 348–354.
Quintern, J., Berger, W., & Dietz, V. (1985). Compensatory reactions to gait perturbations in man: Short-and long-term effects of neuronal adaptation. Neuroscience Letters, 62(3), 371–375.
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Ditz, J.C., Müller-Putz, G.R. (2020). Perturbation-Evoked Potentials: Future Usage in Human-Machine Interaction. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A., Fischer, T. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-28144-1_30
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DOI: https://doi.org/10.1007/978-3-030-28144-1_30
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