Ludic Lunchtalk: Dr. Thorsten O. Zander – From direct control to neuroadaptivity: The use of Brain‐Computer Interfaces for Human‐Machine Systems
Supported by Austrian Research fund FWF/PEEK, lead University of Applied Arts Vienna in cooperation with national and international research partners, Philosophy of Media and Technology, University Vienna, Game Design/ Serious Game Research, Zurich University of the Arts and Computational Neuroscience, BTU Cottbus-Senftenberg/ LMU Munich
Dr. Thorsten O. Zander
Brandenburg Technical University (BTU), Germany / Zander Laboratories, Amsterdam, The Netherlands
In my talk I will provide an overview of recent developments how Brain-Computer Interfaces (BCI) can be applied in Human-Machine Systems, specifically for users without disabilities. Next to direct control paradigms – which might find application in specific use cases – Passive BCIs (pBCIs, [1]) have proven to be a powerful tool to provide information to technical systems without the need for any additional attention or effort by the user.Passive Brain‐Computer Interfaces can assess information about changes in cognitive and affective state in real time and convey an interpretation of these states as implicit commands [2] to a machine.
The machine can then automatically adapt its own state to support a given task in the Human‐Machine System [3]. Furthermore, by collating information about the user state with the task‐specific context and using methods from machine learning and artificial intelligence a user model can be generated that even reflects correlates of higher cognition [4]. The resulting Neuroadaptive Technology leads to a convergence of human and machine intelligence and enables a fundamentally new way of interaction with technology [4, 5, 6].
I will give brief examples for each of the above‐mentioned technical approaches and discuss the hurdles that need to be taken to bring Neuroadaptive Technologies into our daily lives.
References:
[1] Zander, T. O., & Kothe, C. (2011). Towards passive brain–computer interfaces: applying brain–computerinterface technology to human–machine systems in general. Journal of neural engineering, 8(2), 025005.
[2] Zander, T. O., Brönstrup, J., Lorenz, R., & Krol, L. R. (2014). Towards BCI-based implicit control in human–computer interaction. In Advances in Physiological Computing (pp. 67-90). Springer, London.
[3] Zander, T. O., Kothe, C., Jatzev, S., & Gaertner, M. (2010). Enhancing human-computer interaction with inputfrom active and passive brain-computer interfaces. In Brain-computer interfaces (pp. 181-199). Springer, London.
[4] Zander, T. O., Krol, L. R., Birbaumer, N. P., & Gramann, K. (2016). Neuroadaptive technology enables implicitcursor control based on medial prefrontal cortex activity. Proceedings of the National Academy ofSciences, 113(52), 14898-14903.
[5] Lorenz, R., Monti, R. P., Violante, I. R., Anagnostopoulos, C., Faisal, A. A., Montana, G., & Leech, R. (2016).The automatic neuroscientist: a framework for optimizing experimental design with closed-loop real-timefMRI. NeuroImage, 129, 320-334.
[6] Iturrate, I., Chavarriaga, R., Montesano, L., Minguez, J., & Millán, J. D. R. (2015). Teaching brain-machineinterfaces as an alternative paradigm to neuroprosthetics control. Scientific reports, 5, 13893.