Selected
Krol, L. R., Zander, T. O., 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. DOI PDF
We show that it is possible to extract meaningful information from a person's automatic, involuntary brain responses to specific events. In our experiment, a computer presented cognitive probes to participants and gauged their automatic brain responses to them, essentially posing questions directly to the brain. It then used the information thus obtained to effectively control a cursor on the screen, without the participants realising that they were the source of this information.
Krol, L. R., Andreessen, L. M., & Zander, T. O. (2018). Passive Brain-Computer Interfaces: A Perspective on Increased Interactivity. In C. S. Nam, A. Nijholt, & F. Lotte (Eds.), Brain-Computer Interfaces Handbook: Technological and Theoretical Advances (pp. 69-86). Boca Raton, FL, USA: CRC Press. DOI PDF
Passive brain-computer interface (pBCI) technology can be used in a number of different ways to achieve different goals. Here, we present a categorisation of pBCI applications, and identify a trend leading towards fundamentally different interactive systems, which open up novel opportunities but also reveal potential ethical, legal, and societal issues.
Krol, L. R., Pawlitzki, J., Lotte, F., Gramann, K., & Zander, T. O. (2018). SEREEGA: Simulating Event-Related EEG Activity. Journal of Neuroscience Methods, 309, 13-24. DOI PDF
This describes a free and open source MATLAB-based toolbox that researchers can use to simulate event-related EEG data. Simulated EEG data provides a completely known ground truth, allowing EEG-based methods to be evaluated.
Krol, L. R., Haselager, P., & Zander, T. O. (2020). Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology. Journal of Neural Engineering, 17(1), 012001. doi: DOI PDF
Cognitive probing, described in this tutorial paper, refers to an application of active learning where repeated sampling is done by eliciting implicit brain responses. This is a powerful method to obtain information from human participants to feed into neuroadaptive systems, potentially without their consent or awareness. The paper therefore addresses both technological and ethical guidelines and principles.