Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI 2017), (Editors: Gal Elidan, Kristian Kersting, and Alexander T. Ihler), 2017, *equal contribution (conference)
Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), pages: 384-388, (Editors: Gernot R. Müller-Putz, David Steyrl, Selina C. Wriessnegger, Reinhold Scherer), Verlag der Technischen Universität Graz, 2017 (conference)
Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), pages: 160-164, (Editors: Gernot R. Müller-Putz, David Steyrl, Selina C. Wriessnegger, Reinhold Scherer), Verlag der Technischen Universität Graz, 2017 (conference)
Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), pages: 131-136, (Editors: Gernot R. Müller-Putz, David Steyrl, Selina C. Wriessnegger, Reinhold Scherer), Verlag der Technischen Universität Graz, 2017 (conference)
Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017) , pages: 165-170, (Editors: Gernot R. Müller-Putz, David Steyrl, Selina C. Wriessnegger, Reinhold Scherer), Verlag der Technischen Universität Graz, 2017 (conference)
Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), pages: 326-331, (Editors: Gernot R. Müller-Putz, David Steyrl, Selina C. Wriessnegger, Reinhold Scherer), Verlag der Technischen Universität Graz, 2017 (conference)
Journal of Neural Engineering, 11(5):056015, 2014 (article)
Objective. Brain–computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI. Approach. We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS. Main results. Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2%. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8%). Significance. Self-regulation of gamma-power in the SPC is a feasible paradigm for brain–computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.
In Proceedings of the 6th International Brain-Computer Interface Conference, (Editors: G Müller-Putz and G Bauernfeind and C Brunner and D Steyrl and S Wriessnegger and R Scherer), 2014 (inproceedings)
In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, pages: Article ID: 086, (Editors: J.d.R. Millán, S. Gao, R. Müller-Putz, J.R. Wolpaw, and J.E. Huggins), Verlag der Technischen Universität Graz, 5th International Brain-Computer Interface Meeting, 2013, Article ID: 086 (inproceedings)
Brain-computer interfaces (BCIs) provide a new means of communication that does not rely on volitional muscle control. This may provide the capability to locked-in patients, e.g., those suffering from amyotrophic lateral sclerosis, to maintain interactions with their environment. Besides providing communication capabilities to locked-in patients, BCIs may further prove to have a beneficial impact on stroke rehabilitation. In this article, the state-of-the-art of BCIs is reviewed and current research questions are discussed.
(3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)
Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency gamma-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this nding as empirical support for an in uence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.
Journal of Neural Engineering, 9(4):046001, May 2012 (article)
Subjects operating a brain–computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that
high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.
International Journal of Bioelectromagnetism, 13(3):115-116, September 2011 (article)
While research on brain-computer interfacing (BCI) has seen tremendous progress in recent years, performance still varies substantially between as well as within subjects, with roughly 10 - 20% of subjects being incapable of successfully operating a BCI system. In this short report, I argue that this variation in performance constitutes one of the major obstacles that impedes a successful commercialization of BCI systems. I review the current state of research on the neuro-physiological causes of performance variation in BCI, discuss recent progress and open problems, and delineate potential research programs for addressing this issue.
Journal of Neural Engineering, 8(2):1-5, April 2011 (article)
Analyzing neural signals and providing feedback in real-time is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI-technology for therapeutic purposes is increasingly gaining popularity in the BCI-community. In this review, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. The review concludes with a list of open questions and recommendations for future research in this field.
Journal of Neural Engineering, 8(3):1-12, June 2011 (article)
The combination of brain–computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.
In pages: 6, IEEE, Piscataway, NJ, USA, 12th International Conference on Rehabilitation Robotics (ICORR), July 2011 (inproceedings)
A neurorehabilitation approach that combines robot-assisted active physical therapy and Brain-Computer Interfaces (BCIs) may provide an additional mileage with respect to traditional rehabilitation methods for patients with severe motor impairment due to cerebrovascular brain damage (e.g., stroke) and other neurological conditions. In this paper, we describe the design and modes of operation of a robot-based rehabilitation framework that enables artificial support of the sensorimotor feedback loop. The aim is to increase cortical plasticity by means of Hebbian-type learning rules. A BCI-based shared-control strategy is used to drive a Barret WAM 7-degree-of-freedom arm that guides a subject's arm. Experimental validation of our setup is carried out both with healthy subjects and stroke patients. We review the empirical results which we have obtained to date, and argue that they support the feasibility of future rehabilitative treatments employing this novel approach.
In pages: 172-175, (Editors: Müller-Putz, G.R. , R. Scherer, M. Billinger, A. Kreilinger, V. Kaiser, C. Neuper), Verlag der Technischen Universität Graz, Graz, Austria, 5th International Brain-Computer Interface Conference (BCI), September 2011 (inproceedings)
In recent work, we have provided evidence that fronto-parietal γ-range oscillations are a cause of within-subject performance variations in brain-computer interfaces (BCIs) based on motor-imagery. Here, we explore the feasibility of using neurofeedback of fronto-parietal γ-power to induce a mental state that is beneficial for BCI-performance. We provide empirical evidence based on two healthy subjects that intentional attenuation of fronto-parietal γ-power results in an enhanced resting-state sensorimotor-rhythm (SMR). As a large resting-state amplitude of the SMR has been shown to correlate with good BCI-performance, our approach may provide a means to reduce performance variations in BCIs.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems