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)
(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.
Gamma oscillations of the electromagnetic field of the brain are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the brain. While gamma oscillations have been shown to be correlated with brain rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other brain rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the brain, and is of particular importance for brain–computer interfaces (BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a BCI for means of communication.
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.
In pages: 384-387, IEEE, Piscataway, NJ, USA, 5th International IEEE/EMBS Conference on Neural Engineering (NER) , May 2011 (inproceedings)
In recent work, we have provided evidence that fronto-parietal γ-oscillations of the electromagnetic field of the brain modulate the sensorimotor-rhythm. It is unclear, however, what impact this effect may have on explaining and addressing within-subject performance variations of brain-computer interfaces (BCIs). In this paper, we provide evidence that on a group-average classification accuracies in a two-class motor-imagery paradigm differ by up to 22.2% depending on the state of fronto-parietal γ-power. As such, this effect may have a large impact on the design of future BCI-systems. We further investigate whether adapting classification procedures to the current state of γ-power improves classification accuracy, and discuss other approaches to exploiting this effect.
In pages: 312-315, (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)
Robust dry EEG electrodes are arguably the key to making EEG Brain-Computer Interfaces (BCIs) a practical technology. Existing studies on dry EEG electrodes can be characterized by the recording method (stand-alone dry electrodes or simultaneous recording with wet electrodes), the dry electrode technology (e.g. active or passive), the paradigm used for testing (e.g. event-related potentials), and the measure of performance (e.g. comparing dry and wet electrode frequency spectra). In this study, an active-dry electrode prototype is tested, during a motor-imagery task, with EEG-BCI in mind. It is used simultaneously with wet electrodes and assessed using classification accuracy. Our results indicate that the two types of electrodes are comparable in their performance but there are improvements to be made, particularly in finding ways to reduce motion-related artifacts.
Computational Intelligence and Neuroscience, 2011(217987):1-9, August 2011 (article)
Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm
and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject
based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.
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