Reinhold Scherer

The study presented here introduces a Passive BCI detecting responses of the subjects brain on the perception of correct and erroneous auditory signals. 10 experts in music theory who actively play an instrument listened to cadences, sequences of chords, that could have an unexpected, erroneous ending. In consistence with previous studies from the neurosciences we evoked an event-related potential, mainly

Brain-Computer Interfaces (BCI), although very promising, suffer from a poor reliability [1]. Rather than improving brain signal processing alone, an interesting research direction is to guide users to learn BCI control mastery. Thus, we present here a set of motivational and cognitive factors which could influence the learning process, and which should be considered to improve the global performance of BCI users. We based our study on Keller’s integrative theory of motivation, volition, and performance, which combines motivational (affective) and cognitive factors, to explain what makes human users learn and perform efficiently, irrespectively of the task [2]. These factors can guide the creation of learning environments, such as BCI training protocols. According to the theory, the optimization of motivational factors - Attention (triggering a person’s curiosity), Relevance (the compliance with a person’s motives or values), Confidence (the expectancy for success), and Satisfaction...

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

About 300,000 people in Europe alone suffer from a spinal cord injury (SCI), with 11,000 new injuries per year [20]. SCI is caused primarily by traffic and work accidents, and an increasing percentage of the total population also develops SCI from diseases like infections or tumors. About 70% of SCI cases occur in men. 40% are tetraplegic patients with paralyses

Brain-computer interface (BCI) research at the Graz University of Technology started with the classification of event-related desynchronization (ERD) [36, 38] of single-trial electroencephalographic (EEG) data during actual (overt) and imagined (covert) hand movement [9, 18, 40]. At the beginning of our BCI research activities we had a cooperation with the Wadsworth Center in Albany, New York State, USA, with the

Upper extremity deficits are prevalent in individuals with Parkinson disease (PD). In the early stages of PD, such deficits can be subtle and challenging to document on clinical examination. The purpose of this study was to use a novel force sensor system to characterize grip force modulation, including force, temporal, and movement quality parameters, during a fine motor control task in individuals with early stage PD. A case-control study was conducted. Fourteen individuals with early stage PD were compared with a control group of 14 healthy older adults. The relationship of force modulation parameters with motor symptom severity and disease chronicity also was assessed in people with PD. Force was measured during both precision and power grasp tasks using an instrumented twist-cap device capable of rotating in either direction. Compared with the control group, the PD group demonstrated more movement arrests during both precision and power grasp and longer total movement times dur...

ABSTRACT Models of human navigation play an important role for understanding and facilitating user behavior in hypertext systems. In this paper, we conduct a series of principled experiments with decentralized search - an established model of human navigation in social networks - and study its applicability to information networks. We apply several variations of decentralized search to model human navigation in information networks and we evaluate the outcome in a series of experiments. In these experiments, we study the validity of decentralized search by comparing it with human navigational paths from an actual information network - Wikipedia. We find that (i) navigation in social networks appears to differ from human navigation in information networks in interesting ways and (ii) in order to apply decentralized search to information networks, stochastic adaptations are required. Our work illuminates a way towards using decentralized search as a valid model for human navigation in information networks in future work. Our results are relevant for scientists who are interested in modeling human behavior in information networks and for engineers who are interested in using models and simulations of human behavior to improve on structural or user interface aspects of hypertextual systems.