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Background Stroke is a leading cause of long-term disability. Cost-effective post-stroke rehabilitation programs for upper limb are critically needed. Brain-Computer Interfaces (BCIs) which enable the modulation of Electroencephalography (EEG) sensorimotor rhythms are promising tools to promote post-stroke recovery of upper limb motor function. The “Promotoer” study intends to boost the application of the EEG-based BCIs in clinical practice providing evidence for a short/long-term efficacy in enhancing post-stroke hand functional motor recovery and quantifiable indices of the participants response to a BCI-based intervention. To these aims, a longitudinal study will be performed in which subacute stroke participants will undergo a hand motor imagery (MI) training assisted by the Promotoer system, an EEG-based BCI system fully compliant with rehabilitation requirements. Methods This longitudinal 2-arm randomized controlled superiority trial will include 48 first ever, unilateral, subacute stroke participants, randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and a hand MI training not supported by BCI. Both interventions are delivered (3 weekly session; 6 weeks) as add-on regimen to standard intensive rehabilitation. A multidimensional assessment will be performed at: randomization/pre-intervention, 48 h post-intervention, and at 1, 3 and 6 month/s after end of intervention. Primary outcome measure is the Fugl-Meyer Assessment (FMA, upper extremity) at 48 h post-intervention. Secondary outcome measures include: the upper extremity FMA at follow-up, the Modified Ashworth Scale, the Numeric Rating Scale for pain, the Action Research Arm Test, the National Institute of Health Stroke Scale, the Manual Muscle Test, all collected at the different timepoints as well as neurophysiological and neuroimaging measures. Discussion We expect the BCI-based rewarding of hand MI practice to promote long-lasting retention of the early induced improvement in hand motor outcome and also, this clinical improvement to be sustained by a long-lasting neuroplasticity changes harnessed by the BCI-based intervention. Furthermore, the longitudinal multidimensional assessment will address the selection of those stroke participants who best benefit of a BCI-assisted therapy, consistently advancing the transfer of BCIs to a best clinical practice. Trial registration Name of registry: BCI-assisted MI Intervention in Subacute Stroke (Promotoer). Trial registration number: NCT04353297; registration date on the ClinicalTrial.gov platform: April, 15/2020.

Proceedings Of The 7Th Graz Brain-Computer Interface Conference 2017, From Vision To Reality, September 18-22, 2017 Graz University Of Technology, Austria

Proceedings Of The 6Th International Bci Meeting: Bci Past, Present, And Future

Proceedings of the fifth International Brain-Computer Interface Meeting

Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users� training and control of EEG-based BCI applications. An experimental setup for delivery of vibrotactile feedback, including specific hardware and software arrangements, was specified. We compared vibrotactile and visual feedback, addressing the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The preliminary experimental setup included a simulated BCI control. in which all parts reflected the computational and actuation process of an actual BCI, except the souce, which was simulated using a �noisy� PC mouse. Results indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task.

A Brain-Computer Interface (BCI) allow direct expression of its user�s will by interpreting signals which directly reflect the brain�s activity, thus bypassing the natural efferent channels (nerves and muscles). To be correctly mastered, it is needed that this artificial efferent channel is complemented by an artificial feedback, which continuously informs the user about the current state (in the same way as proprioceptors give a feedback about joint angle and muscular tension). This feedback is usually delivered through the visual channel. We explored the benefits of vibrotactile feedback during users� training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified and implemented. Thirteen subjects participated in an experiment where the feedback of the BCI system was delivered either through a visual display, or through a vibrotactile display, while they performed a virtual navigation task. Attention to the task was probed by presenting visual cues that the subjects had to describe afterwards. When compared with visual feedback, the use of tactile feedback did not decrease BCI control performance; on the other side, it improved the capacity of subjects to concentrate on the requested (visual) task. During experiments, vibrotactile feedback felt (after some training) more natural. This study indicated that the vibrotactile channel can function as a valuable feedback modality in the context of BCI applications. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task.

In their early days, BCIs were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cannot only translate brain signals directly into control signals, but also can combine such kind of artificial output with a natural muscle-based output. Thus, the integration of multiple biological signals for real-time interaction holds the promise to enhance a much larger population than originally thought — end users with preserved residual functions who could benefit from new generations of assistive technologies. A BCI system that combines a BCI with other physiological or technical signals is known as hybrid BCI (hBCI). In this work we review the work of a large scale integrated project funded by the European commission which was dedicated to develop practical hybrid BCIs and introduce them in various fields of applications. We present a hBCI framework, which was used in studies with non-impaired as well as end users with motor impairments.

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. Among various methods established during the years, the Directed Transfer Function (DTF), the Partial Directed Coherence (PDC) and the direct DTF (dDTF) are frequency-domain approaches to this problem, all based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. In this paper we propose the use of these methods on cortical signals estimated from high resolution EEG recordings, a non invasive method which exhibits a higher spatial resolution than conventional cerebral electromagnetic measures. The principle contribution of this work are the results of a simulation study, testing the capability of the three estimators to reconstruct a connectivity model imposed, with a particular eye on the capability to distinguish between direct and indirect causality. An application to high resolution EEG recordings during a foot movement is also presented.