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In several fields, the need for a joint analysis of brain activity and eye activity to investigate the association between brain mechanisms and manifest behavior has been felt. In this work, two levels of attentional demand, elicited through a conjunction search task, have been modelled in terms of eye blinks, brain activity, and brain network features. Moreover, the association between endogenous neural mechanisms underlying attentional demand and eye blinks, without imposing a time-locked structure to the analysis, has been investigated. The analysis revealed statistically significant spatial and spectral modulations of the recorded brain activity according to the different levels of attentional demand, and a significant reduction in the number of eye blinks when a higher amount of attentional investment was required. Besides, the integration of information coming from high-density electroencephalography (EEG), brain source localization, and connectivity estimation allowed us to merge spectral and causal information between brain areas, characterizing a comprehensive model of neurophysiological processes behind attentional demand. The analysis of the association between eye and brain-related parameters revealed a statistically significant high correlation (R > 0.7) of eye blink rate with anterofrontal brain activity at 8 Hz, centroparietal brain activity at 12 Hz, and a significant moderate correlation with the participation of right Intra Parietal Sulcus in alpha band (R = −0.62). Due to these findings, this work suggests the possibility of using eye blinks measured from one sensor placed on the forehead as an unobtrusive measure correlating with neural mechanisms underpinning attentional demand.

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

The Brindisys project aims at designing and developing a general assistive technology to support communication and autonomy in people with Amyotrophic Lateral Sclerosis (ALS) from the onset of the disease to the locked-in phase. The prototype consists of a specific interface allowing for communication and environmental control that can be managed both with conventional/assistive input devices and with a P300-based Brain Computer Interface (BCI). This work describes the system functionalities and reports a preliminary assessment with end users.

Brain Computer Interface (BCI) systems aim to provide people with severe motor impairment with an additional/alternative channel to communicate and interact with the external world. At the state of the art there are several custom computer programs specifically developed to be controlled through a BCI. However from the end users' point of view it is important to become familiar with their own assistive device before the BCI represents the only way to access it. The Brindisys project aims at designing and developing a general assistive technology to support communication and autonomy in people with Amyotrophic Lateral Sclerosis (ALS) from the onset of the disease to the locked-in phase. The prototype consists of a specific interface allowing for communication and environmental control that can be managed both with conventional/assistive input devices and with a P300-based BCI. This work describes the system functionalities and reports the results of a preliminary assessment with end users and healthy control subjects.

Previous studies reported controversial results about the efficacy of video-game based therapy (VGT) in improving neurorehabilitation outcomes in children with cerebral palsy (CP).Primary aim was to investigate the effectiveness of VGT with respect to conventional therapy (CT) in improving upper limb motor outcomes in a group of children with CP. Secondary aim was to quantify if VGT leads children to perform a higher number of movements.A cross-over randomized controlled trial (RCT) for investigating the primary aim and a cross-sectional study for investigating the secondary aim of this study.Outpatients.clinical diagnosis of CP, age between 4 and 14 years, level of GMFC between I and IV.QI35, severe comorbidities, incapacity to stand even with an external support.Twenty-two children with CP (6.89±1.91-year old) were enrolled in a cross-over RCT with 16 sessions of VGT (using Xbox with Kinect device) and then 16 of CT or vice versa. Upper limb functioning was assessed using the Quality of Upper Extremities Skills Test (QUEST) and hand abilities using Abilhand-kids score. According to the secondary aim of this study a secondary cross-sectional study has been performed. Eight children with CP (6.50±1.60-year old) were enrolled into a trial in which five wireless triaxial accelerometers were positioned on their forearms, legs and trunk for quantifying the physical activity during VGT vs. CT.QUEST scores significantly improved only after VGT (P=0.003), and not after CT (P=0.056). The reverse occurred for Abilhand-kids scores (P=0.165 vs. P=0.013, respectively). Quantity of performed movements was three times higher in VGT than in CT (+198%, P=0.027).VGT resulted effective in improving the motor functions of upper limb extremities in children with CP, conceivably for the increased quantity of limb movements, but failed in improving the manual abilities for performing activities of daily living which benefited more from CT.VGT performed using the X-Box with Kinect device could enhance the number of upper limb movements in children with CP during rehabilitation and in turn improving upper limb motor skills, but CT remained superior for improving performances in manual activities of daily living.

Proceedings of the fifth International Brain-Computer Interface Meeting

International audience; The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated.