Spatial filters selection towards a rehabilitation BCI

Introducing BCI technology in supporting motor imagery (MI) training has revealed the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in stroke patients. To provide the most accurate and personalized feedback during the treatment, several stages of the electroencephalographic signal processing have to be optimized, including spatial filtering. This study focuses on data-independent approaches to optimize spatial filtering step. Specific aims were: i) assessment of spatial filters' performance in relation to the hand and foot scalp areas; ii) evaluation of simultaneous use of multiple spatial filters; iii) minimization of the number of electrodes needed for training. Our findings indicate that different spatial filters showed different performance related to the scalp areas considered. The simultaneous use of EEG signals conditioned with different spatial filters could either improve classification performance or, at same level of performance could lead to a reduction of the number of electrodes needed for successive training, thus improving usability of BCIs in clinical rehabilitation context

The Promotoer: a successful story of translational research in BCI for motor rehabilitation

Several groups have recently demonstrated in the context of randomized controlled trials (RCTs) how sensorimotor Brain-Computer Interface (BCI) systems can be beneficial for post-stroke motor recovery. Following a successful RCT, at Fondazione Santa Lucia (FSL) a further translational effort was made with the implementation of the Promotœr, an all in-one BCIsupported MI training station. Up to now, 25 patients underwent training with the Promotɶr during their admission for rehabilitation purposes (in add-on to standard therapy). Two illustrative cases are presented. Though currently limited to FSL, the Promotɶr represents a successful story of translational research in BCI for stroke rehabilitation. Results are promising both in terms of feasibility of a BCI training in the context of a real rehabilitation program and in terms of clinical and neurophysiological benefits observed in the patients

EEG Resting-State Brain Topological Reorganization as a Function of Age

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

Electroencephalography (EEG)-Derived Markers to Measure Components of Attention Processing

Although extensively studied for decades, attention system remains an interesting challenge in neuroscience field. The Attention Network Task (ANT) has been developed to provide a measure of the efficiency for the three attention components identified in the Posner’s theoretical model: alerting, orienting and executive control. Here we propose a study on 15 healthy subjects who performed the ANT. We combined advanced methods for connectivity estimation on electroencephalographic (EEG) signals and graph theory with the aim to identify neuro-physiological indices describing the most important features of the three networks correlated with behavioral performances. Our results provided a set of band-specific connectivity indices able to follow the behavioral task performances among subjects for each attention component as defined in the ANT paradigm. Extracted EEG-based indices could be employed in future clinical applications to support the behavioral assessment or to evaluate the influence of specific attention deficits on Brain Computer Interface (BCI) performance and/or the effects of BCI training in cognitive rehabilitation applications

Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation

The Promotɶr is an all-in-one Brain Computer Interface (BCI)-system developed at Fondazione Santa Lucia (Rome, Italy) to support hand motor imagery practice after stroke. In this paper we focus on the optimization of control parameters for the BCI training. We compared two procedures for the feature selection: in the first, features were selected by means of a manual procedure (requiring “skilled users”), in the second a semiautomatic method, developed by us combining physiological and machine learning approaches, guided the feature selection. EEG-based BCI data set collected from 13 stroke patients were analyzed to the aim. No differences were found between the two procedures (paired-samples t-test, p=0.13). Results suggest that the semiautomatic procedure could be applied to support the manual feature selection, allowing no-skilled users to approach BCI technology for motor rehabilitation of stroke patients

User-centered design in brain–computer interfaces — a case study

The array of available brain–computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and – if necessary – adapted to him/her until a suitable BCI system is found

Effect of Hemp Hurd Biochar and Humic Acid on the Flame Retardant and Mechanical Properties of Ethylene Vinyl Acetate

In this work, the combination of biochar produced through a pyrolytic process of hemp hurd with commercial humic acid as a potential biomass-based flame-retardant system for ethylene vinyl acetate copolymer is thoroughly investigated. To this aim, ethylene vinyl acetate composites containing hemp-derived biochar at two different concentrations (i.e., 20 and 40 wt.%) and 10 wt.% of humic acid were prepared. The presence of increasing biochar loadings in ethylene vinyl acetate accounted for an increasing thermal and thermo-oxidative stability of the copolymer; conversely, the acidic character of humic acid anticipated the degradation of the copolymer matrix, even in the presence of the biochar. Further, as assessed by forced-combustion tests, the incorporation of humic acid only in ethylene vinyl acetate slightly decreased both peaks of heat release rate (pkHRR) and total heat release (THR, by 16% and 5%, respectively), with no effect on the burning time. At variance, for the composites containing biochar, a strong decrease in pkHRR and THR values was observed, approaching -69 and -29%, respectively, in the presence of the highest filler loading, notwithstanding, for this latter, a significant increase in the burning time (by about 50 s). Finally, the presence of humic acid significantly lowered the Young’s modulus, unlike biochar, for which the stiffness remarkably increased from 57 MPa (unfilled ethylene vinyl acetate) to 155 Mpa (for the composite containing 40 wt.% of the filler)

Ethylene-Vinyl Acetate (EVA) containing waste hemp-derived biochar fibers: mechanical, electrical, thermal and tribological behavior

To reduce the use of carbon components sourced from fossil fuels, hemp fibers were pyrolyzed and utilized as filler to prepare EVA-based composites for automotive applications. The mechanical, tribological, electrical (DC and AC) and thermal properties of EVA/fiber biochar (HFB) composites containing different amounts of fibers (ranging from 5 to 40 wt.%) have been thoroughly studied. The morphological analysis highlighted an uneven dispersion of the filler within the polymer matrix, with poor interfacial adhesion. The presence of biochar fibers did not affect the thermal behavior of EVA (no significant changes of Tm, Tc and Tg were observed), notwithstanding a slight increase in the crystallinity degree, especially for EVA/HFB 90/10 and 80/20. Conversely, biochar fibers enhanced the thermo-oxidative stability of the composites, which increased with increasing the biochar content. EVA/HFB composites showed higher stiffness and lower ductility than neat EVA. In addition, high concentrations of fiber biochar allowed achieving higher thermal conductivity and microwave electrical conductivity. In particular, EVA/HFB 60/40 showed a thermal conductivity higher than that of neat EVA (respectively, 0.40 vs. 0.33 W·m−1 ·K−1); the same composite exhibited an up to twenty-fold increased microwave conductivity. Finally, the combination of stiffness, enhanced thermal conductivity and intrinsic lubricating features of the filler resulted in excellent wear resistance and friction reduction in comparison with unfilled EVA

Mechanical, electrical, thermal and tribological behavior of epoxy resin composites reinforced with waste hemp-derived carbon fibers

Short hemp fibers, an agricultural waste, were used for producing biochar by pyrolysis at 1000°C. The so-obtained hemp-derived carbon fibers (HFB) were used as filler for improving the properties of an epoxy resin using a simple casting and curing process. The addition of HFB in the epoxy matrix increases the storage modulus while damping factor is lowered. Also, the incorporation of HFB induces a remarkable increment of electrical conductivity reaching up to 6 mS/m with 10 wt% of loading. A similar trend is also observed during high frequency measurements. Furthermore, for the first time wear of these composites has been studied. The use of HFB is an efficient method for reducing the wear rate resistance and the friction coefficient (COF) of the epoxy resin. Excellent results are obtained for the composite containing 2.5 wt% of HFB, for which COF and wear rate decrease by 21% and 80%, respectively, as compared with those of the unfilled epoxy resin. The overall results prove how a common waste carbon source can significantly wide epoxy resin applications by a proper modulation of its electrical and wear properties

Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis

Brain-computer interface (BCI) can provide people with motor disabilities with an alternative channel to access assistive technology (AT) software for communication and environmental interaction. Multiple sclerosis (MS) is a chronic disease of the central nervous system that mostly starts in young adulthood and often leads to a long-term disability, possibly exacerbated by the presence of fatigue. Patients with MS have been rarely considered as potential BCI end-users. In this pilot study, we evaluated the usability of a hybrid BCI (h-BCI) system that enables both a P300-based BCI and conventional input devices (i.e., muscular dependent) to access mainstream applications through the widely used AT software for communication "Grid 3." The evaluation was performed according to the principles of the user-centered design (UCD) with the aim of providing patients with MS with an alternative control channel (i.e., BCI), potentially less sensitive to fatigue. A total of 13 patients with MS were enrolled. In session I, participants were presented with a widely validated P300-based BCI (P3-speller); in session II, they had to operate Grid 3 to access three mainstream applications with (1) an AT conventional input device and (2) the h-BCI. Eight patients completed the protocol. Five out of eight patients with MS were successfully able to access the Grid 3 via the BCI, with a mean online accuracy of 83.3% (+/- 14.6). Effectiveness (online accuracy), satisfaction, and workload were comparable between the conventional AT inputs and the BCI channel in controlling the Grid 3. As expected, the efficiency (time for correct selection) resulted to be significantly lower for the BCI with respect to the AT conventional channels (Z = 0.2, p < 0.05). Although cautious due to the limited sample size, these preliminary findings indicated that the BCI control channel did not have a detrimental effect with respect to conventional AT channels on the ability to operate an AT software (Grid 3). Therefore, we inferred that the usability of the two access modalities was comparable. The integration of BCI with commercial AT input devices to access a widely used AT software represents an important step toward the introduction of BCIs into the AT centers' daily practice