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Hybrid Brain–Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of “more normal” brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.

EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user’s needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.

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 &nbsp

The application of Hybrid Brain-Computer Interfaces (BCI) for post-stroke hand motor rehabilitation requires the investigation of new electromyographic (EMG) features, potentially able to identify pathological synergies to be discouraged. Inter-muscular coherence (IMC) is gaining attention as a descriptor of the mechanisms behind abnormal motor control in stroke patients. With the ultimate goal to exploit IMC features to control BCIs, this work aims at (a) characterizing finger extension and grasping tasks by IMC features, (b) assessing IMC feature performance in classifying different conditions. Classification results (accuracy equal to 0.81 ± 0.19) pave the way for IMC feature application in hybrid BCI control.

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 by 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 signals recorded at rest from 71 healthy subjects (age: 20–63 years). 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%.

By focus group methodology, we examined the opinions and requirements of persons with ALS, their caregivers, and health care assistants with regard to developing a brain-computer interface (BCI) system that fulfills the user's needs. Four overarching topics emerged from this analysis: 1) lack of information on BCI and its everyday applications; 2) importance of a customizable system that supports individuals throughout the various stages of the disease; 3) relationship between affectivity and technology use; and 4) importance of individuals retaining a sense of agency. These findings should be considered when developing new assistive technology. Moreover, the BCI community should acknowledge the need to bridge experimental results and its everyday application.

In their early days, brain–computer interfaces (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. This article presents an hBCI framework, which was used in studies with nonimpaired as well as end users with motor impairments.

[Background] This paper presents the preliminary results of a novel rehabilitation therapy for cervical and trunk control of children with cerebral palsy (CP) based on serious videogames and physical exercise., [Materials] The therapy is based on the use of the ENLAZA Interface, a head mouse based on inertial technology that will be used to control a set of serious videogames with movements of the head., [Methods] Ten users with CP participated in the study. Whereas the control group (n = 5) followed traditional therapies, the experimental group (n = 5) complemented these therapies with a series of ten sessions of gaming with ENLAZA to exercise cervical flexion–extensions, rotations and inclinations in a controlled, engaging environment., [Results] The ten work sessions yielded improvements in head and trunk control that were higher in the experimental group for Visual Analogue Scale, Goal Attainment Scaling and Trunk Control Measurement Scale (TCMS). Significant differences (27% vs. 2% of percentage improvement) were found between the experimental and control groups for TCMS (p < 0.05). The kinematic assessment shows that there were some improvements in the active and the passive range of motion. However, no significant differences were found pre- and post-intervention., [Conclusions] Physical therapy that combines serious games with traditional rehabilitation could allow children with CP to achieve larger function improvements in the trunk and cervical regions. However, given the limited scope of this trial (n = 10) additional studies are needed to corroborate this hypothesis., The research work and publication costs were funded by NetMD (RTC-2015-3967-1), NeuroMOD (DPI2015-68664-C4-1-R), and InterAAC (RTC-2015-4327-1). All these projects were financed by the Spanish Ministry of Economy, Industry and Competitiveness.

[Background]: Spasticity is a motor disorder that is commonly treated manually by a physical therapist (PhT) stretching the muscles. Recent data on learning have demonstrated the importance of human-to-human interaction in improving rehabilitation: cooperative motor behavior engages specific areas of the motor system compared with execution of a task alone., [Objectives]: We hypothesize that PhT-guided therapy that involves active collaboration with the patient (Pt) through shared biomechanical visual biofeedback (vBFB) positively impacts learning and performance by the Pt during ankle spasticity treatment. A sensorized ankle foot orthosis (AFO) was developed to provide online quantitative data of joint range of motion (ROM), angular velocity, and electromyographic activity to the PhT and Pt during the treatment of ankle spasticity., [Methods]: Randomized controlled clinical trial. Ten subacute stroke inpatients, randomized into experimental (EXP) and control (CTRL) groups, underwent six weeks of daily treatment. The EXP group was treated with an active AFO, and the CTRL group was given an inactive AFO. Spasticity, ankle ROM, ankle active and passive joint speed, and coactivation index (CI) were assessed at enrollment and after 15–30 sessions., [Results]: Spasticity and CI (p < 0.005) decreased significantly after training only in the EXP group, in association with a significant rise in active joint speed and active ROM (p < 0.05). Improvements in spasticity (p < 0.05), active joint speed (p < 0.001), and CI (p < 0.001) after treatment differed between the EXP and CTRL groups., [Conclusions]: PhT–Pt sharing of exercise information, provided by joint sensorization and vBFB, improved the efficacy of the conventional approach for treating ankle spasticity in subacute stroke Pts.

ObjectiveIn this study a gaze independent event related potential (ERP)-based brain computer interface (BCI) for communication purpose was combined with an asynchronous classifier endowed with dynamical stopping feature. The aim was to evaluate if and how the performance of such asynchronous system could be negatively affected in terms of communication efficiency and robustness to false positives during the intentional no-control state. Material and methodsThe proposed system was validated with the participation of 9 healthy subjects. A comparison was performed between asynchronous and synchronous classification technique outputs while users were controlling the same gaze independent BCI interface. The performance of both classification techniques were assessed both off-line and on-line by means of the efficiency metric introduced by Bianchi et al. (2007). This latter metric allows to set a different misclassification cost for wrong classifications and abstentions. Robustness was evaluated as the rate of false positives occurring during voluntary no-control states. ResultsThe asynchronous classifier did not exhibited significantly higher accuracy or lower error rate with respect to the synchronous classifier (accuracy: 74.66% versus 87.96%, error rate: 7.11% versus 12.04% respectively). However, the on-line and off-line analysis revealed that the communication efficiency was significantly improved (p

In the present high-resolution electroencephalographic (EEG) study, event-related desynchronization/synchronization (ERD/ERS) of alpha rhythms was computed during an S1–S2 paradigm, in which a visual cue (S1) predicted a SHORT (600 ms) or LONG (1400 ms) foreperiod, preceding a visual go stimulus (S2) triggering right or left finger movement. Could orienting attention to a selective point in time influence the alpha rhythms as a function of the SHORT vs. LONG foreperiod? Stronger selective attentional modulations were predicted for the SHORT than LONG condition. EEG data from 54 channels were “depurated” from phase-locked visual evoked potentials and spatially enhanced by surface Laplacian estimation (i.e., final data analysis was conducted on 16 subjects having a sufficient number of artifact-free EEG single trials). Low-band alpha rhythms (about 6–10 Hz) were supposed to be related to anticipatory attentional processes, whereas high-band alpha rhythms (10–12 Hz) would indicate task-specific visuo-motor processes. Compared to the LONG condition (foreperiod), the SHORT condition induced a quicker and stronger ERS at low-band alpha rhythm (about 6–8 Hz) over midline and bilateral prefrontal, sensorimotor, and posterior parietal areas. In contrast, the concomitant high-band alpha (about 10–12 Hz) ERD/ERS showed no significant difference between the two conditions. In conclusion, temporal attention for a sub-second delay (800 ms) did modulate low-band alpha rhythm over large regions of both cortical hemispheres.

This paper presents the preliminary results of a novel rehabilitation therapy for cervical and trunk control of children with cerebral palsy (CP). The therapy is based on the use of an inertial sensor that will be used to control a set of serious videogames with movements of the head. Ten users with CP participated in the study, in the experimental and control groups. Ten sessions of therapy provided improvements in head and trunk control that were higher in the experimental group for Visual Analogue Scale (VAS), Goal Attainment Scaling (GAS) and Trunk Control Measurement Scale (TCMS). Significant differences (27% vs. 2% of percentage improvement) were found between the experimental and control groups for TCMS (p, ABC EU Project, CPWALKER Project, INTERPLAY Project and IVANPACE Project, which is funded by Obra Social de Caja Cantabria.

The P300 is a positive peak in EEG occurring after presentation of a target stimulus. For brain-computer interfaces (BCIs), eliciting P300s by tactile stimuli would have specific advantages; the display can be hidden under clothes and keeps the user’s gaze free. In addition, robust classification is especially important for BCIs. This motivated us to investigate P300s in response to tactile and visual stimuli unimodally and bimodally. Tactile stimuli were delivered by tactors around the participant’s waist. Visual stimuli were flashed circles on a monitor, schematically representing the tactors. Participants attended to the vibrations and/or flashes of a “target” presented in a stream of standards. The P300 amplitude for the different modalities was comparable in size and depended on electrode location. Classification accuracy was highest in the bimodal condition. We conclude that bimodal stimuli could enhance classification results within a BCI context compared to unimodal presentations.

To evaluate the influence of Motor Imagery (MI) training assisted via Brain Computer Interface (BCI) on brain network organization in subacute stroke patients. We analyzed EEG-derived brain networks estimated before and after two training strategies (with and without BCI support); correlations between connectivity indices and clinical improvement were performed. Twenty-eight subacute stroke patients were enrolled and assigned to two groups: 14 patients underwent a one-month motor imagery (MI) training supported by a sensorimotor–based BCI (BCI group) while 14 underwent a similar MI training without BCI support (CTRL group). Before and after training we recorded EEG from 61 positions during 5 min of rest. Effective connectivity was computed by means of Partial Directed Coherence. Paired sample t -tests and Pearson’s Correlation were employed to analyze data (significance was defined by p p R = 0.568, p = 0.034). Overall findings indicate that MI training supported via BCI induces a reinforcement of interhemispheric connections related to sensorimotor rhythms; brain connectivity is a promising neurophysiological marker for BCI training efficacy in stroke rehabilitation. This work was partially supported by the European ICT Programme Project FP7-224631 and by the project “Brain Computer Interface-Driven Rehabilitation After Stroke: An Add-On Intervention For Hand Motor Recovery” (RF-2010-2319611) founded by the Italian Ministry of Healthcare.

Objective To evaluate the feasibility of brain-computer interface (BCI)-assisted motor imagery training to support hand/arm motor rehabilitation after stroke during hospitalization. Design Proof-of-principle study. Setting Neurorehabilitation hospital. Participants Convenience sample of patients (N=8) with new-onset arm plegia or paresis caused by unilateral stroke. Interventions The BCI-based intervention was administered as an "add-on" to usual care and lasted 4 weeks. Under the supervision of a therapist, patients were asked to practice motor imagery of their affected hand and received as a discrete feedback the movements of a "virtual" hand superimposed on their own. Such a BCI-based device was installed in a rehabilitation hospital ward. Main Outcome Measures Following a user-centered design, we assessed system usability in terms of motivation, satisfaction (by means of visual analog scales), and workload (National Aeronautics and Space Administration–Task Load Index). The usability of the BCI-based system was also evaluated by 15 therapists who participated in a focus group. Results All patients successfully accomplished the BCI training. Significant positive correlations were found between satisfaction and motivation ( P =.001, r=.393). BCI performance correlated with interest ( P =.027, r=.257) and motivation ( P =.012, r=.289). During the focus group, professionals positively acknowledged the opportunity offered by BCI-assisted training to measure patients' adherence to rehabilitation. Conclusions An ecological BCI-based device to assist motor imagery practice was found to be feasible as an add-on intervention and tolerable by patients who were exposed to the system in the rehabilitation environment.

Objective To evaluate the impact of a hybrid control on usability of a P300-based brain-computer interface (BCI) system that was designed to control an assistive technology software and was integrated with an electromyographic channel for error correction. Design Proof-of-principle study with a convenience sample. Setting Neurologic rehabilitation hospital. Participants Participants (N=11) in this pilot study included healthy (n=8) and severely motor impaired (n=3) persons. The 3 people with severe motor disability were identified as potential candidates to benefit from the proposed hybrid BCI system for communication and environmental interaction. Interventions To eventually investigate the improvement in usability, we compared 2 modalities of BCI system control: a P300-based and a hybrid P300 electromyographic-based mode of control. Main Outcome Measures System usability was evaluated according to the following outcome measures within 3 domains: (1) effectiveness (overall system accuracy and P300-based BCI accuracy); (2) efficiency (throughput time and users' workload); and (3) satisfaction (users' satisfaction). We also considered the information transfer rate and time for selection. Results Findings obtained in healthy participants were in favor of a higher usability of the hybrid control as compared with the nonhybrid. A similar trend was indicated by the observational results gathered from each of the 3 potential end-users. Conclusions The proposed hybrid BCI control modality could provide end-users with severe motor disability with an option to exploit some residual muscular activity, which could not be fully reliable for properly controlling an assistive technology device. The findings reported in this pilot study encourage the implementation of a clinical trial involving a large cohort of end-users.

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.

We explored the cortical dynamics during movements of an unaffected body part in tetraplegic subjects with chronic spinal cord injury (SCI). The aims were to find out whether the intact movements were associated with a physiological time‐varying pattern of activity in the motor‐related cortical areas and whether the primary motor area (MI) activation followed a somatotopic distribution. Event‐related potentials to self‐initiated lip movements were analyzed by means of cortical source imaging of EEG recorded from seven tetraplegic subjects and seven control subjects. Regions of interest (ROIs) were selected on individual MRI and the time‐varying electrophysiologic activity (cortical current density, CCD) was estimated on these ROIs and subjected to across‐subject analysis. A significant, bilateral movement‐related pattern of MI activation was detected during motor task execution in SCI patients as well as in controls. The site of local maxima activation displayed a symmetrical discrete distribution within MI, consistently with a putative somatotopic lip representation, in all the subjects. The supplementary motor area proper (SMAp) was always coactivated with MI and coactivation was characterized by a time course with typical premotion and motion phases over both motor areas. A clear‐cut temporal delay between the SMAp and MI activation did not occur either in SCI patients or in controls. These findings obtained with noninvasive neuroelectrical source imaging document that in chronic SCI subjects “executive” motor areas are engaged with a preserved temporal and spatial pattern during preparation and execution of intact movements. Hum Brain Mapp, 2005. © 2005 Wiley‐Liss, Inc.

Today, the concept of brain connectivity plays a central role in the neuroscience. While functional connectivity is defined as the temporal coherence between the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally between cortical sites. The most used method to estimate effective connectivity in neuroscience is the structural equation modeling (SEM), typically used on data related to the brain hemodynamic behavior. However, the use of hemodynamic measures limits the temporal resolution on which the brain process can be followed. The present research proposes the use of the SEM approach on the cortical waveforms estimated from the high-resolution EEG data, which exhibits a good spatial resolution and a higher temporal resolution than hemodynamic measures. We performed a simulation study, in which different main factors were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). Such factors were the signal-to-noise ratio and the duration of the simulated cortical activity. Since SEM technique is based on the use of a model formulated on the basis of anatomical and physiological constraints, different experimental conditions were analyzed, in order to evaluate the effect of errors made in the a priori model formulation on its performances. The feasibility of the proposed approach has been shown in a human study using high-resolution EEG recordings related to finger tapping movements.

The aim of this study is to present a package including standard software for the electroencephalographic (EEG), electro-oculographic (EOG) and electromyographic (EMG) preliminary data analysis, which may be suitable to standardize the results of many EEG research centers studies (i.e. multi-centric studies) especially focused on event-related potentials. In particular, our software package includes (semi)automatic procedures for (i) EOG artifact detection and correction, (ii) EMG analysis, (iii) EEG artifact analysis, (iv) optimization of the ratio between artifact-free EEG channels and trials to be rejected. The performances of the software package on EOG-EEG-EMG data related to cognitive-motor tasks were evaluated with respect to the preliminary data analysis performed by two expert electroencephalographists (gold standard). Due to its extreme importance for multi-centric EEG studies, we compared the performances of two representative "regression" methods for the EOG correction in time and frequency domains. The aim was the selection of the most suitable method in the perspective of a multi-centric EEG study. The results showed an acceptable agreement of approximately 95% between the human and software behaviors, for the detection of vertical and horizontal EOG artifacts, the measurement of hand EMG responses for a cognitive-motor paradigm, the detection of involuntary mirror movements, and the detection of EEG artifacts. Furthermore, our results indicated a particular reliability of a 'regression' EOG correction method operating in time domain (i.e. ordinary least squares). These results suggest that such a software package could be used for multi-centric EEG studies.

Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

A method for the modeling of human movement‐related cortical activity from combined electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes a subject's multi‐compartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multi‐dipole source model, and a regularized linear inverse source estimate based on boundary element mathematics. Linear inverse source estimates of cortical activity were regularized by taking into account the covariance of background EG and MEG sensor noise. EEG (121 sensors) and MEG (43 sensors) data were recorded in separate sessions whereas normal subjects executed voluntary right one‐digit movements. Linear inverse source solution of EEG, MEG, and EEG‐MEG data were quantitatively evaluated by using three performance indexes. The first two indexes (Dipole Localization Error [DLE] and Spatial Dispersion [SDis]) were used to compute the localization power for the source solutions obtained. Such indexes were based on the information provided by the column of the resolution matrix (i.e., impulse response). Ideal DLE values tend to zero (the source current was correctly retrieved by the procedure). In contrast, high DLE values suggest severe mislocalization in the source reconstruction. A high value of SDis at a source space point mean that such a source will be retrieved by a large area with the linear inverse source estimation. The remaining performance index assessed the quality of the source solution based on the information provided by the rows of the resolution matrix R, i.e., resolution kernels. The i‐th resolution kernels of the matrix R describe how the estimation of the i‐th source is distorted by the concomitant activity of all other sources. A statistically significant lower dipole localization error was observed and lower spatial dispersion in source solutions produced by combined EEG‐MEG data than from EEG and MEG data considered separately (P < 0.05). These effects were not due to an increased number of sensors in the combined EEG‐MEG solutions. They result from the independence of source information conveyed by the multimodal measurements. From a physiological point of view, the linear inverse source solution of EEG‐MEG data suggested a contralaterally preponderant bilateral activation of primary sensorimotor cortex from the preparation to the execution of the movement. This activation was associated with that of the supplementary motor area. The activation of bilateral primary sensorimotor cortical areas was greater during the processing of afferent information related to the ongoing movement than in the preparation for the motor act. In conclusion, the linear inverse source estimate of combined MEG and EEG data improves the estimate of movement‐related cortical activity. Hum. Brain Mapping 14:197–209, 2001. © 2001 Wiley‐Liss, Inc.

In recent years, network analyses have been used to evaluate brain reorganization following stroke. However, many studies have often focused on single topological scales, leading to an incomplete model of how focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. In an EEG-based experiment on the performance of hand motor imagery (MI) in 20 patients with unilateral stroke, we observed that the anatomic lesion affects the functional brain network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of the affected hand (Ahand) elicited a significantly lower smallworldness and local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the abnormal reduction in Eloc significantly depended on the increase in interhemispheric connectivity, which was in turn determined primarily by the rise in regional connectivity in the parieto-occipital sites of the affected hemisphere. Further, in contrast to the Uhand MI, in which significantly high connectivity was observed for the contralateral sensorimotor regions of the unaffected hemisphere, the regions that increased in connection during the Ahand MI lay in the frontal and parietal regions of the contralaterally affected hemisphere. Finally, the overall sensorimotor function of our patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly predicted by the connectivity of their affected hemisphere. These results increase our understanding of stroke-induced alterations in functional brain networks., Neuroimage, accepted manuscript (unedited version) available online 19-June-2013

Mobile computing, coupled with advanced types of input interfaces, such as Brain Computer Interfaces (BCIs), and smart spaces can improve the quality of life of persons with disabilities. In this paper, we describe the architecture and the prototype of an assistive system, which allows users to express themselves and partially preserve their independence in controlling electrical devices at home. Even in absence of muscular functions, the proposed system would still allow the user some communication and control capabilities, by relying on non-invasive BCIs. Experiments show how the fully-software realization of the system guarantees effective use with BCIs. © 2013 Springer-Verlag.

Mobile computing, coupled with advanced types of input interfaces, such as Brain Computer Interfaces (BCIs), can improve the quality of life of persons with dierent disabilities. In this paper we describe the architecture and the prototype of an assistive system, which allows users to express themselves and partially preserve their independence in controlling electrical devices at home. Even in absence of muscular functions, the proposed system would still allows the user some communication and control capabilities, by relaying on non-invasive BCIs. Experiments show how the fully-software realization of the system guarantees effective use with BCIs.

Advances in neuroelectric recordings and computational tools allow investigation of interactive brain activity and connectivity in a group of subjects engaged in social interactions.

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.

This special issue is devoted to ‘‘Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing.” This peer-reviewed special issue is one product of the International Conference on Bioinspired systems and Signal Processing (Biosignals 2011), as part of the International Joint Conference on Biomedical Engineering Systems and Technologies, Biostec (http://www.biosignals.biostec.org/BIOSIGNALS2011/home.asp), which was held in Rome, during 26–29 January 2011. This issue was promoted by the IEEE Technical Committee of Biomedical Signal Processing (IEE TC-BSP). The goal of this special issue is to convey mainly several messages which arose from this prestigious and intense conference. The major focus in this meeting was biomedical signal processing, where several areas of interest to the researchers in this field were comprehensively covered. This issue is therefore to report some highlighted contributions by a number of prominent researchers in the related field. Interested readers will find in this special issue papers that belong broadly to the areas of neuroscience, cardiovascular signals, behaviour, body movement, and modelling. These areas have been spanned by a number of articles in computational modelling and intelligence, neuroimaging, neuromarketing, body movement and gait analysis, brain-computer interfacing, electrocardiography and analysis of cardiovascular signals, neurocomputing and retinal image matching technique. What follows is a brief editorial review of the topics covered by the published papers in this special issue.

Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication & Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

Background Sequel to the work on the quantization of nonconservative systems using fractional calculus and quantization of a system with Brownian motion, which aims to consider the dissipation effects in quantum-mechanical description of microscale systems. Results The canonical quantization of a system represented classically by one-dimensional Fick's law, and the diffusion equation is carried out according to the Dirac method. A suitable Lagrangian, and Hamiltonian, describing the diffusive system, are constructed and the Hamiltonian is transformed to Schrodinger's equation which is solved. An application regarding implementation of the developed mathematical method to the analysis of diffusion, osmosis, which is a biological application of the diffusion process, is carried out. Schrödinger's equation is solved. Conclusions The plot of the probability function represents clearly the dissipative and drift forces and hence the osmosis, which agrees totally with the macro-scale view, or the classical-version osmosis.

Objective: In this paper, we propose a body of techniques for the estimation of rapidly changing connectivity relationships between EEG signals estimated in cortical areas, based on the use of adaptive multivariate autoregressive modeling (AMVAR) for the estimation of a time-varying partial directed coherence (PDC). This approach allows the observation of rapidly changing influences between the cortical areas during the execution of a task, and does not require the stationarity of the signals. Methods: High resolution EEG data were recorded from a group of spinal cord injured (SCI) patients during the attempt to move a paralyzed limb. These data were compared with the time-varying connectivity patterns estimated in a control group during the real execution of the movement. Connectivity was estimated with the use of realistic head modeling and the linear inverse estimation of the cortical activity in a series of regions of interest by using time-varying PDC. Results: The SCI population involved a different cortical network than those generated by the healthy subjects during the task performance. Such a network differs for the involvement of the parietal cortices, which increases in strength near to the movement imagination onset for the SCI when compared to the normal population. Conclusions: The application of time-varying PDC allows tracking the evolution of the connectivity between cortical areas in the analyzed populations during the proposed tasks. Such details about the temporal evolution of the connectivity patterns estimated cannot be obtained with the application of the standard estimators of connectivity.

The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs). In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and “supporter” voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.