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Cortico-muscular coupling (CMC) could be used as potential input of a novel hybrid Brain-Computer Interface (hBCI) for motor re-learning after stroke. Here, we aim of addressing the design of a hBCI able to classify different movement tasks taking into account the interplay between the cerebral and residual or recovered muscular activity involved in a given movement. Hence, we compared the performances of four classification methods based on CMC features to evaluate their ability in discriminating finger extension from grasping movements executed by 17 healthy subjects. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performances. Results showed that, regardless of the model, few CMC features (up to 10) allow for a successful classification of two different movements type. Moreover, support vector machine classifier with linear kernel showed the best trade-off between performances and system usability (few electrodes). Thus, these results suggest that a hBCI based on brain-muscular interplay holds the potential to enable more informed neural plasticity and functional motor recovery after stroke. Furthermore, this CMC-based BCI could also allow for a more "natural control" (l.e., that resembling physiological control) of prosthetic devices.

Low Frequency Brain Oscillations (LFOs) are brief periods of oscillatory activity in delta and lower theta band that appear at motor cortical areas before and around movement onset. It has been shown that LFO power decreases in post-stroke patients and re-emerges with motor functional recovery. To date, LFOs have not yet been explored during the motor execution (ME) and imagination (MI) of simple hand movements, often used in BCI-supported motor rehabilitation protocols post-stroke. This study aims at analyzing the LFOs during the ME and MI of the finger extension task in a sample of 10 healthy subjects and 2 stroke patients in subacute phase. The results showed that LFO power peaks occur in the preparatory phase of both ME and MI tasks on the sensorimotor channels in healthy subjects and their alterations in stroke patients. Clinical Relevance- Results suggest that LFOs could be explored as biomarker of the motor function recovery in rehabilitative protocols based on the movement imagination.

Stroke survivors experience muscular pattern alterations of the upper limb that decrease their ability to perform daily-living activities. The Box and Block test (BBT) is widely used to assess the unilateral manual dexterity. Although BBT provides insights into functional performance, it returns limited information about the mechanisms contributing to the impaired movement. This study aims at exploring the BBT by means of muscle synergies analysis during the execution of BBT in a sample of 12 healthy participants with their dominant and non-dominant upper limb. Results revealed that: (i) the BBT can be described by 1 or 2 synergies; the number of synergies (ii) does not differ between dominant and non-dominant sides and (iii) varies considering each phase of the task; (iv) the transfer phase requires more synergies. Clinical Relevance- This preliminary study characterizes muscular synergies during the BBT task in order to establish normative patterns that could assist in understanding the neuromuscular demands and support future evaluations of stroke deficits.

Brain-computer interfaces have increasingly found applications in motor function recovery in stroke patients. In this context, it has been demonstrated that associative-BCI protocols, implemented by means the movement related cortical potentials (MRCPs), induce significant cortical plasticity. To date, no methods have been proposed to deal with brain signal (i.e. MRCP feature) non-stationarity. This study introduces adaptive learning methods in MRCP detection and aims at comparing a no-adaptive approach based on the Locality Sensitive Discriminant Analysis (LSDA) with three LSDA-based adaptive approaches. As a proof of concept, EEG and force data were collected from six healthy subjects while performing isometric ankle dorsiflexion. Results revealed that adaptive algorithms increase the number of true detections and decrease the number of false positives per minute. Moreover, the markedly reduction of BCI system calibration time suggests that these methods have the potential to improve the usability of associative-BCI in post-stroke motor recovery.

Transcranial cerebellar direct current stimulation (tcDCS) can offer new insights into the cerebellar function and disorders, by modulating noninvasively the activity of cerebellar networks. Taking into account the functional interplay between the cerebellum and the cerebral cortex, we addressed the effects of unilateral tcDCS (active electrode positioned over the right cerebellar hemisphere) on the electroencephalographic (EEG) oscillatory activity and on the cortical network organization at resting state. Effects on spectral (de)synchronizations and functional connectivity after anodal and cathodal stimulation were assessed with respect to a sham condition. A lateralized synchronization over the sensorimotor area in gamma band, as well as an increase of the network segregation in sensory-motor rhythms and a higher communication between hemispheres in gamma band, were detected after anodal stimulation. The same measures after cathodal tcDCS returned responses similar to the sham condition.

Community structure is a feature of complex networks that can be crucial for the understanding of their internal organization. This is particularly true for brain networks, as the brain functioning is thought to be based on a modular organization. In the last decades, many clustering algorithms were developed with the aim to identify communities in networks of different nature. However, there is still no agreement about which one is the most reliable, and to test and compare these algorithms under a variety of conditions would be beneficial to potential users. In this study, we performed a comparative analysis between six different clustering algorithms, analyzing their performances on a ground-truth consisting of simulated networks with properties spanning a wide range of conditions. Results show the effect of factors like the noise level, the number of clusters, the network dimension and density on the performances of the algorithms and provide some guidelines about the use of the more appropriate algorithm according to the different conditions. The best performances under a wide range of conditions were obtained by Louvain and Leicht & Newman algorithms, while Ronhovde and Infomap proved to be more appropriate in very noisy conditions. Finally, as a proof of concept, we applied the algorithms under exam to brain functional connectivity networks obtained from EEG signals recorded during a sustained movement of the right hand, obtaining a clustering of scalp electrodes which agrees with the results of the simulation study conducted.

Partial Directed Coherence (PDC) is a powerful estimator of effective connectivity. In neuroscience it is used in different applications with the aim to investigate the communication between brain regions during the execution of different motor or cognitive tasks. When multiple trials are available, PDC can be computed over multiple realizations, provided that the assumption of stationarity across trials is verified. This allows to improve the amount of data, which is an important constraint for the estimation accuracy. However, the stationarity of the data across trials is not always guaranteed, especially when dealing with patients. In this study we investigated how the inter-trials variability of an EEG dataset affects the PDC accuracy. Effects of density variations and of changes of connectivity values across trials were first investigated with a simulation study and then tested on real EEG data collected from two post-stroke patients during a motor imagery task and characterized by different inter-trials variability. Results showed the effect of different factors on the PDC accuracy and the robustness of such estimator in a range of conditions met in practical applications.

In this study the P300 latency jitter has been explored in an EEG data set collected from a group of patients with disorders of consciousness (DOC; n=13) that was administered with an auditory Oddball paradigm under passive and active conditions. A method based on wavelet transform was applied to estimate single trial P300 waveforms. Preliminary results showed that 5 Vegetative State (VS) and 8 Minimally Conscious Staten (MCS) patients exhibited significantly higher values of P300 latency jitter as compared to those obtained from a control group of 12 healthy subjects. In addition, the magnitude of the P300 latency jitter negatively correlated with patients' clinical status. The existence of such phenomenon might substantially limit an effective use of Brain Computer Interface systems for communication.

In this study, a framework able to classify online different levels of mental workload induced during a simulated flight by using the combination of the Electroencephalogram (EEG) and the Heart Rate (HR) biosignals has been proposed. Ten healthy subjects were involved in the experimental protocol, performing the NASA - Multi Attribute Task Battery (MATB) over three different difficulty levels in order to simulate three classic showcases in a flight scene (cruise flight phase, flight level maintaining, and emergencies). The analyses showed that the proposed system is able to estimate online the mental workload of the subjects over the three different conditions reaching a high discriminability (p<.05). In addition, it has been found that the classification parameters remained stable within a week, without recalibrating the system with new parameters.

Methods based on the multivariate autoregressive (MVAR) approach are commonly used for effective connectivity estimation as they allow to include all available sources into a unique model. To ensure high levels of accuracy for high model dimensions, all the observations are used to provide a unique estimation of the model, and thus of the network and its properties. The unavailability of a distribution of connectivity values for a single experimental condition prevents to perform statistical comparisons between different conditions at a single subject level. This is a major limitation, especially when dealing with the heterogeneity of clinical conditions presented by patients. In the present paper we proposed a novel approach to the construction of a distribution of connectivity in a single subject case. The proposed approach is based on small perturbations of the networks properties and allows to assess significant changes in brain connectivity indexes derived from graph theory. Its feasibility and applicability were investigated by means of a simulation study and an application to real EEG data.

One of the main limitations commonly encountered when dealing with the estimation of brain connectivity is the difficulty to perform a statistical assessment of significant changes in brain networks at a single-subject level. This is mainly due to the lack of information about the distribution of the connectivity estimators at different conditions. While group analysis is commonly adopted to perform a statistical comparison between conditions, it may impose major limitations when dealing with the heterogeneity expressed by a given clinical condition in patients. This holds true particularly for stroke when seeking for quantitative measurements of the efficacy of any rehabilitative intervention promoting recovery of function. The need is then evident of an assessment which may account for individual pathological network configuration associated with different level of patients' response to treatment; such network configuration is highly related to the effect that a given brain lesion has on neural networks. In this study we propose a resampling-based approach to the assessment of statistically significant changes in cortical connectivity networks at a single subject level. First, we provide the results of a simulation study testing the performances of the proposed approach under different conditions. Then, to show the sensitivity of the method, we describe its application to electroencephalographic (EEG) data recorded from two post-stroke patients who showed different clinical recovery after a rehabilitative intervention.

In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.

In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.

Partial Directed Coherence (PDC) is a spectral multivariate estimator for effective connectivity, relying on the concept of Granger causality. Even if its original definition derived directly from information theory, two modifies were introduced in order to provide better physiological interpretations of the estimated networks: i) normalization of the estimator according to rows, ii) squared transformation. In the present paper we investigated the effect of PDC normalization on the performances achieved by applying the statistical validation process on investigated connectivity patterns under different conditions of Signal to Noise ratio (SNR) and amount of data available for the analysis. Results of the statistical analysis revealed an effect of PDC normalization only on the percentages of type I and type II errors occurred by using Shuffling procedure for the assessment of connectivity patterns. No effects of the PDC formulation resulted on the performances achieved during the validation process executed instead by means of Asymptotic Statistic approach. Moreover, the percentages of both false positives and false negatives committed by Asymptotic Statistic are always lower than those achieved by Shuffling procedure for each type of normalization.

Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population. In this paper we proposed a new approach for adjacency matrix extraction based on a statistical threshold as valid alternative to empirical approaches, extensively used in Neuroscience field (i.e. fixing the edge density). In particular we performed a simulation study for investigating the effects of the two different extraction approaches on the topological properties of the investigated networks. In particular, the comparison was performed on two different datasets, one composed by uncorrelated random signals (null-model) and the other one by signals acquired on a mannequin head used as a phantom (EEG null-model). The results highlighted the importance to use a statistical threshold for the adjacency matrix extraction in order to describe the real existing topological properties of the investigated networks. The use of an empirical threshold led to an erroneous definition of small-world properties for the considered connectivity patterns.

The aim of the study is to analyze the variation of the EEG power spectra in theta band when a novice starts to learn a new task. In particular, the goal is to find out the differences from the beginning of the training to the session in which the performance level is good enough for considering him/her able to complete the task without any problems. While the novices were engaged in the flight simulation tasks we recorded the brain activity by using high resolution EEG techniques as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). Results show clear changes in the EEG power spectra in theta band over the frontal brain areas, either over the left, the midline and the right side, during the learning process of the task. These results are also supported by the autonomic signals of HR and EBR, by the performances' trends and by the questionnaires for the evaluation of the perceived workload level.

Recent studies have investigated changes in the human brain network organization during the normal aging. A reduction of the connectivity between brain areas was demonstrated by combining neuroimaging technologies and graph theory. Clustering, characteristic path length and small-worldness are key topological measures and they are widely used in literature. In this paper we propose a new methodology that combine advanced techniques of effective connectivity estimation, graph theoretical approach and classification by SVM method. EEG signals recording during rest condition from 20 young subjects and 20 mid-aged adults were studied. Partial Directed Coherence was computed by means of General Linear Kalman Filter and graph indexes were extracted from estimated patterns. At last small-worldness was used as feature for the SVM classifier. Results show that topological differences of brain networks exist between young and mid-aged adults: small-worldness is significantly different between the two populations and it can be used to classify the subjects with respect to age with an accuracy of 69%.

Controlling an aircraft during a flight is a compelling condition, which requires a strict and well coded interaction between the crew. The interaction level between the Captain and the First Officer changes during the flight, ranging from a maximum (during takeoff and landing, as well as in case of a failure of the instrumentation or other emergency situations) to a minimum during quiet mid-flight. In this study, our aim is to investigate the neural correlates of different kinds and levels of interaction between couples of professional crew members by means of the innovative technique called brain hyperscanning, i.e. the simultaneous recording of the hemodynamic or neuroelectrical activity of different human subjects involved in interaction tasks. This approach allows the observation and modeling of the neural signature specifically dependent on the interaction between subjects, and, even more interestingly, of the functional links existing between the brain activities of the subjects interacting together. In this EEG hyperscanning study, different phases of a flight were reproduced in a professional flight simulator, which allowed, on one side, to reproduce the ecological setting of a real flight, and, on the other, to keep under control the different levels of interaction induced in the crew by means of systematic and simulated failures of the aircraft instrumentation. Results of the procedure of linear inverse estimation, together with functional hyperconnectivity estimated by means of Partial Directed Coherence, showed a dense network of connections between the activity in the two brains in the takeoff and landing phases, when the cooperation between the crew is maximal, while conversely no significant links were shown during the phases in which the activity of the two pilots was independent.

Brain-Computer Interfaces (BCIs) process brain activity in real time, and mediate non-muscular interaction between and individual and the environment. The subserving algorithms can be used to provide a quantitative measurement of physiological or pathological cognitive processes - such as Motor Imagery (MI) - and feed it back the user. In this paper we propose the clinical application of a BCI-based rehabilitation device, to promote motor recovery after stroke. The BCI-based device and the therapy exploiting its use follow the same principles that drive classical neuromotor rehabilitation, and (i) provides the physical therapist with a monitoring instrument, to assess the patient's participation in the rehabilitative cognitive exercise; (ii) assists the patient in the practice of MI. The device was installed in the ward of a rehabilitation hospital and a group of 29 patients were involved in its testing. Among them, eight have already undergone a one-month training with the device, as an add-on to the regular therapy. An improved system, which includes analysis of Electromyographic (EMG) patterns and Functional Electrical Stimulation (FES) of the arm muscles, is also under clinical evaluation. We found that the rehabilitation exercise based on BCI-mediated neurofeedback mechanisms enables a better engagement of motor areas with respect to motor imagery alone and thus it can promote neuroplasticity in brain regions affected by a cerebrovascular accident. Preliminary results also suggest that the functional outcome of motor rehabilitation may be improved by the use of the proposed device.

The "Default Mode Network" concept was defined, in fMRI field, as a consistent pattern, involving some regions of the brain, which is active during resting state activity and deactivates during attention demanding or goal-directed tasks. Several fMRI studies described its features also correlating the deactivations with the attentive load required for the task execution. Despite the efforts in EEG field, aiming at correlating the spectral features of EEG signals with DMN, an electrophysiological correlate of the DMN hasn't yet been found. In this study we used advanced techniques for functional connectivity estimation for describing the neuroelectrical properties of DMN. We analyzed the connectivity patterns elicited during the rest condition by 55 healthy subjects by means of Partial Directed Coherence. We extracted some graph indexes in order to describe the properties of the resting network in terms of local and global efficiencies, symmetries and influences between different regions of the scalp. Results highlighted the presence of a consistent network, elicited by more than 70% of analyzed population, involving mainly frontal and parietal regions. The properties of the resting network are uniform among the population and could be used for the construction of a normative database for the identification of pathological conditions.

In order to improve Brain Computer Interface usability for real life context, they should be able to adapt their speed to the user's current psychophysical state and to understand from the ongoing EEG when he/she intends to suspend the control. In this work we evaluated an asynchronous classifier which provides these feature with 20 healthy subjects, who were engaged in an environmental control task or in a spelling task. We also demonstrated how the proposed classifier can improve communication efficiency with respect to classical synchronous classifiers.

A home is smart when, being aware of its own state and that of its users, is capable of controlling itself in order to support the user wishes and thus improving their quality of life. This holds both for users with special needs and for those with ordinary domestic needs. In this paper, we overview the Smart Homes for All project which represents the current state of the art with respect to software control and user interfaces in the smart homes arena.

Partial Directed Coherence (PDC) is a powerful tool to estimate a frequency domain description of Granger causality between multivariate time series. One of the main limitation of this estimator, however, has been so far the criteria used to assess the statistical significance, which have been obtained through surrogate data approach or arbitrarily imposed thresholds. The aim of this work is to test the performances of a validation approach based on the rigorous asymptotic distributions of PDC, recently proposed in literature. The performances of this method, defined in terms of percentages of false positives and false negatives, were evaluated by means of a simulation study taking into account factors like the Signal to Noise Ratio (SNR) and the amount of data available for the estimation and the use of different methods for the statistical corrections for multiple comparisons. Results of the Analysis Of Variance (ANOVA) performed on false positives and false negatives revealed a strong dependency of the performances from all the factors investigated. In particular, results indicate an amount of Type I errors below 7% for all conditions, while Type II errors are below 10% when the SNR is at least 1, the data length of at least 50 seconds and the appropriate correction for multiple comparisons is applied.

Brain Hyperscanning, i.e. the simultaneous recording of the cerebral activity of different human subjects involved in interaction tasks, is a very recent field of Neuroscience aiming at understanding the cerebral processes generating and generated by social interactions. This approach allows the observation and modeling of the neural signature specifically dependent on the interaction between subjects, and, even more interestingly, of the functional links existing between the activities in the brains of the subjects interacting together. In this EEG hyperscanning study we explored the functional hyperconnectivity between the activity in different scalp sites of couples of Civil Aviation Pilots during different phases of a flight reproduced in a flight simulator. Results shown a dense network of connections between the two brains in the takeoff and landing phases, when the cooperation between them is maximal, in contrast with phases during which the activity of the two pilots was independent, when no or quite few links were shown. These results confirms that the study of the brain connectivity between the activity simultaneously acquired in human brains during interaction tasks can provide important information about the neural basis of the "spirit of the group".

The aim of the present research is to investigate the EEG activity elicited by a fast observation of face of real politicians during a simulated political election. Politician's face are taken from real local election performed in Italy in the 2004 and 2008. We recorded the EEG activity of eight healthy subjects while they are asked to give a judgment on dominance, trustworthiness traits and a preference of vote on faces shown. Statistical differences of spectral EEG scalp activity have been mapped onto a realistic head model. For each experimental condition, we employed the t-test to compare the PSD values and adopted the False Discovery Rate correction for multiple comparisons. The scalp statistical maps revealed a desynchronization in the alpha band related to the politicians who lost the simulated elections and have been judged less trustworthy. Although these results might be congruent with the recent literature, the present is the first EEG study about and there is the need to extend the paradigm and the analysis on a larger number of subjects to validate these results.

In this paper we show how the possibility of recording simultaneously the cerebral neuroelectric activity in different subjects (EEG hyperscanning) during the execution of different tasks could return useful information about the "internal" cerebral state of the subjects. We present the results obtained by EEG hyperscannings during ecological task (such as the execution of a card game) as well as that obtained in a series of couples of subjects during the performance of the Prisoner's Dilemma Game. The simultaneous recordings of couples of interacting subjects allows to observe and to model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. Results obtained in a study of different groups recorded during the card game revealed a larger activity in prefrontal and anterior cingulated cortex in different frequency bands for the player that leads the game when compared to other players. Results collected in a population of 10 subjects during the performance of the Prisoner's Dilemma suggested that the most consistently activated structure is the orbitofrontal region (roughly described by the Brodmann area 10) during the condition of competition in both the tasks. It could be speculated whether the pattern of cortical connectivity between different cortical areas in different subjects could be employed as a tool for assessing the outcome of the task in advance.

In this study we measured simultaneously by EEG hyperscannings the neuroelectric activity in 6 couples of subjects during the performance of the "Chicken's game", derived from game theory. The simultaneous recording of the EEG in couples of interacting subjects allows to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. Results suggested that the one of the most consistently activated structure in this particular social interaction paradigm is the left orbitofrontal cortex. Connectivity results also showed a significant involvement of the orbitofrontal regions of both hemispheres across the observed population. Taken together, results confirms that the study of the brain activities in humans during social interactions can take benefit from the simultaneous acquisition of brain activity during such interaction.

The evaluation of the topological properties of brain networks is an emergent research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral "marker" for motor actions that result in successful reaching of a target.

In the present study, we propose a theoretical graph procedure to investigate the communication redundancy in brain networks. By taking into account all the possible paths between pairs of cortical regions, this method captures the network redundancy i.e. a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. As an example for its potential, we apply this procedure to the cortical networks estimated from high-resolution EEG signals in a group of spinal cord injured patients during the attempt of the foot movement. Preliminary results suggest that in the high spectral contents the effects due to the spinal trauma affect the expected redundancy attitude by suppressing mainly the longer alternative pathways between the cortical regions.

In this paper we illustrate the capability of tracking brain activity during the observation of commercial TV spots by using advanced high resolution EEG statistical techniques in time and frequency domains. In particular, 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 part 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 have been proved able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.

One of the most challenging questions open in Neuroscience today is the characterization of the brain responses during social interaction. A major limitation of the approaches used in most of the studies performed so far is that only one of the participating brains is measured each time. The "interaction" between cooperating, competing or communicating brains is thus not measured directly, but inferred by independent observations aggregated by cognitive models and assumptions that link behavior and neural activation. In this paper, we present the results of the simultaneous neuroelectric recording of 5 couples of subjects engaged in cooperative games (EEG hyperscanning). The simultaneous recordings of couples of interacting subjects allows to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. We used a paradigm called Prisoner's dilemma derived from the game theory. Results collected in a population of 10 subjects suggested that the most consistently activated structure in social interaction paradigms is the orbitofrontal region (roughly described by the Brodmann area 10) during the condition of competition.

In this study we estimated time-varying cortical connectivity patterns 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 by using time-varying Partial Directed Coherence. Connectivity was estimated from high resolution EEG recordings with the use of realistic head modelling and the linear inverse estimation of the cortical activity in a series of Regions of Interest of the cortex (ROIs). The experimental evidences obtained support the conclusion that the SCI population involved a larger cortical network than those generated by the healthy subjects during the task performance. Such 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. Such details about the temporal evolution of the connectivity patterns cannot be obtained with the application of the standard estimators of connectivity.

In this study we estimated time-varying cortical connectivity patterns from a group of Spinal Cord Injured (SCI) patients during the attempt to move a paralyzed limb. This data were compared with the time-varying connectivity patterns estimated in a control group during the effective execution of the movement. Connectivity was estimated from high resolution EEG recordings with the use of realistic head modelling and the linear inverse estimation of the cortical activity. Time-varying PDC was obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Such estimator is able to follow rapid changes in the connectivity between cortical areas during an experimental task. The obtained experimental evidences support the conclusion that the SCI population involved a larger cortical network than those generated by the healthy subjects during the task performance. Such network differs for the involvement of the parietal cortices, which increases in strength near to the EMG onset.

In the present study, we estimated the cortical networks were from high-resolution EEG recordings in a group of spinal cord injured patients and in a group of healthy subjects, during the preparation of a limb movement. Then, we use the Markov Clustering method to analyse the division of the network into community structures. The results indicate large differences between the injured patients and the healthy subjects. In particular, the networks of spinal cord injured patient exhibited a higher density of clusters. In the Alpha (7-12 Hz) frequency band, the two observed largest communities were mainly composed by the cingulate motor areas with the supplementary motor areas, and by the pre-motor areas with the right primary motor area of the foot. This functional separation could reflect the partial alteration in the primary motor areas because of the effects of the spinal cord injury.

In the present work, we estimated the functional networks in the frequency domain from a set of high-resolution EEG data in a group of healthy subjects during the showing of commercial spots within a neutral documentary. Then, we evaluated the differences in the cortical network associated with later remembered and not-remembered commercials by calculating the global- E(g) and local-efficiency E(l) indexes. During the visualization of the video-clips that will be forgotten (FRG), the cortical network exhibited high values of global- and local-efficiency, reflecting a small-world configuration. During the visualization of the video-clips that will be remembered (RMB), the same indexes appeared significantly lower. Such a difference seems not depending on the spectral content of the cortical activity. This result shows how the network communication efficiency would be affected by the presence of attentional and semantic processes that are behind a successful memory encoding in a lifelike situation.

The present work intends to evaluate the functional characteristics of the cerebral network during the successful memory encoding of TV commercials. We estimated the functional networks in the frequency domain from a set of high-resolution EEG data. High resolution EEG recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). Summarizing the main results of the present study, a sign of the memorization of a particular set of TV commercials have been found in a group of investigated subjects with the aid of advanced modern tools for the acquisition and the processing of EEG data. The cerebral processes involved during the observation of TV commercials that were remembered successively by the population examined (RMB dataset) are generated by the posterior parietal cortices and the prefrontal areas, rather bilaterally and are irrespective of the frequency bands analyzed. Such results are compatible with previously results obtained from EEG recordings with superficial electrodes as well as with the brain activations observed with the use of MEG and fMRI devices.

In this paper we propose the use of an adaptive multivariate approach to define time-varying multivariate estimators based on the Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC). DTF and PDC are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. Time-varying DTF and PDC were obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Such estimators are able to follow rapid changes in the connectivity between cortical areas during an experimental task. We provide an application to the cortical estimations obtained from high resolution EEG data, recorded from a group of healthy subject during a combined foot-lips movement, and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected, one constant across the task and the other evolving during the preparation of the joint movement.

A major limitation of the approaches used in most of the studies performed so far for the characterization of the brain responses during social interaction is that only one of the participating brains is measured each time. The "interaction" between cooperating, competing or communicating brains is thus not measured directly, but inferred by independent observations aggregated by cognitive models and assumptions that link behavior and neural activation. In this paper, we use the simultaneous neuroelectric recording of several subjects engaged in cooperative games (EEG hyperscanning). This EEG hyperscanning allow us to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. We used a paradigm called Prisoner's dilemma derived from the game theory. Results collected in a population of 22 subjects suggested that the most consistently activated structure in social interaction paradigms is the medial prefrontal cortex, which is found to be active in all the conflict situations analyzed. The role of the anterior cingulated cortex (ACC) assumes a main character being a discriminant factor for the "defect" attitude of the entire population examined. This observation is compatible with the role that the Theory of Mind assigns to the ACC.

In this work, a novel approach is proposed in order to capture relevant features related to the structure and organization of the functional brain networks estimated in the time-frequency domain. To achieve this, we used a cascade of computational tools able to estimate first the electrical activity of the cortical surface by using high resolution EEG techniques. Then, on the cortical signals from different regions of interests we estimated the time-varying functional connectivity patterns by means of the adaptive Partial Directed Coherence. Such time-varying connectivity estimation returns a series of causality patterns evolving during the examined task which can be summarized and interpreted with the aid of mathematical indexes based on the graph theory. The combination of all these methods is demonstrated on a set of high resolution EEG data recorded from a healthy subject performing a simple foot movement.

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.

In this pilot study, a system that allows disabled persons to improve or recover their mobility and communication within the surrounding environment was implemented and validated. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Fourteen patients with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program. All users utilized regular assistive control options (e.g., microswitches or head trackers) while four patients learned to operate the system by means of a non-invasive EEG-based Brain-Computer Interface, based on the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp.

In order to study the concurrent activity in subjects interacting in cooperation or competition activities, the issue of the simultaneous recording of their brain activity became mandatory. The simultaneous recording of neuroelectric activity of the brain is called "EEG hyperscanning". We would like present results obtained by EEG hyperscannings performed on a group of subjects engaged in a card game. The EEG hyperscannings have been performed with the simultaneous use of high resolution EEG devices on groups of four subjects while they were playing a card game. We estimated the concurrent activity in multiple brains of the group and we depicted the causal connections between regions of different brains. Results obtained in a study of several groups recorded by the EEG hyperscanning reveal larger activity in prefrontal and anterior cingulated cortex in different frequency bands for the player that start the game when compared to other players. EEG hyperscannings will open a different area for the study of neuroscience, in which the activity of multiple brains during social cooperation could be investigated.