Cortical network dynamics during foot movements.

The present work intends to evaluate the dynamics of the cerebral networks during the preparation and the execution of the foot movement. In order to achieve this objective, we have used mathematical tools capable of estimating the cortical activity via high-resolution EEG techniques. Afterwards we estimated, the instantaneous relationships occurring among the time-series of sixteen regions of interest (ROIs) in the Alpha (7-12 Hz) and Beta (13-29 Hz) band through the adaptive multivariate autoregressive models. Eventually, we evaluated the weighted-topology of the cerebral networks by calculating some theoretical graph indexes. The results show that the main structural changes are encoded in the highest spectral contents (Beta band). In particular, during the execution of the foot movement the cingulate motor areas (CM) work as network "hubs" presenting a large amount of outgoing links to the other ROIs. Moreover, the connectivity pattern changes its structure according to the different temporal stages of the task. In particular, the communication between the ROIs reaches its highest level of efficiency during the preparation of the foot movement, as revealed by the "small-world" property of the network, which is characterized by the presence of abundant clustering connections combined with short average distances between the cortical areas.

High-resolution EEG techniques for brain-computer interface applications.

High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.

Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach.

Over the last 20 years, a body of techniques known as high resolution EEG has allowed precise estimation of cortical activity from non-invasive EEG measurements. The availability of cortical waveforms from non-invasive EEG recordings allows to have not only the level of activation within a single region of interest (ROI) during a particular task, but also to estimate the causal relationships among activities of several cortical regions. However, interpreting resulting connectivity patterns is still an open issue, due to the difficulty to provide an objective measure of their properties across different subjects or groups. A novel approach addressed to solve this difficulty consists in manipulating these functional brain networks as graph objects for which a large body of indexes and tools are available in literature and already tested for complex networks at different levels of scale (Social, WorldWide-Web and Proteomics). In the present work, we would like to show the suitability of such approach, showing results obtained comparing separately two groups of subjects during the same motor task and two different motor tasks performed by the same group. In the first experiment two groups of subjects (healthy and spinal cord injured patients) were compared when they moved and attempted to move simultaneously their right foot and lips, respectively. The contrast between the foot-lips movement and the simple foot movement was addressed in the second experiment for the population of the healthy subjects. For both the experiments, the main question is whether the "architecture" of the functional connectivity networks obtained could show properties that are different in the two groups or in the two tasks. All the functional connectivity networks gathered in the two experiments showed ordered properties and significant differences from "random" networks having the same characteristic sizes. The proposed approach, based on the use of indexes derived from graph theory, can apply to cerebral connectivity patterns estimated not only from the EEG signals but also from different brain imaging methods.

Estimate of causality between independent cortical spatial patterns during movement volition in spinal cord injured patients.

Static hemodynamic or neuroelectric images of brain regions activated during particular tasks do not convey the information of how these regions communicate to each other. Cortical connectivity estimation aims at describing these interactions as connectivity patterns which hold the direction and strength of the information flow between cortical areas. In this study, we attempted to estimate the causality between distributed cortical systems during a movement volition task in preparation for execution of simple movements by a group of normal healthy subjects and by a group of Spinal Cord Injured (SCI) patients. To estimate the causality between the spatial distributed patterns of cortical activity in the frequency domain, we applied a series of processing steps on the recorded EEG data. From the high-resolution EEG recordings we estimated the cortical waveforms for the regions of interest (ROIs), each representing a selected sensor group population. The solutions of the linear inverse problem returned a series of cortical waveforms for each ROI considered and for each trial analyzed. For each subject, the cortical waveforms were then subjected to Independent Component Analysis (ICA) pre-processing. The independent components obtained by the application of the ThinICA algorithm were further processed by a Partial Directed Coherence algorithm, in order to extract the causality between spatial cortical patterns of the estimated data. The source-target cortical dependencies found in the group of normal subjects were relatively similar in all frequency bands analyzed. For the normal subjects we observed a common source pattern in an ensemble of cortical areas including the right parietal and right lip primary motor areas and bilaterally the primary foot and posterior SMA areas. The target of this cortical network, in the Granger-sense of causality, was shown to be a smaller network composed mostly by the primary foot motor areas and the posterior SMA bilaterally. In the case of the SCI population, both the source and the target cortical patterns had larger sizes than in the normal population. The source cortical areas included always the primary foot and lip motor areas, often bilaterally. In addition, the right parietal area and the bilateral premotor area 6 were also involved. Again, the patterns remained substantially stable across the different frequency bands analyzed. The target cortical patterns observed in the SCI population had larger extensions when compared to the normal ones, since in most cases they involved the bilateral activation of the primary foot movement areas as well as the SMA, the primary lip areas and the parietal cortical areas.

Hypermethods for EEG hyperscanning.

Until now, in EEG studies the activity of the brain during simple or complex tasks have been recorded in a single subject. Often, during such EEG recordings, subjects interacts with the external devices or the researchers in order to reproduce conditions similar to the those usually occurring in the real-life. However, 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 hemodynamic or neuroelectric activity of the brain is called "hyperscanning". We would like present results obtained by EEG hyperscannings performed on a group of subjects engaged in cooperative games. The EEG hyperscannings have been performed with the simultaneous use of high resolution EEG devices on groups of three and four subjects while they were playing cooperative games. The analysis of such data have been conducted with analysis method that taken into account the particular nature of the data simultaneously gathered from different subjects. We called these methods hypermethods. In particular, we estimate the concurrent activity in multiple brains of the group and we depicted the causal connections between regions of different brains (hyperconnectivity). The resulting causality patterns will link certain areas of the brain of a subject to the waveforms obtained from the other brain areas of another subject of the same group. Results obtained in a study of several groups recorded by the hyperscanning reveals causal links between prefrontal areas of the different subjects when they are performing cooperative games in different frequency bands. Hypermethods for hyperscanning will open a different area for the study of neuroscience, in which the activity of multiple brains during social cooperation could be investigated. In such area the importance of EEG will be relevant due to its temporal and spatial resolution now obtainable w- ith the high resolution EEG techniques.

Neural basis for the brain responses to the marketing messages: an high resolution EEG study.

We investigated the behaviour of the brain during the visualization of commercial videos by tracking the cortical activity and the functional connectivity changes in normal subjects. High resolution EEG recordings were performed in a group of healthy subjects, and the cortical activity during the visualization of standard commercial spots and emotional spots (no profit companies) was estimated by using the solution of the linear inverse problem with the use of realistic head models. The cortical activity was evaluated in several regions of interest (ROIs) coincident with the Brodmann areas. The pattern of cortical connectivity was obtained by using the partial directed coherence (PDC) and investigated in the time and frequency domains, in the principal four frequency bands, namely the theta (4-7 Hz), the alpha (8-12 Hz), the beta (13-30 Hz) and the gamma (above 30 Hz). Results suggest a time-varying engagement of the orbitofrontal circuits that is thought to be involved in the reward value of the stimuli.

Removal of ocular artifacts for high resolution EEG studies: a simulation study.

Eye movements and blinks may produce unusual voltage changes that propagates from the eyeball through the head as volume conductor up to the scalp electrodes, generating severe electroencephalographic artifacts. Several methods are now available to correct the distortion induced by these events on the EEG, having different advantages and drawbacks. The main focus of this work is to quantify the performance of the removal of EOG artifact due to the application of the independent component analysis (ICA) methodology. The precise quantification of the effects of artifact removal by ICA is possible by using a simulation setup, with a realistic head model, that it is able to mimic the occurrence of an eye blink. The electrical activity generated by the simulated eyeblink were propagated through the realistic head model and superimposed to a clean segment of EEG. Then, artifact removal was performed by using the ICA approach. Ocular artifact removal was evaluated in different operative conditions, characterized by different signal to noise ratio and number of electrodes. The error measures used were the relative error and the correlation coefficient between the clear, original EEG segment and those obtained after the application of the ICA procedure.