Jlenia Toppi

Brain-computer interface boosts motor imagery practice during stroke recovery

Motor imagery (MI) is assumed to enhance poststroke motor recovery, yet its benefits are debatable. Brain–computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI. The efficacy of BCI‐monitored MI practice as add‐on intervention to usual rehabilitation care was evaluated in a randomized controlled pilot study in subacute stroke patients.

Neuroelectrical hyperscanning measures simultaneous brain activity in humans.

In this study we illustrate a methodology able to follow and study concurrent and simultaneous brain processes during cooperation between individuals, with non invasive EEG methodologies. We collected data from fourteen pairs of subjects while they were playing a card game with EEG. Data collection was made simultaneously on all the subjects during the card game. An extension of the Granger-causality approach allows us to estimate the functional connection between signals estimated from different Regions of Interest (ROIs) in different brains during the analyzed task. Finally, with the use of graph theory, we contrast the functional connectivity patterns of the two players belonging to the same team. Statistically significant functional connectivities were obtained from signals estimated in the ROIs modeling the anterior cingulate cortex (ACC) and the prefrontal areas described by the Brodmann areas 8 with the signals estimated in all the other modelled cortical areas. Results presented suggested the existence of Granger-sense causal relations between the EEG activity estimated in the prefrontal areas 8 and 9/46 of one player with the EEG activity estimated in the ACC of their companion. We illustrated the feasibility of functional connectivity methodology on the EEG hyperscannings performed on a group of subjects. These functional connectivity estimated from the couple of brains could suggest, in statistical and mathematical terms, the modelled cortical areas that are correlated in Granger-sense during the solution of a particular task. EEG hyperscannings could be used to investigate experimental paradigms where the knowledge of the simultaneous interactions between the subjects have a value.

Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements

The aim of this research is to analyze the changes in the EEG frontal activity during the observation of commercial videoclips. In particular, we aimed to investigate the existence of EEG frontal asymmetries in the distribution of the signals’ power spectra related to experienced pleasantness of the video, as explicitly rated by the eleven experimental subjects investigated. In the analyzed population, maps of Power spectral density (PSD) showed an asymmetrical increase of theta and alpha activity related to the observation of pleasant (unpleasant) advertisements in the left (right) hemisphere. A correlation analysis revealed that the increase of PSD at left frontal sites is negatively correlated with the degree of pleasantness perceived. Conversely, the de-synchronization of left alpha frontal activity is positively correlated with judgments of high pleasantness. Moreover, our data presented an increase of PSD related to the observation of unpleasant commercials, which resulted higher with respect to the one elicited by pleasant advertisements.

On the use of EEG or MEG brain imaging tools in neuromarketing research

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.

Imaging the Social Brain by Simultaneous Hyperscanning during Subject Interaction

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

On ERPs detection in disorders of consciousness rehabilitation.

Disorders of Consciousness (DOC) like Vegetative State (VS), and Minimally Conscious State (MCS) are clinical conditions characterized by the absence or intermittent behavioral responsiveness. A neurophysiological monitoring of parameters like Event-Related Potentials (ERPs) could be a first step to follow-up the clinical evolution of these patients during their rehabilitation phase. Eleven patients diagnosed as VS (n = 8) and MCS (n = 3) by means of the JFK Coma Recovery Scale Revised (CRS-R) underwent scalp EEG recordings during the delivery of a 3-stimuli auditory oddball paradigm, which included standard, deviant tones and the subject own name (SON) presented as a novel stimulus, administered under passive and active conditions. Four patients who showed a change in their clinical status as detected by means of the CRS-R (i.e., moved from VS to MCS), were subjected to a second EEG recording session. All patients, but one (anoxic etiology), showed ERP components such as mismatch negativity (MMN) and novelty P300 (nP3) under passive condition. When patients were asked to count the novel stimuli (active condition), the nP3 component displayed a significant increase in amplitude (p = 0.009) and a wider topographical distribution with respect to the passive listening, only in MCS. In 2 out of the 4 patients who underwent a second recording session consistently with their transition from VS to MCS, the nP3 component elicited by passive listening of SON stimuli revealed a significant amplitude increment (p < 0.05). Most relevant, the amplitude of the nP3 component in the active condition, acquired in each patient and in all recording sessions, displayed a significant positive correlation with the total scores (p = 0.004) and with the auditory sub-scores (p < 0.00001) of the CRS-R administered before each EEG recording. As such, the present findings corroborate the value of ERPs monitoring in DOC patients to investigate residual unconscious and conscious cognitive function.

Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices

Driving tasks are vulnerable to the effects of sleep deprivation and mental fatigue, diminishing drivers ability to respond effectively to unusual or emergent situations. Physiological and brain activity analysis could help to understand how to provide useful feedback and alert signals to the drivers for avoiding car accidents. In this study we analyze the insurgence of mental fatigue or drowsiness during car driving in a simulated environment by using high resolution EEG techniques as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). Results suggest that it is possible to introduce a EEG-based cerebral workload index that it is sensitive to the mental efforts of the driver during drive tasks of different levels of difficulty. Workload index was based on the estimation of increase of EEG power spectra in the theta band over prefrontal areas and the simultaneous decrease of EEG power spectra over parietal areas in alpha band during difficult drive conditions. Such index could be used in a future to assess on-line the mental state of the driver during the drive task.

How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

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

The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: An application in a neuromarketing experiment

This paper presents some considerations about the use of adequate statistical techniques in the framework of the neuroelectromagnetic brain mapping. With the use of advanced EEG/MEG recording setup involving hundred of sensors, the issue of the protection against the type I errors that could occur during the execution of hundred of univariate statistical tests, has gained interest. In the present experiment, we investigated the EEG signals from a mannequin acting as an experimental subject. Data have been collected while performing a neuromarketing experiment and analyzed with state of the art computational tools adopted in specialized literature. Results showed that electric data from the mannequins head presents statistical significant differences in power spectra during the visualization of a commercial advertising when compared to the power spectra gathered during a documentary, when no adjustments were made on the alpha level of the multiple univariate tests performed. The use of the Bonferroni or Bonferroni-Holm adjustments returned correctly no differences between the signals gathered from the mannequin in the two experimental conditions. An partial sample of recently published literature on different neuroscience journals suggested that at least the 30% of the papers do not use statistical protection for the type I errors. While the occurrence of type I errors could be easily managed with appropriate statistical techniques, the use of such techniques is still not so largely adopted in the literature.

Investigating Cooperative Behavior in Ecological Settings: An EEG Hyperscanning Study

The coordinated interactions between individuals are fundamental for the success of the activities in some professional categories. We reported on brain-to-brain cooperative interactions between civil pilots during a simulated flight. We demonstrated for the first time how the combination of neuroelectrical hyperscanning and intersubject connectivity could provide indicators sensitive to the humans’ degree of synchronization under a highly demanding task performed in an ecological environment. Our results showed how intersubject connectivity was able to i) characterize the degree of cooperation between pilots in different phases of the flight, and ii) to highlight the role of specific brain macro areas in cooperative behavior. During the most cooperative flight phases pilots showed, in fact, dense patterns of interbrain connectivity, mainly linking frontal and parietal brain areas. On the contrary, the amount of interbrain connections went close to zero in the non-cooperative phase. The reliability of the interbrain connectivity patterns was verified by means of a baseline condition represented by formal couples, i.e. pilots paired offline for the connectivity analysis but not simultaneously recorded during the flight. Interbrain density was, in fact, significantly higher in real couples with respect to formal couples in the cooperative flight phases. All the achieved results demonstrated how the description of brain networks at the basis of cooperation could effectively benefit from a hyperscanning approach. Interbrain connectivity was, in fact, more informative in the investigation of cooperative behavior with respect to established EEG signal processing methodologies applied at a single subject level.