Anton Giulio Maglione

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.

How to Measure Cerebral Correlates of Emotions in Marketing Relevant Tasks

Nowadays, there is a growing interest in measuring emotions through the estimation of cerebral variables. Several techniques and methods are used and debated in neuroscience. In such a context, the present paper provides examples of time-varying variables related to the estimation of emotional valence, arousal and Approach-Withdrawal behavior in marketing relevant contexts. In particular, we recorded electroencephalographic (EEG), galvanic skin response (GSR) and heart rate (HR) in a group of healthy subjects while they are watching different TV commercials. Specifically, results obtained in the Experiment 1 shows a significant increase of cortical power spectral density across left frontal areas in the alpha band and an enhance of cardiac activity during the observation of TV commercials that have been judged pleasant. In the Experiment 2, frontal EEG asymmetry, GSR and HR measurements are used to draw cognitive and emotional indices in order to track the subject’s internal state frame by frame of the commercial. A specific case study shows how the variations of the defined Approach-Withdrawal and emotional indices can distinguish the reactions of younger adults from the older ones during the observation of a funny spot. This technology could be of help for marketers to overcome some of the drawbacks of the standard marketing tools (e.g., interviews, focus groups) usually adopted during the analysis of the emotional perception of advertisements.

Understanding the Impact of TV Commercials: Electrical Neuroimaging

In this paper, the applications of electrical neuroimaging, a discipline that uses electroencephalography (EEG) and intensive signal processing for the evaluation of marketing stimuli was illustrated.

Alpha EEG Frontal Asymmetries during Audiovisual Perception in Cochlear Implant Users. A Study with Bilateral and Unilateral Young Users.

OBJECTIVES The aim of the present study is to investigate the variations of the electroencephalographic (EEG) alpha rhythm in order to measure the appreciation of bilateral and unilateral young cochlear implant users during the observation of a musical cartoon. The cartoon has been modified for the generation of three experimental conditions: one with the original audio, another one with a distorted sound and, finally, a mute version. METHODS The EEG data have been recorded during the observation of the cartoons in the three experimental conditions. The frontal alpha EEG imbalance has been calculated as a measure of motivation and pleasantness to be compared across experimental populations and conditions. RESULTS The EEG frontal imbalance of the alpha rhythm showed significant variations during the perception of the different cartoons. In particular, the pattern of activation of normal-hearing children is very similar to the one elicited by the bilateral implanted patients. On the other hand, results related to the unilateral subjects do not present significant variations of the imbalance index across the three cartoons. CONCLUSION The presented results suggest that the unilateral patients could not appreciate the difference in the audio format as well as bilaterally implanted and normal hearing subjects. The frontal alpha EEG imbalance is a useful tool to detect the differences in the appreciation of audiovisual stimuli in cochlear implant patients.

Evaluation of the workload and drowsiness during car driving by using high resolution EEG activity and neurophysiologic indices.

Sleep deprivation and/or a high workload situation can adversely affect driving performance, decreasing a drivers capacity to respond effectively in dangerous situations. In this context, to provide useful feedback and alert signals in real time to the drivers physiological and brain activities have been increasingly investigated in literature. In this study, we analyze the increase of cerebral workload and the insurgence of drowsiness during car driving in a simulated environment by using high resolution electroencephalographic techniques (EEG) as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). The simulated drive tasks were modulated with five levels of increasing difficulty. A workload index was then generated by using the EEG signals and the related HR and EBR signals. Results suggest that the derived workload index is sensitive to the mental efforts of the driver during the different drive tasks performed. Such workload index was based on the estimation the variation of EEG power spectra in the theta band over prefrontal cortical areas and the variation of the EEG power spectra over the parietal cortical areas in alpha band. In addition, results suggested as HR increases during the execution of the difficult driving tasks while instead it decreases at the insurgence of the drowsiness. Finally, the results obtained showed as the EBR variable increases of its values when the insurgence of drowsiness in the driver occurs. The proposed workload index could be then used in a near future to assess on-line the mental state of the driver during a drive task.

Mental workload estimations in unilateral deafened children.

Despite of technological innovations, noisy environments still constitute a challenging and stressful situation for words recognition by hearing impaired subjects. The evaluation of the mental workload imposed by the noisy environments for the recognition of the words in prelingually deaf children is then of paramount importance since it could affect the speed of the learning process during scholar period.The aim of the present study was to investigate different electroencephalographic (EEG) power spectral density (PSD) components (in theta4-8 Hz - and alpha - 8-12 Hz - frequency bands) to estimate the mental workload index in different noise conditions during a word recognition task in prelingually deaf children, a population not yet investigated in relation to the workload index during auditory tasks. A pilot study involving a small group of prelingually deaf children was then subjected to EEG recordings during an auditory task composed by a listening and a successive recognition of words with different noise conditions. Results showed that in the pre-word listening phase frontal EEG PSD in theta band and the ratio of the frontal EEG PSD in theta band and the parietal EEG PSD in alpha band (workload index; IWL) reported highest values in the most demanding noise condition. In addition, in the phase preceding the word forced-choice task the highest parietal EEG PSD in alpha band and IWL values were reported at the presumably simplest condition (noise emitted in correspondence of the subjects deaf ear). These results could suggest the prominence of EEG PSD theta component activity in the pre-word listening phase. In addition, a more challenging noise situation in the pre-choice phase would be so “over-demanding” to fail to enhance both the alpha power and the IWL in comparison to the already demanding “simple” condition.

Electroencephalographic correlates of sensorimotor integration and embodiment during the appreciation of virtual architectural environments

Nowadays there is the hope that neuroscientific findings will contribute to the improvement of building design in order to create environments which satisfy mans demands. This can be achieved through the understanding of neurophysiological correlates of architectural perception. To this aim, the electroencephalographic (EEG) signals of 12 healthy subjects were recorded during the perception of three immersive virtual reality environments (VEs). Afterwards, participants were asked to describe their experience in terms of Familiarity, Novelty, Comfort, Pleasantness, Arousal, and Presence using a rating scale from 1 to 9. These perceptual dimensions are hypothesized to influence the pattern of cerebral spectral activity, while Presence is used to assess the realism of the virtual stimulation. Hence, the collected scores were used to analyze the Power Spectral Density (PSD) of the EEG for each behavioral dimension in the theta, alpha and mu bands by means of time-frequency analysis and topographic statistical maps. Analysis of Presence resulted in the activation of the frontal-midline theta, indicating the involvement of sensorimotor integration mechanisms when subjects expressed to feel more present in the VEs. Similar patterns also characterized the experience of familiar and comfortable VEs. In addition, pleasant VEs increased the theta power across visuomotor circuits and activated the alpha band in areas devoted to visuospatial exploration and processing of categorical spatial relations. Finally, the de-synchronization of the mu rhythm described the perception of pleasant and comfortable VEs, showing the involvement of left motor areas and embodied mechanisms for environment appreciation. Overall, these results show the possibility to measure EEG correlates of architectural perception involving the cerebral circuits of sensorimotor integration, spatial navigation, and embodiment. These observations can help testing architectural hypotheses in order to design environments matching the changing needs of humans.

High-resolution EEG analysis of power spectral density maps and coherence networks in a proportional reasoning task.

Proportional reasoning is very important logical skill required in mathematics and science problem solving as well as in everyday life decisions. However, there is a lack of studies on neurophysiological correlates of proportional reasoning. To explore the brain activity of healthy adults while performing a balance scale task, we used high-resolution EEG techniques and graph-theory based connectivity analysis. After unskilled subjects learned how to properly solve the task, their cortical power spectral density (PSD) maps revealed an increased parietal activity in the beta band. This indicated that subjects started to perform calculations. In addition, the number of inter-hemispheric connections decreased after learning, implying a rearrangement of the brain activity. Repeated performance of the task led to the PSD decrease in the beta and gamma bands among parietal and frontal regions along with a synchronization of lower frequencies. These findings suggest that repetition led to a more automatic task performance. Subjects were also divided in two groups according to their scores on the test of logical thinking (TOLT). Although no group differences in the accuracy and reaction times were found, EEG data showed higher activity in the beta and gamma bands for the group that scored better on TOLT. Learning and repetition induced changes in the pattern of functional connectivity were evident for all frequency bands. Overall, the results indicated that higher frequency oscillations in frontal and parietal regions are particularly important for proportional reasoning.

EEG Frontal Asymmetry Related to Pleasantness of Olfactory Stimuli in Young Subjects

It is widely known, in neuroscientific literature, that the brain prefrontal cortex activity asymmetry is closely linked with the pleasantness emotion experienced by the subject during a sensorial stimulation. Thus, from the electroencephalographic (EEG) signal it is possible to estimate the approach/withdrawal index, and this index has been largely investigated and validated in scientific literature, regarding visual and acoustic stimuli. In this work, we present an innovative study aimed to prove, in a systematic way, that such brain AW index is actually correlated with the “pleasant” or “no-pleasant” perception also of olfactory stimuli, conveniently produced by standardised methods in the sensory specific scientific literature. In particular, we recorded the electroencephalographic (EEG) signal from a group, gender balanced, of 24 healthy and no-smokers subjects during the perception of ten different smells, presented by means of the “Screening test-odour identification” set (Sniffin’ sticks, Burghart). The cerebral AW indexes of all the subjects, for each odorous stimulus, were compared with the appreciation numeric score assessed by the subject during the experiment, by performing a statistical correlation test. Findings show that it is possible to evaluate the pleasantness or no-pleasantness of odorous substances by means of the analysis of EEG signals collected during the presentation of such substances, making way for new applications of such measure kind in experimental environments more and more ecological, as the typical ones of the marketing research areas.