Gianluca Di Flumeri

Avionic technology testing by using a cognitive neurometric index: A study with professional helicopter pilots

In this study, we investigated the possibility to evaluate the impact of different avionic technologies on the mental workload of helicopters pilots by measuring their brain activity with the EEG during a series of simulated missions carried out at AgustaWestland facilities in Yeovil (UK). The tested avionic technologies were: i) Head-Up Display (HUD); ii) Head-Mounted Display (HMD); iii) Full Conformal symbology (FC); iv) Flight Guidance (FG) symbology; v) Synthetic Vision System (SVS); and vi) Radar Obstacles (RO) detection system. It has been already demonstrated that in cognitive tasks, when the cerebral workload increases the EEG power spectral density (PSD) in theta band over frontal areas increases, and the EEG PSD in alpha band decreases over parietal areas. A mental workload index (MWL) has been here defined as the ratio between the frontal theta and parietal alpha EEG PSD values. Such index has been used for testing and comparing the different avionic technologies. Results suggested that the HUD provided a significant (p<;.05) workload reduction across all the flight scenarios with respect to the other technologies. In addition, the simultaneous use of FC and FG technologies (FC+FG) produced a significant decrement of the workload (p<;.01) with respect to the use of only the FC. Moreover, the use of the SVS technology provided on Head Down Display (HDD) with the simultaneous use of FC+FG and the RO seemed to produce a lower cerebral workload when compared with the use of only the FC. Interestingly, the workload estimation by means of subjective measures, provided by pilots through a NASA-TLX questionnaire, did not provide any significant differences among the different flight scenarios. These results suggested that the proposed MWL cognitive neurometrics could be used as a reliable measure of the users mental workload, being a valid indicator for the comparison and the test of different avionic technologies.

On the Use of Cognitive Neurometric Indexes in Aeronautic and Air Traffic Management Environments

In this paper the use of neurophysiological indexes for an objective evaluation of mental workload, during an ecological Air Traffic Management (ATM) task, has been proposed.

EEG-Based Cognitive Control Behaviour Assessment: An Ecological study with Professional Air Traffic Controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic settings.

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.

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.

Reliability over time of EEG-based mental workload evaluation during Air Traffic Management (ATM) tasks

Machine-learning approaches for mental workload (MW) estimation by using the user brain activity went through a rapid expansion in the last decades. In fact, these techniques allow now to measure the MW with a high time resolution (e.g. few seconds). Despite such advancements, one of the outstanding problems of these techniques regards their ability to maintain a high reliability over time (e.g. high accuracy of classification even across consecutive days) without performing any recalibration procedure. Such characteristic will be highly desirable in real world applications, in which human operators could use such approach without undergo a daily training of the device. In this work, we reported that if a simple classifier is calibrated by using a low number of brain spectral features, between those ones strictly related to the MW (i.e. Frontal and Occipital Theta and Parietal Alpha rhythms), those features will make the classifier performance stable over time. In other words, the discrimination accuracy achieved by the classifier will not degrade significantly across different days (i.e. until one week). The methodology has been tested on twelve Air Traffic Controls (ATCOs) trainees while performing different Air Traffic Management (ATM) scenarios under three different difficulty levels.

Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network

Subjects’ interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects’ activities, due to high workload tendencies, were less coordinated.

A new regression-based method for the eye blinks artifacts correction in the EEG signal, without using any EOG channel

Eye blinks artifacts correction in the EEG signal is a best practice in many applications. Nowadays, different approaches can be used to overcome such an issue: the most used methods are based on regression techniques and Independent Component Analysis. It is not clear which is the best performing method, thus the choice of which method to adopt depends on the specific application, on the basis of the method limitations. In fact, on one hand the regression-based methods require at least one EOG channel, and are affected by the mutual contamination between EEG and EOG signals. On the other hand, the ICA-based methods need a higher number of electrodes and a greater computational effort than the regression-based ones. In this study, a new regression-based method has been proposed and compared with three of the most used algorithms (Gratton, extended InfoMax, SOBI) for eye blinks correction. The results showed that the proposed algorithm was able (i) to achieve similar efficiency of the other methods in correcting the blinks, but without requiring neither EOG channels, nor a great electrodes number, nor a high computational effort, and (ii) to preserve EEG information in blink-free signal segments.

Neuroelectrical Indexes for the Study of the Efficacy of TV Advertising Stimuli

In this chapter, we present the findings of an experiment aimed to investigate cognitive and emotional changes of cerebral activity during the observation of TV commercials. In particular, we recorded the electroencephalographic (EEG), galvanic skin response (GSR) and heart rate (HR) from a group of 24 healthy subjects during the observation of a series of TV advertisements. The group was equally divided also by gender (male, female) and age (young, old). Comparisons of cerebral and emotional indices previously defined have been performed to highlight gender differences between TV commercial and scenes of interest of specific commercials. Findings show how EEG methodologies, along with the measurements of autonomic variables, could be used to obtain information not obtainable otherwise with verbal interviews. These cerebral and emotional indexes could help to analyze the perception of TV advertisements according to the consumer’s gender and age.

Gender and Age Related Effects While Watching TV Advertisements: An EEG Study

The aim of the present paper is to show how the variation of the EEG frontal cortical asymmetry is related to the general appreciation perceived during the observation of TV advertisements, in particular considering the influence of the gender and age on it. In particular, we investigated the influence of the gender on the perception of a car advertisement (Experiment 1) and the influence of the factor age on a chewing gum commercial (Experiment 2). Experiment 1 results showed statistically significant higher approach values for the men group throughout the commercial. Results from Experiment 2 showed significant lower values by older adults for the spot, containing scenes not very enjoyed by them. In both studies, there was no statistical significant difference in the scene relative to the product offering between the experimental populations, suggesting the absence in our study of a bias towards the specific product in the evaluated populations. These evidences state the importance of the creativity in advertising, in order to attract the target population.