Ilenia Graziani

Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator’s skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.g. attention, information processing, and working memory. A group of 10 subjects have been asked to train daily with the NASA multi-attribute-task-battery. During such training period the neuro-physiological, behavioral and subjective data have been collected. In particular, the neuro-physiological signals have been recorded on the first (T1), on the third (T3) and on the last training day (T5), while the behavioral and subjective data have been collected every day. Finally, all these data have been compared for a complete overview of the learning process and its relations with the neuro-physiological parameters. It has been shown how the integration of brain activity, in the theta and alpha frequency bands, with the autonomic parameters of heart rate and eyeblink rate could be used as metric for the evaluation of the learning progress, as well as the final training level reached by the subjects, in terms of request of cognitive resources.

Towards a multimodal bioelectrical framework for the online mental workload evaluation.

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.

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.

A neurophysiological training evaluation metric for air traffic management.

The aim of this work was to analyze the possibility to apply a neuroelectrical cognitive metrics for the evaluation of the training level of subjects during the learning of a task employed by Air Traffic Controllers (ATCos). In particular, the Electroencephalogram (EEG), the Electrocardiogram (ECG) and the Electrooculogram (EOG) signals were gathered from a group of students during the execution of an Air Traffic Management (ATM) task, proposed at three different levels of difficulty. The neuroelectrical results were compared with the subjective perception of the task difficulty obtained by the NASA-TLX questionnaires. From these analyses, we suggest that the integration of information derived from the power spectral density (PSD) of the EEG signals, the heart rate (HR) and the eye-blink rate (EBR) return important quantitative information about the training level of the subjects. In particular, by focusing the analysis on the direct and inverse correlation of the frontal PSD theta (4-7 (Hz)) and HR, and of the parietal PSD alpha (10-12 (Hz)) and EBR, respectively, with the degree of mental and emotive engagement, it is possible to obtain useful information about the training improvement across the training sessions.

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.

Neuroelectric brain imaging during a real visit of a fine arts gallery: a neuroaesthetic study of XVII century Dutch painters

Neuroaesthetic is a scientific discipline founded more than a decade ago and it refers to the study of the neural bases of beauty perception in art. The aim of this paper is to investigate the neuroelectrical correlates of brain activity of the observation of real paintings showed in a national fine arts gallery (Scuderie del Quirinale) in Rome, Italy. In fact, the present study was designed to examine how motivational factors as indexed by EEG asymmetry over the prefrontal cortex (relative activity of the left and right hemispheres) could be related to the experience of viewing a series of figurative paintings. The fine arts gallery was visited by a group of 25 subjects during an exhibition of the XVII century Dutch painters. Results suggested a strict correlation of the estimated EEG asymmetry with the verbal pleasantness scores reported by the subjects (p<;0,05) and an inverse correlation of the perceived pleasantness with the observed paintings surface dimensions (d<;0,002).

Human Factors and Neurophysiological Metrics in Air Traffic Control: A Critical Review

This paper provides a focused and organized review of the research progress on neurophysiological indicators, also called “neurometrics,” to show how they can effectively address some of the most important human factors (HFs) needs in the air traffic management (ATM) field. In order to better understand and highlight available opportunities of such neuroscientific applications, state of the art on the most involved HFs and related cognitive processes (e.g., mental workload and cognitive training) are presented together with examples of possible applications in current and future ATM scenarios. Furthermore, this paper will discuss the potential enhancements that further research and development activities could bring to the efficiency and safety of the ATM service.

The great beauty: A neuroaesthetic study by neuroelectric imaging during the observation of the real Michelangelo's Moses sculpture

Recent studies have been showed as the perception of real or displayed masterpieces by ancient or modern painters generate stable neuroelectrical correlates in humans. In this study, we collected the neuroelectrical brain activity correlated with the observation of the real sculpture of Michelangelos Moses within the church where it is actually installed in a group of healthy subjects. In addition to the cerebral activity also the heart rate (HR) and the galvanic skin response (GSR) were collected simultaneously, to assess the emotional engage of the investigated population. The Moses sculpture was observed by the group from three different point of views, each one revealing different details of the sculpture. In addition, in each location the light conditions related to the specific observation of the sculpture were explicitly changed. Results showed that cerebral activity of the subjects varied significantly across the three different views and for light condition against no light condition (p<;0.04). Furthermore, the emotional engage estimated on the whole population is higher for a point of observation in which the Moses face is directed toward the eyes of the observers (p<;0.02). Finally, the cerebral appreciation of the investigated group was found maximum from a perspective in which all the details of the sculpture could be easily grab by the eyes. Results suggested how the perception of the sculpture depends critically by the point of view of the observers and how such point of view can produce separate emotional and cerebral responses.

Differences in the perceived music pleasantness between monolateral cochlear implanted and normal hearing children assessed by EEG

The perception of the music in cochlear implanted (CI) patients is an important aspect of their quality of life. In fact, the pleasantness of the music perception by such CI patients can be analyzed through a particular analysis of EEG rhythms. Studies on healthy subjects show that exists a particular frontal asymmetry of the EEG alpha rhythm which can be correlated with pleasantness of the perceived stimuli (approach-withdrawal theory). In particular, here we describe differences between EEG activities estimated in the alpha frequency band for a monolateral CI group of children and a normal hearing one during the fruition of a musical cartoon. The results of the present analysis showed that the alpha EEG asymmetry patterns related to the normal hearing group refers to a higher pleasantness perception when compared to the cerebral activity of the monolateral CI patients. In fact, the present results support the statement that a monolateral CI group could perceive the music in a less pleasant way when compared to normal hearing children.