title: Avionic technology testing by using a cognitive neurometric index: A study with professional helicopter pilots.
abstract: In this study, we investigated the possibility to evaluate the impact of different avionic technologies on the mental workload of helicopter's 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 user's mental workload, being a valid indicator for the comparison and the test of different avionic technologies.
authors.forename: Gianluca
authors.initials: G
authors.lastname: Borghini
authors.forename: Pietro
authors.initials: P
authors.lastname: Aricò
authors.forename: Gianluca
authors.initials: G
authors.lastname: Di Flumeri
authors.forename: Serenella
authors.initials: S
authors.lastname: Salinari
authors.forename: Alfredo
authors.initials: A
authors.lastname: Colosimo
authors.forename: Stefano
authors.initials: S
authors.lastname: Bonelli
authors.forename: Linda
authors.initials: L
authors.lastname: Napoletano
authors.forename: Ana
authors.initials: A
authors.lastname: Ferreira
authors.forename: Fabio
authors.initials: F
authors.lastname: Babiloni
mesh_terms: D000328:Adult; D000337:Aerospace Medicine; D000401:Aircraft; D001359:Aviation; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D005222:Mental Fatigue; D008606:Mental Processes; D008875:Middle Aged; D008991:Monitoring, Physiologic; D011597:Psychomotor Performance; D012815:Signal Processing, Computer-Assisted; D011795:Surveys and Questionnaires; D013672:Technology; D014785:Vision, Ocular; D016526:Workload