This research compares methods for measuring pilot mental workload (MWL) from the electrocardiogram (ECG) signal. ECG-based metrics have been used extensively in MWL research. Heart rate (HR) and heart-rate variability (HRV) exhibit changes in response to varying levels of task demand. Classical methods for HRV analysis examine the ECG signal in the linear time and frequency domains. More contemporary research has advanced the notion that nonlinear elements contribute to cardiac control and ECG signal generation, spawning development of analytical techniques borrowed from the domain of nonlinear dynamics (NLD). Applications of nonlinear HRV analysis are substantial in clinical diagnosis settings; however, such applications are less frequent in MWL research, especially in the aviation domain. Specifically, the relative utility of linear and non-linear HRV analysis methods has not been fully assessed in pilot MWL research. This thesis contributes to aforementioned research gap by comparing a non-linear HRV method, utilizing transition probability variances (TPV), to classical time and frequency domain methods, focusing the analysis on sensitivity and diagnosticity. ECG data is harvested from a recent study characterizing spatial disorientation (SDO) risk amongst three candidate off-boresight (OBS) helmet-mounted display (HMD) symbologies in a tactically relevant live-flight task. A comparative analysis of methods on this dataset and supplemental workload analysis for the HMD study are presented. Results indicate the TPV method may exhibit higher sensitivity and diagnosticity than classical methods. However, limitations of this analysis warrant further investigation into this question.

Assessing the pilot’s cognitive state is of increasing importance in aviation, especially for the development of adaptive assistance systems. For this purpose, the assessment of mental workload (MWL) is of special interest as an indication when and how to adapt the automation to fit the pilot’s current needs. Thus, there is a need to assess the pilot continuously, objectively and non-intrusively. Neurophysiological measurements like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are promising candidates for such an assessment. Yet, there is evidence that EEG- and fNIRS-based MWL measures are susceptible to influences from other concepts like mental fatigue (MF), and decrease in accuracy when MWL and MF confound. Still, there are only few studies targeting this problem, and no systematic investigation into this problem has taken place. Thus, the validity of neurophysiological MWL measures is not clear yet. In order to undertake such a systematic investigation, I conducted three studies: one experiment in which I investigated the effects of increasing MWL on cortical activation when MF is controlled for; a second experiment in which I examined the effects of increasing MF on cortical activation when MWL is controlled for; and a further comparative analysis of the gathered data. In order to induce MWL and MF in a controllable and comparable fashion, I conceived and used a simplified simulated flight task with an incorporated adapted n-back and monitoring task. I used a concurrent EEG-fNIRS measurement to gain neurophysiological data, and collected performance data and self-reported MWL and MF. In the first study (N = 35), I induce different four levels of MWL by increasing the difficulty of the n-back task, and controlled for MF by means of randomization and a short task duration (≤ 45 minutes). Higher task difficulty elicited higher subjective MWL ratings, declining performance, increased frontal theta band power and decreased frontal deoxyhaemoglobin (HbR) concentration. Furthermore, fNIRS proved more sensitive to tasks with low difficulty, and EEG to tasks with high difficulty. Only the combination of both methods was able to discriminate all four induced MWL levels. Thus, frontal theta band power and HbR were sensitive to changing MWL. In the second study (N = 31), I. I induced MF by means of time on task. Thus, I prolonged the task duration to approx. 90 minutes, and controlled for MWL by using a low but constant task difficulty derived from the first experiment. Over the course of the experiment, the participants’ subjective MF increased linearly, but their performance remained stable. In the EEG data, there was an early increase and levelling in parietal alpha band power and a slower, but steady increase in frontal theta band power. The fNIRS data did not show a consistent trend in any direction with increasing MF. Thus, only parietal alpha and frontal theta band power were sensitive to changing MF. In the third study, I investigated the validity of two EEG indices commonly used for MWL assessment, the Task Load Index (TLI) and the Engagement Index (EI). I computed the indices from the data of the two experiments, and compared the results between the datasets, and to single band powers. The TLI increased with increasing MWL, but was less sensitive than theta band power alone, and varied slightly with increasing MF. The EI did not vary with MWL, and was not sensitive to gradually increasing MF. Thus, neither index could be considered a valid MWL measure. In sum, neurophysiological measures can be used to assess changes in MWL. Yet, frontal HbR was the only measure sensitive to MWL that did not also vary with MF, and further research is needed to conclude if this finding holds true under different task characteristics. Thus, the tested EEG and fNIRS measures are only valid indications of MWL when confounding effects of MF are explicitly controlled for. I discuss further influences on the tested EEG and fNIRS measures, possible combinations with other data sources, and practical challenges for a neurophysiological MWL assessment. I conclude that neurophysiological measures should be used carefully outside the laboratory, as their validity will likely suffer in realistic settings. When their limitations are understood and respected, they can help to understand the cognitive processes involved in MWL, and can be a valuable addition to an MWL assessment.

The Aviation Safety Action Program (ASAP) is a voluntary, non-jeopardy reporting program supported by commercial airlines. The program provides pilots with a way to report unsafe occurrences, including their own errors, without risk of punitive action on the part of the airlines or the Federal Aviation Administration (FAA). Through a set of on-site visits to airlines with ASAP programs, deficiencies were identified in the way airlines collect ASAP reports from pilots. It was concluded that these deficiencies might be limiting the ability of airlines to identify hazards contributing to reported safety events. The purpose of this research was to determine if the use of an ASAP reporting form based on a human factors model, referred to as the Threat and Error Management (TEM) model, would result in pilots providing a larger quantity and higher quality of information as compared to information provided by pilots using a standard ASAP reporting form. The TEM model provides a framework for a taxonomy that includes factors related to safety events pilots encounter, behaviors and errors they make, and threats associated with the complexities of their operational environment. A comparison of reports collected using the TEM Reporting Form and a standard reporting form demonstrated that narrative descriptions provided by pilots using the TEM Reporting Form included both a larger quantity and higher quality of information. Quantity of information was measured by comparing the average word count of the narrative descriptions. Quality of information was measured by comparing the discriminatory power of the words in the narrative descriptions and the extent to which the narrative descriptions from the two sets of reports contributed to a set of latent concepts. The findings suggest that the TEM Reporting Form can help pilots provide longer descriptions, more relevant information related to safety hazards, and expand on concepts that contribute to reported safety events. The use of the TEM Reporting Form for the collection of ASAP reports should be considered by airlines as a preferred collection method for improving the quantity and quality of information reported by pilots through ASAP programs. / text

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