Unraveling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks.

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.

Cybersickness-related changes in brain hemodynamics: A pilot study comparing transcranial Doppler and near-infrared spectroscopy assessments during a virtual ride on a roller coaster.

Our aim was to assess cerebral blood flow changes during cybersickness. Transcranial Doppler (TCD) ultrasound and near infrared spectroscopy (NIRS) were used separately in two independent experiments. In both studies, a 15-min virtual roller coaster ride was used as a provocative visual stimulus. Subjective nausea ratings were obtained at 1 min intervals. The TCD study was performed in 14 healthy subjects (8 males and 6 females); in this study we also measured heart rate and arterial pressure. In a separate study a 52-channel NIRS device (Hitachi ETG-4000) was used to monitor activated brain regions by measuring oxy-hemoglobin (HbO2) concentration in 9 healthy subjects (4 male, 5 females). The TCD study results showed a significant increase in systolic (+3.8 ±â€¯1.8 mm Hg) and diastolic (+6.7 ±â€¯1.3 mm Hg) pressure at the end of the virtual ride (maximum nausea) compared to baseline (no nausea). We also found that middle cerebral artery (MCA) and posterior cerebral artery (PCA) systolic flow velocity decreased significantly at the end of the ride when compared to baseline values. Likewise, the relative systolic and diastolic conductance in the MCA decreased significantly (-0.03 ±â€¯0.02 cm × s-1 × mm Hg-1, t, p = 0.0058 and -0.03 ±â€¯0.01 cm × s-1 × mm Hg-1, p = 0.05, respectively) at maximum nausea when compared to no nausea. Additionally, there was a significant decrease (-0.02 ±â€¯0.01 cm × s-1 × mm Hg-1, p = 0.03) in the relative systolic conductance in the PCA at the end of the ride. Analysis of the NIRS results showed a significant increase in HbO2 concentration in 15/52 channels in parieto-temporal regions of both hemispheres in participants who experienced motion sickness symptoms during the experiment. This increase in HbO2 concentration correlated with increasing nausea and motion sickness symptoms. We conclude that cybersickness causes complex changes in cerebral blood flow, with an increase in perfusion in some cortical regions, but with a decrease of global cerebral perfusion.

Profiling subjective symptoms and autonomic changes associated with cybersickness.

Our aim was to expand knowledge of cybersickness - a subtype of motion sickness provoked by immersion into a moving computer-generated virtual reality. Fourteen healthy subjects experienced a 15-min rollercoaster ride presented via a head-mounted display (Oculus Rift), for 3 consecutive days. Heart rate, respiration, finger and forehead skin conductance were measured during the experiment; this was complemented by a subjective nausea rating during the ride and by Motion Sickness Assessment Questionnaire before, immediately after and then 1, 2 and 3h post-ride. Physiological measurements were analysed in three dimensions: ride time, association with subjective nausea rating and experimental day. Forehead, and to a lesser extent finger phasic skin conductance activity showed a correlation with the reported nausea ratings, while alteration in other measured parameters were mostly related to autonomic arousal during the virtual ride onset. A significant habituation was observed in subjective symptom scores and in the duration of tolerated provocation. The latter increased from 7.0±1.3min on the first day to 12.0±2.5min on the third day (p<0.05); this was associated with a reduced slope of nausea rise from 1.3±0.3units/min on the first to 0.7±0.1units/min on the third day (p<0.01). Furthermore, habituation with repetitive exposure was also determined in the total symptom score post-ride: it fell from 1.6±0.1 on the first day to 1.2±0.1 on the third (p<0.001). We conclude that phasic changes of skin conductance on the forehead could be used to objectively quantify nausea; and that repetitive exposure to provocative VR content results in habituation.

Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints.

In inverse electrocardiography, the goal is to estimate cardiac electrical sources from potential measurements on the body surface. It is by nature an ill-posed problem, and regularization must be employed to obtain reliable solutions. This paper employs the multiple constraint solution approach proposed in Brooks et al. (IEEE Trans Biomed Eng 46(1):3-18, 1999) and extends its practical applicability to include more than two constraints by finding appropriate values for the multiple regularization parameters. Here, we propose the use of real-valued genetic algorithms for the estimation of multiple regularization parameters. Theoretically, it is possible to include as many constraints as necessary and find the corresponding regularization parameters using this approach. We have shown the feasibility of our method using two and three constraints. The results indicate that GA could be a good approach for the estimation of multiple regularization parameters.

Noise reduction using anisotropic diffusion filter in inverse electrocardiology.

Filtering has been widely used in biomedical signal processing and image processing applications to cancel noise effects in signals recorded from the body. However, it is important to keep the desired characteristics of the physiological signal of interest while suppressing the noise characteristics. In this study, we used anisotropic diffusion filter (ADF) to cancel the noise on the body surface potentials measurements (BSPM) with the goal of improving the corresponding solutions of the inverse problem of electrocardiology (ECG). ADFs have been applied to image processing and they have the advantage of preserving sharp edges while rejecting the noise, thus we have chosen ADFs instead of more conventional filtering techniques. We used unfiltered and filtered BSPMs to estimate the epicardial potential distributions. We compared Tikhonov regularization results when the data included measurement noise and geometric errors. In both cases, filtering of BSPMs using the ADF improved our solutions.

Use of genetic algorithm for selection of regularization parameters in multiple constraint inverse ECG problem.

Tikhonov regularization is one of the most widely used regularization approaches in literature to overcome the ill-posedness of the inverse electrocardiography problem. However, the resulting solutions are biased towards the constraint used for regularization. One alternative to obtain improved results is to employ multiple constraints in the cost function. This approach has been shown to produce better results; however finding appropriate regularization parameters is a serious limitation of the method. In this study, we propose estimating multiple regularization parameters using a genetic algorithm based approach. Applicability of the approach is demonstrated here using two and three constraints. The results show that GA based multiple constraints approach improves the Tikhonov regularization solutions.