Using functional near infrared spectroscopy to assess cognitive performance of UAV sensor operators during route scanning

The composition of UAV (Unmanned Aerial Vehicle) crew will sometimes define roles specific to tasks associated with the Ground Control Station (GCS). The sensor operator task is specific to both the type of platform and GCS they are operating, but in many instances the role of this operator is critical in determining mission success. In order to assess mission effectiveness we applied human performance measures that focussed on neurological brain imaging techniques and other physiological biomarkers in conjunction with behavioral data acquired from the sensor operator task. In the execution of the experiment, this included such tasks as route scanning, target detection and positive identification, and the tracking of identified targets. Within the scope of this paper, we reported the preliminary results for the route scanning task. Over the duration of three trials brain activity measures from the prefrontal cortex region were acquired via functional near infrared spectroscopy (fNIRS) in this research study. As the trials progressed, there was a significant difference between low and high performers on the route scanning task as determined by specific biomarkers, namely oxygenated haemoglobin. These findings support previous studies and indicates the benefits of applying neurophysiological measures in order to gain further objective insight into human cognitive performance. The use of fNIRS in this context is also discussed in terms of providing a key benefit in dynamically evaluating human performance in parallel with personalized training for UAV operators

Safety assessment of near infrared light emitting diodes for diffuse optical measurements

BACKGROUND: Near infrared (NIR) light has been used widely to monitor important hemodynamic parameters in tissue non-invasively. Pulse oximetry, near infrared spectroscopy, and diffuse optical tomography are examples of such NIR light-based applications. These and other similar applications employ either lasers or light emitting diodes (LED) as the source of the NIR light. Although the hazards of laser sources have been addressed in regulations, the risk of LED sources in such applications is still unknown. METHODS: Temperature increase of the human skin caused by near infrared LED has been measured by means of in-vivo and in-vitro experiments. Effects of the conducted and radiated heat in the temperature increase have been analyzed separately. RESULTS: Elevations in skin temperature up to 10°C have been observed. The effect of radiated heat due to NIR absorption is low – less than 0.5°C – since emitted light power is comparable to the NIR part of sunlight. The conducted heat due to semiconductor junction of the LED can cause temperature increases up to 9°C. It has been shown that adjusting operational parameters by amplitude modulating or time multiplexing the LED decreases the temperature increase of the skin significantly. CONCLUSION: In this study, we demonstrate that the major risk source of the LED in direct contact with skin is the conducted heat of the LED semiconductor junction, which may cause serious skin burns. Adjusting operational parameters by amplitude modulating or time multiplexing the LED can keep the LED within safe temperature ranges

The NIRS Analysis Package: Noise Reduction and Statistical Inference

Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS

Evaluation of UAS operator training during search and surveillance tasks

Unmanned aircraft system (UAS) sensor operators are typically required to execute search and surveillance tasks. Brain-in-the-loop measures during such tasks can help evaluate expertise development and cognitive capacities of the operator, which can be an important asset in designing adaptive and personalized training systems. Emergence of functional near infrared spectroscopy (fNIRS) has enabled monitoring of operators’ prefrontal cortex (PFC) area, which is associated with higher level cognitive functioning such as decisionmaking, problem-solving, working memory and attention in everyday working environments. In a previous sensor operator training study, we investigated and reported preliminary evidence suggesting that fNIRS measures acquired from the left prefrontal cortex were associated with the development of scanning efficiency. Here we extend these findings by exploring skill acquisition in terms of changes in the functional brain activity correlated with the improvement in target search task. During each target search task participants were required to engage in route scanning, and target identification. Neurophysiological measures via fNIRS were found to be positively correlated with behavioral results suggesting that those who were actively engaged in finding targets, had significant changes in both left and right prefrontal cortex