Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation.

Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver's drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Determination of an optimized conversion matrix for device independent skin color image analysis.

BACKGROUND AND OBJECTIVE: A cross-polarized diffuse reflectance (CDR) color imaging system was developed for quantitative evaluation of port wine stain (PWS) response to laser therapy. To obtain calibrated Commission International de l'Eclairage (CIE) color space images from RGB (red, green, and blue) images, it was necessary to derive an optimized conversion matrix specific to our imaging system. STUDY DESIGN/MATERIALS AND METHODS: A chromameter (CR-200, Minolta) and CDR imaging system were used to acquire CIELAB (CIE L*, a*, and b*) tristimulus values and RGB image values, respectively. A cost function was defined using these sample data sets and then a minimization algorithm was applied to obtain an optimized conversion matrix for our imaging system and illumination conditions. CIELAB color space values (L*, a*, and b*) obtained with the chromameter and CDR color images were compared to assess the accuracy of the derived matrix. RESULTS: In measurements using in vitro standard color patch or in vivo human skin samples, use of the optimized conversion matrix resulted in a good correlation with standard chromameter values for PWS human skin sites. CONCLUSIONS: The cost function minimization algorithm resulted in an optimized conversion matrix for our CDR imaging system. Use of the optimized matrix improved the utility of CDR color image analysis as a simple non-contact measurement technique to monitor quantitatively PWS response to laser therapy.