This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks.

Background: The effectiveness of in vitro fertilization depends on the assessment and selection of oocytes and embryos with the highest developmental potential. One of the tasks in the ICSI (intracytoplasmic sperm injection) procedure is the classification of oocytes according to the stages of their meiotic maturity. Oocytes classification traditionally is done manually during their observation under the light microscope. The paper is part of the bigger task, the development of the system for optimal oocyte and embryos selection. In the hereby work, we present the method for the automatic classification of oocytes based on their images, that employs DNN algorithms. Results: For the purpose of oocyte class determination, two structures based on deep neural networks were applied. DeepLabV3Plus was responsible for the analysis of oocyte images in order to extract specific regions of oocyte images. Then extracted components were transferred to the network, inspired by the SqueezeNet architecture, for the purpose of oocyte type classification. The structure of this network was refined by a genetic algorithm in order to improve generalization abilities as well as reduce the network’s FLOPs thus minimizing inference time. As a result, $$\overline{Acc}$$Acc ¯ at the level of 0.964 was obtained at the level of the validation set and 0.957 at the level of the test set. Generated neural networks as well as code that allows running the processing pipe were made publicly available. Conclusions: In this paper, the complete pipeline was proposed that is able to automatically classify human oocytes into three classes MI, MII, and PI based on the oocytes’ microscopic image.

The purpose of this research is to examine and assess the relation between a pilot’s concentration and reaction time with specific brain activity during short-haul flights. Participants took part in one-hour long flight sessions performed on the FNPT II class flight simulator. Subjects were instructed to respond to unexpected events that occurred during the flight. The brainwaves of each participant were recorded with the Emotiv EPOC+ Scientific Contextual EEG device. The majority of participants showed a statistically significant, positive correlation between Theta Power in the frontal lobe and response time. Additionally, most subjects exhibited statistically significant, positive correlations between band-power and reaction times in the Theta range for the temporal and parietal lobes. Statistically significant event-related changes (ERC) were observed for the majority of subjects in the frontal lobe for Theta frequencies, Beta waves in the frontal lobe and in all lobes for the Gamma band. Notably, significant ERC was also observed for Theta and Beta frequencies in the temporal and occipital Lobes, Alpha waves in the frontal, parietal and occipital lobes for most participants. A difference in brain activity patterns was observed, depending on the performance in time-restricted tasks.

This study presents a comprehensive analysis of power consumption in the context of AR applications on the HoloLens 2. As an ablation study, the research systematically dissects various aspects, shedding light on both hardware and software components. The power consumption was examined in diverse scenarios, ranging from the device at idle to running AR applications of increasing complexity. Overall, the study provides insights into the power management aspects associated with Hololens 2 AR devices and applications, offering cues for optimizing energy efficiency and enhancing the user experience. The findings confirm that display-related factors, such as the screen brightness and content complexity, significantly impact power consumption. However, the results reveal that power usage does not scale linearly with the number of objects in an AR scene due to overhead factors and object occlusion. Furthermore, usage scenarios such as lighting conditions and large/minor user motions are analyzed. Moreover, the influence of different development platforms, Unity and Unreal, on power consumption is discussed, highlighting their respective contributions to energy usage. These findings might be useful for researchers, developers, and users of AR technology.

Performing complex activities that accompany piloting an airplane requires a lot of concentration, coordination and reflex from the pilots. Additionally, the responsibility that lies on a person that controls any kind of flying platform can lead to the increased amount of pressure and stress. Considering all of the aforementioned conditions, it is essential to provide a system capable of evaluating and monitoring of pilot's alertness and mental fatigue during different phases of flight. Such solution can potentially drastically improve the efficiency of flight operating staff, which may lead to the increase in the overall flight safety. In this study we attempt to propose the statistical measures allowing to evaluate mental parameters of airplane pilots during the simulated flight sessions. Thanks to that, a flight aiding solution can be proposed, which would be capable of monitoring and alerting of any potentially dangerous cases of pilot's decreased concentration or increased weariness. Additionally, the specific requirements of different flight phases could be taken into consideration. The electroencephalography (EEG) is one of the most popular technologies used for the recording and monitoring of brain bioelectrical activity. Such activity can then be properly processed and used for the task of statistical description of mental states required for this research. For experiment presented in this article, the Emotiv EPOC+ Scientific Contextual EEG devices were used. Such apparatus provides 14 EEG channels positioned with the accordance to the standard 10-20 electrode placement system. Very important features of the EPOC+ headsets are their portability and ease of montage which are critical in this study. In the research a group of 5 people, with no or little previous experience with flight simulators, participated in an approximately 3 hour long session each. During that time they were asked to continuously perform a piloting sequence which consisted of 3 flight phases: starting (approx. 1 minute), flight (approx. 6 minutes, 4 turns) and landing (approx. 1 minute). As a result about 15 full flight sequences for each subject were obtained and later used for the purpose of alertness and mental fatigue evaluation. All experiments were performed on the Elite S812 FNPT II and Elite S923 FNPT II MCC flight simulators.

The purpose of the research was to examine and assess the relation between the pilot’s concentration and reaction time with specific brain activity during short-haul flights. In order to obtain this task, participants took part in 1-hour long flight sessions performed on FNPT II class flight simulator. During the flight auto–pilot was activated and subjects were instructed to give response to unexpected events that were occurring during the flight. Brainwaves of each participant were recorded with Emotiv EPOC+ Scientific Contextual EEG device. Statistical analyses of the results were presented in the article.