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Povzetek: The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain–computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals’ performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.
Ključne besede: aging, elderly, machine learning, visual oddball study, brain-computer interface
Objavljeno v DiRROS: 01.02.2023; Ogledov: 423; Prenosov: 220
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Ključne besede: identical location electron microscopy, image analysis, machine learning, electrocatalysis, metallic nanoparticles
Objavljeno v DiRROS: 01.07.2022; Ogledov: 596; Prenosov: 422
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Ključne besede: neuroendocrine tumors, lung neoplasms, carcinoma, prognosis, pathology, histology, Ki-67 antigen, machine learning, image enhancement, lung cancer, neuroendocrine neoplasms
Objavljeno v DiRROS: 09.11.2021; Ogledov: 825; Prenosov: 349
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Povzetek: This study evaluated the effects of an exergame program (TennisVirtua-4, Playstation Kinect) combined with traditional tennis training on autonomic regulation, tennis technique, gross motor skills, clinical reaction time, and cognitive inhibitory control in children. Sixty-three children were randomized into four groups (1st % two exergame and two regular trainings sessions/week, 2nd % one exergame and one regular training sessions/week, 3rd % two regular trainings sessions/week, and 4th % one regular training session/week) and compared at baseline, 6-month immediately post intervention and at 1-year follow-up post intervention. At 6-month post intervention the combined exergame and regular training sessions revealed: higher breathing frequency, heart rate (all ps % 0.001) and lower skin conductance levels (p = 0.001) during exergaming; additional benefits in the point of contact and kinetic chain elements of the tennis forehand and backhand technique (all ps % 0.001); negative impact on the shot preparation and the follow-through elements (all ps % 0.017); higher ball skills (as part of the gross motor skills) (p < 0.001); higher percentages of clinical reaction time improvement (1st %9.7% vs 3rd group %7.4% and 2nd %6.6% vs 4th group %4.4%, all ps % 0.003) and cognitive inhibitory control improvement in both congruent (1st %20.5% vs 3rd group %18.4% and 2nd %11.5% vs 4th group %9.6%, all ps % 0.05) and incongruent (1st group %19.1% vs 3rd group %12.5% and 2nd group %11.4% vs 4th group %6.5%, all ps % 0.001) trials. The 1-year follow-up test showed no differences in the tennis technique, clinical reaction time and cognitive inhibitory control improvement between groups with the same number of trainings per week. The findings support exergaming as an additional training tool, aimed to improve important cognitive-motor tennis skills by adding dynamics to the standardized training process. Caution should be placed to planning this training, e.g., in a mesocycle, since exergaming might decrease the improvement of specific tennis technique parts of the trainees. (ClinicalTrials.gov; ID: NCT03946436).
Ključne besede: tennis, training, performance, children, motor learning, cognitive learning, teaching, strategies
Objavljeno v DiRROS: 17.03.2021; Ogledov: 1226; Prenosov: 1022
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Povzetek: Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand.
Ključne besede: demand forecasting, buildings, energy modeling, forecast accuracy, machine learning
Objavljeno v DiRROS: 15.03.2019; Ogledov: 2295; Prenosov: 1078
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