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Query: "author" (Uroš Marušič) .

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Slovenian Mobile Brain/Body Imaging Laboratory (SloMoBIL)
Uroš Marušič, Manca Peskar, 2021, other component parts

Published in DiRROS: 02.03.2023; Views: 269; Downloads: 147
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On the influence of aging on classification performance in the visual EEG oddball paradigm using statistical and temporal features
Nina Omejc, Manca Peskar, Aleksandar Miladinović, Voyko Kavcic, Sašo Džeroski, Uroš Marušič, 2023, original scientific article

Abstract: 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.
Keywords: aging, elderly, machine learning, visual oddball study, brain-computer interface
Published in DiRROS: 01.02.2023; Views: 247; Downloads: 142
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Does standing up enhance performance on the stroop task in healthy young adults? : a systematic review and meta-analysis
Maja Maša Šömen, Manca Peskar, Bettina Wollesen, Klaus Gramann, Uroš Marušič, 2023, review article

Abstract: Understanding the changes in cognitive processing that accompany changes in posture can expand our understanding of embodied cognition and open new avenues for applications in (neuro)ergonomics. Recent studies have challenged the question of whether standing up alters cognitive performance. An electronic database search for randomized controlled trials was performed using Academic Search Complete, CINAHL Ultimate, MEDLINE, PubMed, and Web of Science following PRISMA guidelines, PICOS framework, and standard quality assessment criteria (SQAC). We pooled data from a total of 603 healthy young adults for incongruent and 578 for congruent stimuli and Stroop effect (mean age = 24 years). Using random-effects results, no difference was found between sitting and standing for the Stroop effect (Hedges’ g = 0.13, 95% CI = −0.04 to 0.29, p = 0.134), even when comparing congruent (Hedges’ g = 0.10; 95% CI: −0.132 to 0.339; Z = 0.86; p = 0.389) and incongruent (Hedges’ g = 0.18; 95% CI: −0.072 to 0.422; Z = 1.39; p = 0.164) stimuli separately. Importantly, these results imply that changing from a seated to a standing posture in healthy young adults is unlikely to have detrimental effects on selective attention and cognitive control. To gain a full understanding of this phenomenon, further research should examine this effect in a population of healthy older adults, as well as in a population with pathology.
Keywords: healthy young adults, dual tasks, posture, Stroop task, cognitive-motor interference, sit-to-stand workstations
Published in DiRROS: 30.01.2023; Views: 256; Downloads: 141
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Combining physical and virtual worlds for motor-cognitive training interventions : position paper with guidelines on technology classification in movement-related research
Luka Šlosar, Claudia Voelcker-Rehage, Armin Paravlić, Ensar Abazović, Eling D. de Bruin, Uroš Marušič, 2022, review article

Abstract: Efficient movements require intact motor and cognitive function. There is a growing literature on motor-cognitive interventions to improve the overall quality of life of healthy or diseased older people. For such interventions, novel technological advances are crucial not only in terms of motivation but also to improve the user experience in a multi-stimuli world, usually offered as a mixture of real and virtual environments. This article provides a classification system for movement-related research dealing with motor-cognitive interventions performed in different extents of a virtual environment. The classification is divided into three categories: (a) type of digital device with the associated degree of immersiveness provided; (b) presence or absence of a human-computer interaction; and (c) activity engagement during training, defined by activity >1.5 Metabolic Equivalent of task. Since virtual reality (VR) often categorizes different technologies under the same term, we propose a taxonomy of digital devices ranging from computer monitors and projectors to head-mounted VR technology. All immersive technologies that have developed rapidly in recent years are grouped under the umbrella term Extended Reality (XR). These include augmented reality (AR), mixed reality (MR), and VR, as well as all technologies that have yet to be developed. This technology has potential not only for gaming and entertainment, but also for research, motor-cognitive training programs, rehabilitation, telemedicine, etc. This position paper provides definitions, recommendations, and guidelines for future movement-related interventions based on digital devices, human-computer interactions, and physical engagement to use terms more consistently and contribute to a clearer understanding of their implications.
Keywords: extended reality, virtual reality, augmented reality, mixed reality, exergaming, taxonomy, classification
Published in DiRROS: 21.12.2022; Views: 278; Downloads: 139
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Inter-person differences in isometric coactivations of triceps surae and tibialis anterior decrease in young, but not in older adults after 14 days of bed rest
Matjaž Divjak, Gašper Sedej, Nina Murks, Mitja Gerževič, Uroš Marušič, Rado Pišot, Boštjan Šimunič, Aleš Holobar, 2022, original scientific article

Abstract: We examined activation patterns of the gastrocnemius medialis (GM), gastrocnemius lateralis (GL), soleus (SO), and tibialis anterior (TA) muscles in eight older (58.4 ± 3.3 years) and seven young (23.1 ± 2.9 years) participants, before and after 14 days of horizontal bed rest. Visual feedback on the exerted muscle torque was provided to the participants. The discharge patterns of individual motor units (MUs) were studied in three repetitions of isometric plantar flexion at 30 and 60% of Maximum Voluntary Contraction (MVC), before, and 1 day after the 14-day bed rest, respectively. In the GL and GM muscles, the older participants demonstrated higher MU discharge rates than the young, regardless of the contraction level, both before and after the bed rest. In the TA and SO muscles, the differences between the older and young participants were less consistent. Detailed analysis revealed person-specific changes in the MU discharge rates after the bed rest. To quantify the coactivation patterns we calculated the correlation coefficients between the cumulative spike trains of identified MUs from each muscle, and measured the root mean square difference of the correlation coefficients between the trials of the same session (intra-session variability) and between different sessions (inter-session variability) in each participant (intra-person comparison) and across participants (inter-person comparison). In the intra-person comparison, the inter-session variability was higher than the intra-session variability, either before or after the bed rest. At 60% MVC torque, the young demonstrated higher inter-person variability of coactivation than the older participants, but this variability decreased significantly after the bed rest. In older participants, inter-person variability was consistently lower at 60% than at 30% MVC torque. In young participants, inter-person variability became lower at 60% than at 30% MVC torque only after the bed rest. Precaution is required when analyzing the MU discharge and coactivation patterns, as individual persons demonstrate individual adaptations to aging or bed rest.
Keywords: high density electromyography, muscle disuse, motor units, discharge rate, aging
Published in DiRROS: 05.09.2022; Views: 447; Downloads: 253
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A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics
Arnau Dillen, Elke Lathouwers, Aleksandar Miladinović, Uroš Marušič, Fakhreddine Ghaffari, Olivier Romain, Romain Meeusen, Kevin De Pauw, 2022, original scientific article

Abstract: Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.
Keywords: neuroprosthetics, brain-computer interface, machine learning, electroencephalography, data-driven learning, lower limb amputation
Published in DiRROS: 21.07.2022; Views: 399; Downloads: 282
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Impact of motor-cognitive interventions on selected gait and balance outcomes in older adults : ǂa ǂsystematic review and meta-analysis of randomized controlled trials
Kaja Teraž, Luka Šlosar, Armin Paravlić, Eling D. de Bruin, Uroš Marušič, 2022, review article

Abstract: Background: Efficient performance of most daily activities requires intact and simultaneous execution of motor and cognitive tasks. To mitigate age-related functional decline, various combinations of motor and cognitive training have shown promising results. The aim of this systematic review and meta-analysis of randomized controlled trials (RCTs) was to evaluate the efficacy of different types of motor-cognitive training interventions (e.g., sequential and simultaneous) on selected functional outcomes in healthy older adults. Methods: Six online academic databases were used to retrieve eligible RCTs up to April 2021, following PRISMA guidelines and PICO criteria. A random-effects model was used for all meta-analyses conducted on selected functional outcomes: single- and dual-task gait speed, the Timed Up and Go Test (TUG), and Berg Balance Scale (BBS) score. Effect size (ES) was calculated as Hedges' g and interpreted as: trivial: <0.20, small: 0.20–0.60, moderate: 0.61–1.20, large: 1.21–2.00, very large: 2.01–4.00 or extremely large >4.00. Results: From 2,546 retrieved records, 91 RCTs were included for meta-analysis (n = 3,745 participants; 64.7–86.9 years). The motor-cognitive interventions included differed according to the type of training (e.g., sequential, simultaneous with additional cognitive task or exergame training. The results showed that motor-cognitive interventions can improve gait speed under single-task conditions (small ES = 0.34, P = 0.003). The effect of the intervention was moderated by the type of control group (Q = 6.203, P = 0.013): passive (moderate ES = 0.941, P = 0.001) vs. active controls (trivial ES = 0.153, P = 0.180). No significant effect was found for dual-task walking outcomes (P = 0.063). Motor-cognitive intervention had a positive effect on TUG (small ES = 0.42, P < 0.001), where the effect of intervention was moderated by control group [passive (moderate ES = 0.73, P = 0.001) vs. active (small ES = 0.20, P = 0.020)], but not by the type of training (P = 0.064). Finally, BBS scores were positively affected by motor-cognitive interventions (small ES = 0.59, P < 0.001) with however no significant differences between type of control group (P = 0.529) or intervention modality (P = 0.585). Conclusions: This study provides evidence for the effectiveness of various types of motor-cognitive interventions on performance-based measures of functional mobility in healthy older adults. With respect to significant effects, gait speed under single-task condition was improved by motor-cognitive interventions, but the evidence shows that this type of intervention is not necessarily more beneficial than motor training alone. On the other hand, motor-cognitive interventions are better at improving multicomponent tasks of dynamic balance and mobility function, as measured by the TUG. Because of substantial heterogeneity and the current limited availability of different types of interventions, the conclusions should be interpreted with caution.
Keywords: motor-cognitive interventions, dual-task, elderly, mobility, postural control
Published in DiRROS: 21.06.2022; Views: 409; Downloads: 309
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Brain dynamics underlying preserved cycling ability in patients with Parkinson’s disease and freezing of gait
Teja Ličen, Martin Rakuša, Nicolaas I. Bohnen, Paolo Manganotti, Uroš Marušič, 2022, review article

Abstract: Parkinson’s disease (PD) is generally associated with abnormally increased beta band oscillations in the cortico-basal ganglia loop during walking. PD patients with freezing of gait (FOG) exhibit a more distinct, prolonged narrow band of beta oscillations that are locked to the initiation of movement at ∼18 Hz. Upon initiation of cycling movements, this oscillation has been reported to be weaker and rather brief in duration. Due to the suppression of the overall beta band power during cycling and its continuous nature of the movement, cycling is considered to be less demanding for cortical networks compared to walking, including reduced need for sensorimotor processing, and thus unimpaired continuous cycling motion. Furthermore, cycling has been considered one of the most efficient non-pharmacological therapies with an influence on the subthalamic nucleus (STN) beta rhythms implicative of the deep brain stimulation effects. In the current review, we provide an overview of the currently available studies and discuss the underlying mechanism of preserved cycling ability in relation to the FOG in PD patients. The mechanisms are presented in detail using a graphical scheme comparing cortical oscillations during walking and cycling in PD.
Keywords: gait, freezing of gait, Parkinson's disease, cycling, cortical oscillations, beta band
Published in DiRROS: 21.06.2022; Views: 431; Downloads: 346
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