1. Exploring the impact of electroencephalography-based neurofeedback (EEG NFB) on motor deficits in Parkinson’s disease : a targeted literature reviewLaura Blaznik, Uroš Marušič, 2025, review article Abstract: Parkinson’s disease (PD) is a progressive neurodegenerative disorder, with pharmacological treatments predominantly focusing on dopaminergic therapies. In the early stages of PD, symptoms may also be alleviated through non-pharmacological interventions. One such non-invasive technique is electroencephalogram neurofeedback (EEG NFB), which has shown promising results in improving the cognitive and motor functions of PD patients. The aim of our study was to assess the existing evidence, identify key trends and determine potential opportunities for future research in the field of EEG NFB for PD. This analysis explores the impact of EEG NFB on motor deficits in PD and identifies key factors for the successful implementation of EEG NFB as evidenced in the literature. The synthesis includes findings from five relevant studies, including one case study, one pilot study and three randomized controlled trials. Study selection followed the PICO framework to ensure relevance and rigor. The results suggest a correlation between sensorimotor rhythm (SMR) and beta rhythms, with increases in SMR (13–15 Hz) and beta (12–15 Hz) rhythms linked to improvements in balance, mobility and stability in PD patients. However, limitations such as small sample sizes, brief intervention durations and lack of follow-up warrant a cautious interpretation. Future research should prioritize robust protocols, larger samples and extended neurofeedback training to fully assess EEG NFB’s potential for PD management.
Keywords: Parkinson's disease, motor deficits, biofeedback, neurofeedback, electroencephalography
Published in DiRROS: 27.02.2025; Views: 277; Downloads: 152
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2. Cortico-muscular phase connectivity during an isometric knee extension task in people with early Parkinson’s diseaseNina Omejc, Tomislav Stankovski, Manca Peskar, Miloš Kalc, Paolo Manganotti, Klaus Gramann, Sašo Džeroski, Uroš Marušič, 2025, original scientific article Abstract: — Introduction: Parkinson’s disease (PD) is characterized by enhanced beta-band activity (13–30 Hz) in the motor control regions. Simultaneously, corticomuscular (CM) connectivity in the beta-band during isometric contractions tends to decline with age, in various diseases, and under dual-task conditions. Objective: This study aimed to characterize electroencephalograph (EEG) and electromyograph (EMG) power spectra during a motor task, assess CM phase connectivity, and explore how these measures are modulated by an additional cognitive task. Specifically, we focused on the beta-band to explore the relationship between heightened beta amplitude and reduced beta CM connectivity. Methodology: Early-stage people with PD and age-matched controls performed an isometric knee extension task, a cognitive task, and a combined dual task, while EEG (128ch) and EMG (2x32ch) were recorded. CM phase connectivity was assessed through phase coherence and a phase dynamics model. Results: The EEG power spectrum revealed no cohort differences in the beta-band. EMG also showed no differences up to 80 Hz. However, the combined EEG-EMG analysis uncovered reduced beta phase coherence in people with early PD during the motor task. CM phase coherence exhibited distinct scalp topography and frequency ranges compared to the EEG power spectrum, suggesting different mechanisms for pathological beta increase and CM connectivity. Additionally, phase dynamics modelling indicated stronger directional coupling from the cortex to the active muscle and less prominent phase coupling across people with PD. Despite high inter-individual variability, these metrics may prove useful for personalized assessments, particularly in people with heightened CM connectivity.
Keywords: electroencephalography, brain modeling, electromiography, coherence, motors, diseases, couplings, electrodes, oscillators, protocols
Published in DiRROS: 13.01.2025; Views: 351; Downloads: 187
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3. Beta–gamma phase-amplitude coupling as a non-invasive biomarker for Parkinson’s diseas : insights from Electroencephalography studiesTisa Hodnik, Stiven Roytman, Nicolaas I. Bohnen, Uroš Marušič, 2024, review article Abstract: Phase-amplitude coupling (PAC) describes the interaction of two separate frequencies in which the lower frequency phase acts as a carrier frequency of the higher frequency amplitude. It is a means of carrying integrated streams of information between micro- and macroscale systems in the brain, allowing for coordinated activity of separate brain regions. A beta–gamma PAC increase over the sensorimotor cortex has been observed consistently in people with Parkinson’s disease (PD). Its cause is attributed to neural entrainment in the basal ganglia, caused by pathological degeneration characteristic of PD. Disruptions in this phenomenon in PD patients have been observed in the resting state as well as during movement recordings and have reliably distinguished patients from healthy participants. The changes can be detected non-invasively with the electroencephalogram (EEG). They correspond to the severity of the motor symptoms and the medication status of people with PD. Furthermore, a medication-induced decrease in PAC in PD correlates with the alleviation of motor symptoms measured by assessment scales. A beta–gamma PAC increase has, therefore, been explored as a possible means of quantifying motor pathology in PD. The application of this parameter to closed-loop deep brain stimulation could serve as a self-adaptation measure of such treatment, responding to fluctuations of motor symptom severity in PD. Furthermore, phase-dependent stimulation provides a new precise method for modulating PAC increases in the cortex. This review offers a comprehensive synthesis of the current EEG-based evidence on PAC fluctuations in PD, explores the potential practical utility of this biomarker, and provides recommendations for future research.
Keywords: neurodegenerative diseases, Parkinson’s disease, electroencephalography, phase-amplitude coupling
Published in DiRROS: 21.03.2024; Views: 810; Downloads: 420
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4. A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprostheticsArnau 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: 1247; Downloads: 823
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5. Higher neural demands on stimulus processing after prolonged hospitalization can be mitigated by a cognitively stimulating environmentUroš Marušič, Rado Pišot, Voyko Kavcic, 2021, original scientific article Abstract: Prolonge d periods of complete physical inactivity or bed rest trigger various alterations in the functional and metabolic levels of the human body. However, bed rest-related adaptations of the central nervous system are less known and thoroughly studied. The aim of this study was to investigate brain electrophysiological changes using event-related potentials (ERPs) after 14 days of bed rest and 12 consecutive sessions of computerized cognitive training (CCT). Sixteen older (Mage= 60 years) healthy volunteers were randomly divided into a CCT treatment group and an active control group. All participants performed ERP measurements based on the foveal visual presentation of a circle on a black background before and after bed rest. After 14 days of bed rest, participants in the control group showed increased peak P1 amplitude (p = .012), decreased P1 latency (p = .024), and increased P2 amplitude (p = .036), while the CCT group also showed decreased P1 latency (p = .023) and decreased P2 latency (p = .049). Our results suggest that, even from a central adaptation perspective, prolonged periods of physical inactivity or bed rest trigger additional neural recruitment and should therefore be minimized, and that CCT may serve as a tool to mitigate this. Future research should focus on other aspects of central nervous system adaptation following periods of immobilization/hospitalization to improve our knowledge of infl uence of physical inactivity and its eff ects on cortical activity and to develop appropriate countermeasures to mitigate functional dysregulation.
Keywords: aging, physical inactivity, immobilization, electroencephalography, EEG, computerized cognitive training
Published in DiRROS: 13.05.2021; Views: 1828; Downloads: 1309
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