A5054160349- EEG and Brain-Computer Interfaces
- Muscle activation and electromyography studies
- Neuroscience and Neural Engineering
- Stroke Rehabilitation and Recovery
- Neural dynamics and brain function
- Digital Mental Health Interventions
- Mental Health Research Topics
- Behavioral Health and Interventions
- Schizophrenia research and treatment
- Functional Brain Connectivity Studies
- Psychosomatic Disorders and Their Treatments
- Heart Rate Variability and Autonomic Control
- Health and Medical Studies
- Transcranial Magnetic Stimulation Studies
TH Bingen University of Applied Sciences
2024
University of Tübingen
2015-2024
Globally, mental health conditions pose a substantial burden of disease. Despite the availability evidence-based pharmacological and psychological treatments, symptoms subgroup patients do not respond to these interventions, only minority have access them. This study aimed assess efficacy ImPuls, 6-month transdiagnostic group exercise intervention, plus treatment-as-usual, compared with treatment-as-usual alone in outpatients various disorders.
In the present work, we investigated relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data 30 chronic stroke patients with severe upper-limb paralysis are basis observational study presented here. These underwent an intervention including movement training based on combined brain-machine interfaces and physiotherapy several weeks recorded in a double-blinded randomized clinical trial. We analyzed alpha oscillations over cortex 22 these...
While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of states are enabling real-time decoding and modulation an entire functional network. The present study proposes a method for neurofeedback from electroencephalographic (EEG) signals. It involves the creation fused model based Common Spatial Patterns (CSPs) data several healthy individuals. subject-independent is then used to...
More than 85% of stroke survivors suffer from different degrees disability for the rest their lives. They will require support that can vary occasional to full time assistance. These conditions are also associated an enormous economic impact families and health care systems. Current rehabilitation treatments have limited efficacy long-term effect is controversial. Here we review challenges related design development neural interfaces rehabilitative purposes. We analyze current bibliographic...
Recent studies have demonstrated the efficacy of brain-machine interfaces (BMI) for motor rehabilitation after stroke, especially those patients with severe paralysis. However, a cerebro-vascular accident can affect brain in many different manners, and lesions diverse areas, even from significantly volumes, lead to similar or equal deficits. The location insult influences way activates when moving attempting move paralyzed limb. Since essence rehabilitative BMI is precisely decode commands...
In the past few years, innovative upper-limb rehabilitation methods have been proposed for chronic stroke patients. These aim at functional motor using Brain-machine interfaces to constitute an alternate pathway from brain muscles. Even in patients with absence of residual finger movements, recovery could be achieved. The extent which these interventions are affected by individual lesion topology is yet understood. this study EEG was measured 30 during movement attempts paretic arm. We show...
Introduction: The primary constraint of non-invasive brain-machine interfaces (BMIs) in stroke rehabilitation lies the poor spatial resolution motor intention related neural activity capture. To address this limitation, hybrid brain-muscle-machine (hBMIs) have been suggested as superior alternatives. These incorporate supplementary input data from muscle signals to enhance accuracy, smoothness and dexterity device control. Nevertheless, determining distribution control between brain muscles...
Evidence suggests that patients suffering from different mental disorders benefit exercise programs combined with behavior change techniques. Based on this evidence, we have developed an program (ImPuls) specifically designed to provide additional treatment option in the outpatient health care system. The implementation of such complex into context requires research goes beyond evaluation effectiveness, and includes process evaluation. So far, related interventions has rarely been conducted....
Mental disorders are prevalent and cause considerable burden of disease. Exercise has been shown to be efficacious treat major depressive disorders, insomnia, panic disorder with without agoraphobia post traumatic stress (PTSD).This pragmatic, two arm, multi-site randomised controlled trial will evaluate the efficacy cost-effectiveness manualized, group-based six-months exercise intervention "ImPuls", among physically inactive patients disorder, PTSD within a naturalistic outpatient context...
Alpha oscillatory activity and its dynamics have a key role in motor sensory functions. Stroke affects different brain structures, which can result pathological changes alpha oscillations. We studied the relationship between amplitude of oscillations resting state their modulation during attempt movement 37 patients with severe paralysis after stroke. As previously observed healthy subjects, resting-state significantly correlated event-related desynchronization (ERD) paralyzed hand. Further,...
Modern approaches to motor rehabilitation of severe upper limb paralysis in chronic stroke decode movements from electromyography for controlling orthoses. Muscle fatigue is a phenomenon that influences these neurophysiological signals and may diminish the decoding quality. Characterization potential signal changes during movement patterns training could therefore help improve accuracy. In present work we investigated how electromyographic indices muscle Deltoid Anterior evolve typical...
Technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial decoding continuous human actions. Classical modeling methods, linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG kinematics, well generalizability across subjects. We propose a deep learning (DL) model of hand...
Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier part real-time fMRI (rt-fMRI NF) system that can guide patients with depression in achieving healthy state, and then examine subsequent changes. In first step, support vector machine (SVM) trained from information happy autobiographical imagery motor blocks received female...
Summary Background This study aimed to assess the efficacy of ImPuls, a transdiagnostic group exercise intervention, plus treatment as usual (TAU) compared TAU alone in outpatients with various mental disorders. Methods In this pragmatic, two-arm, multi-site randomised controlled trial Germany, 10 outpatient rehabilitative and medical care facilities were involved sites. Participants diagnosed according ICD-10 one or more following disorders based on structured clinical interviews: major...
Current brain-machine-interface (BMI) rehabilitation approaches typically focus on a specific aspect of neural activity. Auxiliary signals, derived from independent measures activity and recorded in parallel might be useful quantifying tracking subjects mental state performance. In this work, we demonstrate that event-related potentials can reliably observed stroke survivors with chronic paralysis during BMI intervention. The averaged evoked response remains stable over sessions varies...