A5033019597- EEG and Brain-Computer Interfaces
- Muscle activation and electromyography studies
- Neuroscience and Neural Engineering
- Stroke Rehabilitation and Recovery
- Motor Control and Adaptation
- Advanced Memory and Neural Computing
- Neural dynamics and brain function
Madrid Health Service
2024
University of Tübingen
2015-2023
Tecnalia
2021-2023
Bernstein Center for Computational Neuroscience Tübingen
2015-2021
Ikerbasque
2017-2021
Max Planck Society
2016
Max Planck Institute for Biological Cybernetics
2016
Including supplementary information from the brain or other body parts in control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched are referred to as hybrid BMIs (hBMIs) have proven be more robust accurate than regular for assistive rehabilitative applications. Electromyographic (EMG) activity is one most widely utilized biosignals hBMIs, it provides a quite direct measurement motion intention user. Whereas existing non-invasive EEG-EMG-hBMIs only...
Objective. Brain–computer-interfaces (BCIs) have been proposed not only as assistive technologies but also rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal noise ratio from electroencephalography (EEG), multidimensional decoding has main obstacle implement non-invasive BCIs in real-live scenarios. This study explores classification of several functional reaching movements same limb using EEG oscillations order create a more...
Objective. Stroke affects the expression of muscle synergies underlying motor control, most notably in patients with poorer function. The majority studies on have conventionally approached this analysis by assuming alterations inner structures after stroke. Although different synergy-based features based assumption to some extent described pathological mechanisms post-stroke neuromuscular a biomarker that reliably reflects function and recovery is still missing.Approach. Based theory...
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...
In recent years, a significant effort has been invested in the development of kinematics-decoding models from electromyographic (EMG) signals to achieve more natural control interfaces for rehabilitation therapies. However, dexterous EMG-based interface including multiple degrees freedom (DOFs) upper limb still remains challenge. Another persistent issue surface myoelectric is non-stationarity EMG across sessions. this work, decoding 7 distal and proximal DOFs' kinematics during coordinated...
Abstract The motor impairment occurring after a stroke is characterized by pathological muscle activation patterns or synergies. However, while robot-aided myoelectric interfaces have been proposed for rehabilitation, they do not address this issue, which might result in inefficient interventions. Here, we present novel paradigm that relies on the correction of activity as way to elicit even patients with complete paralysis. Previous studies demonstrated there are no substantial inter-limb...
Myoelectric control of rehabilitation devices engages active recruitment muscles for motor task accomplishment, which has been proven to be essential in rehabilitation. Unfortunately, most electromyographic (EMG) activity-based controls are limited one single degree-of-freedom (DoF), not permitting multi-joint functional tasks. On the other hand, discrete EMG-triggered approaches fail provide continuous feedback about muscle during movement. For such purposes, myoelectric interfaces...
Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, number of degrees freedom that can be simultaneously controlled is limited, which hinders functional tasks and effectiveness rehabilitation therapy. The objective this study was develop a myoelectric interface would multi-degree-of-freedom control an exoskeleton...
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...
In recent years, there has been an increasing interest in using electroencephalographic (EEG) activity to close the loop between brain oscillations and movement induce functional motor rehabilitation. Rehabilitation robots or exoskeletons have controlled EEG activity. However, all studies used a 2-class one-dimensional decoding scheme. this study we investigated of 5 movements same limb towards online scenario. Six healthy participants performed three-dimensional center-out reaching task...
Deciphering and analyzing the neural correlates of different movements from same limb using electroencephalography (EEG) would represent a notable breakthrough in field sensorimotor neurophysiology. Functional involve concurrent posture co-ordination head eye movements, which create electrical activity that affects EEG recordings. In this paper, we revisit identification brain signatures reaching present, test, validate protocol to separate effect task-related visuomotor activity. Ten...
Low-frequency electroencephalographic (EEG) activity provides relevant information for decoding movement commands in healthy subjects and paralyzed patients. Brainmachine interfaces (BMI) exploiting these signals have been developed to provide closed-loop feedback induce neuroplasticity. Several offline online studies already demonstrated that discriminable related can be decoded from low-frequency EEG activity. However, there is still not a well-established procedure guarantee this...