The Experts below are selected from a list of 2460 Experts worldwide ranked by ideXlab platform
Jonathan R Wolpaw - One of the best experts on this subject based on the ideXlab platform.
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controlling pre movement Sensorimotor Rhythm can improve finger extension after stroke
Journal of Neural Engineering, 2018Co-Authors: Sumner L Norman, Dennis J Mcfarland, Jonathan R Wolpaw, A Miner, Steven C Cramer, Eric T Wolbrecht, David J ReinkensmeyerAbstract:Author(s): Norman, SL; McFarland, DJ; Miner, A; Cramer, SC; Wolbrecht, ET; Wolpaw, JR; Reinkensmeyer, DJ | Abstract: ObjectiveBrain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement Sensorimotor Rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke.ApproachEight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance.Main resultsOf the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3 ± 7.5 blocks versus 3.5 ± 3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score.SignificanceThese results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.
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effects of Sensorimotor Rhythm modulation on the human flexor carpi radialis h reflex
Frontiers in Neuroscience, 2018Co-Authors: Aiko K Thompson, Jonathan R Wolpaw, Hannah Carruth, Rachel Haywood, Jeremy N Hill, William A Sarnacki, Lynn M Mccane, Dennis J McfarlandAbstract:Abstract People can learn over training sessions to increase or decrease Sensorimotor Rhythms (SMRs) in the electroencephalogram (EEG). Activity-dependent brain plasticity is thought to guide spinal plasticity during motor skill learning; thus, SMR training may affect spinal reflexes and thereby influence motor control. To test this hypothesis, we investigated the effects of learned mu (8-13 Hz) SMR modulation on the flexor carpi radialis (FCR) H-reflex in 6 subjects with no known neurological conditions and 2 subjects with chronic incomplete spinal cord injury (SCI). All subjects had learned and practiced over more than 10 <30-min training sessions to increase (SMR-up trials) and decrease (SMR-down trials) mu-Rhythm amplitude over the hand/arm area of left Sensorimotor cortex with ≥80% accuracy. Right FCR H-reflexes were elicited at random times during SMR-up and SMR-down trials, and in between trials. SMR modulation affected H-reflex size. In all the neurologically normal subjects, the H-reflex was significantly larger (116%±6 (mean±SE) during SMR-up trials than between trials, and significantly smaller (92%±1) during SMR-down trials than between trials (p<0.05 for both, paired t-test). One subject with SCI showed similar H-reflex size dependence (high for SMR-up trials, low for SMR-down trials): the other subject with SCI showed no dependence. These results support the hypothesis that SMR modulation has predictable effects on spinal reflex excitability in people who are neurologically normal; they also suggest that it might be used to enhance therapies that seek to improve functional recovery in some individuals with SCI or other CNS disorders.
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adaptive laplacian filtering for Sensorimotor Rhythm based brain computer interfaces
Journal of Neural Engineering, 2013Co-Authors: Dennis J Mcfarland, Jonathan R WolpawAbstract:Objective. Sensorimotor Rhythms (SMRs) are 8–30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over Sensorimotor cortex that change with movement and/or movement imagery. Many brain–computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.
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value of amplitude phase and coherence features for a Sensorimotor Rhythm based brain computer interface
Brain Research Bulletin, 2012Co-Authors: Dean J Krusienski, Dennis J Mcfarland, Jonathan R WolpawAbstract:Abstract Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain–computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of Sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.
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A comparison of regression techniques for a two-dimensional Sensorimotor Rhythm-based brain-computer interface.
Journal of Neural Engineering, 2010Co-Authors: Joan Fruitet, Dennis J Mcfarland, Jonathan R WolpawAbstract:People can learn to control electroencephalogram (EEG) features consisting of Sensorimotor-Rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.
Dennis J Mcfarland - One of the best experts on this subject based on the ideXlab platform.
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bci based Sensorimotor Rhythm training can affect individuated finger movements
Brain-Computer Interfaces, 2020Co-Authors: Dennis J Mcfarland, Sumner L Norman, Eric T Wolbrecht, David J Reinkensmeyer, W A Sarnacki, J R WolpawAbstract:Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. BCI technology might also be able to enhance rehabilitation of motor function. ...
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controlling pre movement Sensorimotor Rhythm can improve finger extension after stroke
Journal of Neural Engineering, 2018Co-Authors: Sumner L Norman, Dennis J Mcfarland, Jonathan R Wolpaw, A Miner, Steven C Cramer, Eric T Wolbrecht, David J ReinkensmeyerAbstract:Author(s): Norman, SL; McFarland, DJ; Miner, A; Cramer, SC; Wolbrecht, ET; Wolpaw, JR; Reinkensmeyer, DJ | Abstract: ObjectiveBrain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement Sensorimotor Rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke.ApproachEight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance.Main resultsOf the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3 ± 7.5 blocks versus 3.5 ± 3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score.SignificanceThese results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.
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effects of Sensorimotor Rhythm modulation on the human flexor carpi radialis h reflex
Frontiers in Neuroscience, 2018Co-Authors: Aiko K Thompson, Jonathan R Wolpaw, Hannah Carruth, Rachel Haywood, Jeremy N Hill, William A Sarnacki, Lynn M Mccane, Dennis J McfarlandAbstract:Abstract People can learn over training sessions to increase or decrease Sensorimotor Rhythms (SMRs) in the electroencephalogram (EEG). Activity-dependent brain plasticity is thought to guide spinal plasticity during motor skill learning; thus, SMR training may affect spinal reflexes and thereby influence motor control. To test this hypothesis, we investigated the effects of learned mu (8-13 Hz) SMR modulation on the flexor carpi radialis (FCR) H-reflex in 6 subjects with no known neurological conditions and 2 subjects with chronic incomplete spinal cord injury (SCI). All subjects had learned and practiced over more than 10 <30-min training sessions to increase (SMR-up trials) and decrease (SMR-down trials) mu-Rhythm amplitude over the hand/arm area of left Sensorimotor cortex with ≥80% accuracy. Right FCR H-reflexes were elicited at random times during SMR-up and SMR-down trials, and in between trials. SMR modulation affected H-reflex size. In all the neurologically normal subjects, the H-reflex was significantly larger (116%±6 (mean±SE) during SMR-up trials than between trials, and significantly smaller (92%±1) during SMR-down trials than between trials (p<0.05 for both, paired t-test). One subject with SCI showed similar H-reflex size dependence (high for SMR-up trials, low for SMR-down trials): the other subject with SCI showed no dependence. These results support the hypothesis that SMR modulation has predictable effects on spinal reflex excitability in people who are neurologically normal; they also suggest that it might be used to enhance therapies that seek to improve functional recovery in some individuals with SCI or other CNS disorders.
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use of phase locking value in Sensorimotor Rhythm based brain computer interface zero phase coupling and effects of spatial filters
Medical & Biological Engineering & Computing, 2017Co-Authors: Wenjuan Jian, Minyou Chen, Dennis J McfarlandAbstract:Phase-locking value (PLV) is a potentially useful feature in Sensorimotor Rhythm-based brain–computer interface (BCI). However, volume conduction may cause spurious zero-phase coupling between two EEG signals and it is not clear whether PLV effects are independent of spectral amplitude. Volume conduction might be reduced by spatial filtering, but it is uncertain what impact this might have on PLV. Therefore, the goal of this study was to explore whether zero-phase PLV is meaningful and how it is affected by spatial filtering. Both amplitude and PLV feature were extracted in the frequency band of 10–15 Hz by classical methods using archival EEG data of 18 subjects trained on a two-target BCI task. The results show that with right ear-referenced data, there is meaningful long-range zero-phase synchronization likely involving the primary motor area and the supplementary motor area that cannot be explained by volume conduction. Another novel finding is that the large Laplacian spatial filter enhances the amplitude feature but eliminates most of the phase information seen in ear-referenced data. A bipolar channel using phase-coupled areas also includes both phase and amplitude information and has a significant practical advantage since fewer channels required.
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adaptive laplacian filtering for Sensorimotor Rhythm based brain computer interfaces
Journal of Neural Engineering, 2013Co-Authors: Dennis J Mcfarland, Jonathan R WolpawAbstract:Objective. Sensorimotor Rhythms (SMRs) are 8–30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over Sensorimotor cortex that change with movement and/or movement imagery. Many brain–computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.
Niels Birbaumer - One of the best experts on this subject based on the ideXlab platform.
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Sensorimotor Rhythm modulation depends on resting state oscillations and cortex integrity in severely paralyzed stroke patients
International IEEE EMBS Conference on Neural Engineering, 2019Co-Authors: Eduardo Lopezlarraz, Niels Birbaumer, Andreas M Ray, Ander RamosmurguialdayAbstract:Alpha oscillatory activity and its dynamics have a key role in motor and sensory functions. Stroke affects different brain structures, which can result in pathological changes in alpha oscillations. We studied the relationship between the amplitude of alpha oscillations in resting state and their modulation during the attempt of movement in 37 patients with severe paralysis after stroke. As previously observed in healthy subjects, resting-state alpha activity significantly correlated with the alpha event-related desynchronization (ERD) during the attempt of movement of the paralyzed hand. Further, alpha ERD correlated with the presence or absence of damage in cortical structures, but resting-state alpha power did not. This result provides new insights on the understanding of the brain changes after stroke, which may help in future therapies to help the patients to recover their lost motor function.
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Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses
PloS one, 2012Co-Authors: Ander Ramos-murguialday, Sebastian Halder, Markus Schurholz, Vittorio Caggiano, Moritz Wildgruber, Andrea Caria, Eva Maria Hammer, Niels BirbaumerAbstract:Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the Sensorimotor Rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' Sensorimotor Rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent “negative” feedback (participants' Sensorimotor Rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks.
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Brain-computer interfaces: Communication and restoration of movement in paralysis
Journal of Physiology, 2007Co-Authors: Niels Birbaumer, Leonardo G CohenAbstract:The review describes the status of brain-computer or brain-machine interface research. We focus on non-invasive brain-computer interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients. BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarized rats support our hypothesis; operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication, BCIs consisting of operant learning of EEG slow cortical potentials and Sensorimotor Rhythm were demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using Sensorimotor Rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise, but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour.
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breaking the silence brain computer interfaces bci for communication and motor control
Psychophysiology, 2006Co-Authors: Niels BirbaumerAbstract:Brain–computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone. Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials, Sensorimotor Rhythm and P300, and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs, like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children.
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patients with als can use Sensorimotor Rhythms to operate a brain computer interface
Neurology, 2005Co-Authors: Andrea Kubler, Niels Birbaumer, Dennis J Mcfarland, Femke Nijboer, Jurgen Mellinger, Theresa M Vaughan, H Pawelzik, Gerwin Schalk, Jonathan R WolpawAbstract:People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG Rhythms recorded over Sensorimotor cortex. These results suggest that a Sensorimotor Rhythm-based BCI could help maintain quality of life for people with ALS.
Christa Neuper - One of the best experts on this subject based on the ideXlab platform.
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how much do strategy reports tell about the outcomes of neurofeedback training a study on the voluntary up regulation of the Sensorimotor Rhythm
Frontiers in Human Neuroscience, 2020Co-Authors: Miriam Autenrieth, Christa Neuper, Silvia Erika Kober, Guilherme WoodAbstract:The core learning mechanisms of neurofeedback (NF) training are associative, implicit, and, consequently, largely impervious to consciousness. Many other aspects of training that determine training outcomes, however, are accessible to conscious processing. The outcomes of Sensorimotor Rhythm (SMR) up-regulation training are related to the strategies reported by participants. The classification methods of individual strategies employed hitherto were possibly under influence of the idiosyncratic interpretation of the rater. To measure and possibly overcome this limitation, we employed independent raters to analyze strategies reported during SMR up-regulation training. Sixty-two healthy young participants took part in a single session of SMR up-regulation training. After completing six blocks of training, in which they received either simple visual feedback or a gamified version thereof, participants were required to report the strategies employed. Their individual learning outcomes were computed as well. Results point out that individual strategies as well as NF learning outcomes were not particularly sensitive to the presence of gamified elements in training the SMR up-regulation. A high degree of consistency across independent raters classifying strategy reports was observed. Some strategies were more typical of responders while other ones were more common among non-responders. In summary, we demonstrate a more objective and transparent way to analyze individual mental strategies to shed more light on the differences between NF responders and non-responders.
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specific or nonspecific evaluation of band baseline and cognitive specificity of Sensorimotor Rhythm and gamma based neurofeedback
International Journal of Psychophysiology, 2017Co-Authors: Silvia Erika Kober, Matthias Witte, Christa Neuper, Guilherme WoodAbstract:Neurofeedback (NF) is often criticized because of the lack of empirical evidence of its specificity. Our present study thus focused on the specificity of NF on three levels: band specificity, cognitive specificity, and baseline specificity. Ten healthy middle-aged individuals performed ten sessions of SMR (Sensorimotor Rhythm, 12-15Hz) NF training. A second group (N=10) received feedback of a narrow gamma band (40-43Hz). Effects of NF on EEG resting measurements (tonic EEG) and cognitive functions (memory, intelligence) were evaluated using a pre-post design. Both training groups were able to linearly increase the target training frequencies (either SMR or gamma), indicating the trainability of these EEG frequencies. Both NF training protocols led to nonspecific changes in other frequency bands during NF training. While SMR NF only led to concomitant changes in slower frequencies, gamma training affected nearly the whole power spectrum. SMR NF specifically improved memory functions. Gamma training showed only marginal effects on cognitive functions. SMR power assessed during resting measurements significantly increased after SMR NF training compared to a pre-assessment, indicating specific effects of SMR NF on baseline/tonic EEG. The gamma group did not show any pre-post changes in their EEG resting activity. In conclusion, SMR NF specifically affects cognitive functions (cognitive specificity) and tonic EEG (baseline specificity), while increasing SMR during NF training nonspecifically affects slower EEG frequencies as well (band non-specificity). Gamma NF was associated with nonspecific effects on the EEG power spectrum during training, which did not lead to considerable changes in cognitive functions or baseline EEG activity.
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Resting-state Sensorimotor Rhythm (SMR) power predicts the ability to up-regulate SMR in an EEG-instrumental conditioning paradigm.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2015Co-Authors: Johanna Louise Reichert, Silvia Erika Kober, Christa Neuper, Guilherme WoodAbstract:highlights abstract Objective: Instrumental conditioning of EEG activity (EEG-IC) is a promising method for improvement and rehabilitation of cognitive functions. However, it has been found that even healthy adults are not always able to learn how to regulate their brain activity during EEG-IC. In the present study, the role of a neurophysiological predictor of EEG-IC learning performance, the resting-state power of sensorimo- tor Rhythm (rs-SMR, 12-15 Hz), was investigated. Methods: Eyes-open and eyes-closed rs-SMR power was assessed before N = 28 healthy adults underwent 10 training sessions of instrumental SMR conditioning (ISC), in which participants should learn to volun- tarily increase their SMR power by means of audio-visual feedback. A control group of N = 19 participants received gamma (40-43 Hz) or sham EEG-IC. Results: N = 19 of the ISC participants could be classified as ''responders'' as they were able to increase SMR power during training sessions, while N = 9 participants (''non-responders'') were not able to increase SMR power. Rs-SMR power in responders before start of ISC was higher in widespread parieto-occipital areas than in non-responders. A discriminant analysis indicated that eyes-open rs-SMR power in a central brain region specifically predicted later ISC performance, but not an increase of SMR in the control group. Conclusions: Together, these findings indicate that rs-SMR power is a specific and easy-to-measure predic- tor of later ISC learning performance. Significance: The assessment of factors that influence the ability to regulate brain activity is of high relevance, as it could be used to avoid potentially frustrating and expensive EEG-IC training sessions for participants who have a low chance of success.
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Sensorimotor Rhythm based brain computer interface training the impact on motor cortical responsiveness
Journal of Neural Engineering, 2011Co-Authors: Floriana Pichiorri, Christa Neuper, Andrea Kubler, F De Vico Fallani, Febo Cincotti, Fabio Babiloni, Marco Molinari, Sonja C Kleih, Donatella MattiaAbstract:The main purpose of electroencephalography (EEG)-based brain–computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how Sensorimotor Rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naive participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22–29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.
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a scanning protocol for a Sensorimotor Rhythm based brain computer interface
Biological Psychology, 2009Co-Authors: Elisabeth V C Friedrich, Christa Neuper, Dennis J Mcfarland, Theresa M Vaughan, Peter Brunner, Jonathan R WolpawAbstract:The scanning protocol is a novel Brain-Computer Interface (BCI) implementation that can be controlled with Sensorimotor Rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5 s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of ten naive users trained for ten 30-min sessions over five weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30-80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.
Andrea Kubler - One of the best experts on this subject based on the ideXlab platform.
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the influence of motivation and emotion on Sensorimotor Rhythm based brain computer interface performance
Psychophysiology, 2021Co-Authors: Sonja C Kleihdahms, Loic Botrel, Andrea KublerAbstract:While decades of research have investigated and technically improved brain-computer interface (BCI)-controlled applications, relatively little is known about the psychological aspects of brain-computer interfacing. In 35 healthy students, we investigated whether extrinsic motivation manipulated via monetary reward and emotional state manipulated via video and music would influence behavioral and psychophysiological measures of performance with a Sensorimotor Rhythm (SMR)-based BCI. We found increased task-related brain activity in extrinsically motivated (rewarded) as compared with nonmotivated participants but no clear effect of emotional state manipulation. Our experiment investigated the short-term effect of motivation and emotion manipulation in a group of young healthy subjects, and thus, the significance for patients in the locked-in state, who may be in need of a BCI, remains to be investigated.
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Sensorimotor Rhythm based brain computer interface training the impact on motor cortical responsiveness
Journal of Neural Engineering, 2011Co-Authors: Floriana Pichiorri, Christa Neuper, Andrea Kubler, F De Vico Fallani, Febo Cincotti, Fabio Babiloni, Marco Molinari, Sonja C Kleih, Donatella MattiaAbstract:The main purpose of electroencephalography (EEG)-based brain–computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how Sensorimotor Rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naive participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22–29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.
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patients with als can use Sensorimotor Rhythms to operate a brain computer interface
Neurology, 2005Co-Authors: Andrea Kubler, Niels Birbaumer, Dennis J Mcfarland, Femke Nijboer, Jurgen Mellinger, Theresa M Vaughan, H Pawelzik, Gerwin Schalk, Jonathan R WolpawAbstract:People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG Rhythms recorded over Sensorimotor cortex. These results suggest that a Sensorimotor Rhythm-based BCI could help maintain quality of life for people with ALS.
