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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, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional tasks and the effectiveness of the rehabilitation therapy. The objective of this study was to develop a myoelectric interface that would allow multi-degree-of-freedom control of an exoskeleton involving arm, wrist and hand joints, with an eye toward rehabilitation. We tested the effectiveness of a myoelectric decoder trained with data from one upper limb and mirrored to control a multi-degree-of-freedom exoskeleton with the opposite upper limb (i.e., mirror myoelectric interface) in 10 healthy participants. We demonstrated successful simultaneous control of multiple upper-limb joints by all participants. We showed evidence that subjects learned the mirror myoelectric model within the span of a five-session experiment, as reflected by a significant decrease in the time to execute trials and in the number of failed trials. These results are the necessary precursor to evaluating if a decoder trained with EMG from the healthy limb could foster learning of natural EMG patterns and lead to motor rehabilitation in stroke patients.

Objective. Stroke affects the expression of muscle synergies underlying motor control, most notably in patients with poorer motor function. The majority of studies on muscle synergies have conventionally approached this analysis by assuming alterations in the inner structures of synergies after stroke. Although different synergy-based features based on this assumption have to some extent described pathological mechanisms in post-stroke neuromuscular control, a biomarker that reliably reflects motor function and recovery is still missing. Approach. Based on the theory of muscle synergies, we alternatively hypothesize that functional synergy structures are physically preserved and measure the temporal correlation between the recruitment profiles of healthy modules by paretic and healthy muscles, a feature hereafter reported as the FSRI. We measured clinical scores and extracted the muscle synergies of both ULs of 18 chronic stroke survivors from the electromyographic activity of 8 muscles during bilateral movements before and after 4 weeks of non-invasive BMI controlled robot therapy and physiotherapy. We computed the FSRI as well as features quantifying inter-limb structural differences and evaluated the correlation of these synergy-based measures with clinical scores. Main results. Correlation analysis revealed weak relationships between conventional features describing inter-limb synergy structural differences and motor function. In contrast, FSRI values during specific or combined movement data significantly correlated with UL motor function and recovery scores. Additionally, we observed that BMI-based training with contingent positive proprioceptive feedback led to improved FSRI values during the specific trained finger extension movement. Significance. We demonstrated that FSRI can be used as a reliable physiological biomarker of motor function and recovery in stroke, which can be targeted via BMI-based proprioceptive therapies and adjuvant physiotherapy to boost effective rehabilitation. This study was funded by the Fortüne-Program of the University of Tübingen (2452-0-0/2), the Bundesministerium für Bildung und Forschung (AMORSA (FKZ-16SV7754), REHOME (V5GR2001M1007-01)), EUROSTARS (SubliminalHomeRehab (FKZ: 01QE2023C E! 113928)) and the Basque Government Science Program (SINICTUS (2018222036), MODULA (KK-2019/00018), Elkartek-EXOTEK (KK-2016/00083)). N Irastorza-Landa's work was funded by the Basque Government's scholarship for predoctoral students.

Neuromuscular electrical stimulation (NMES) of the nervous system has been extensively used in neurorehabilitation due to its capacity to engage the muscle fibers, improving muscle tone, and the neural pathways, sending afferent volleys toward the brain. Although different neuroimaging tools suggested the capability of NMES to regulate the excitability of sensorimotor cortex and corticospinal circuits, how the intensity and dose of NMES can neuromodulate the brain oscillatory activity measured with electroencephalography (EEG) is still unknown to date. We quantified the effect of NMES parameters on brain oscillatory activity of 12 healthy participants who underwent stimulation of wrist extensors during rest. Three different NMES intensities were included, two below and one above the individual motor threshold, fixing the stimulation frequency to 35 Hz and the pulse width to 300 μs. Firstly, we efficiently removed stimulation artifacts from the EEG recordings. Secondly, we analyzed the effect of amplitude and dose on the sensorimotor oscillatory activity. On the one hand, we observed a significant NMES intensity-dependent modulation of brain activity, demonstrating the direct effect of afferent receptor recruitment. On the other hand, we described a significant NMES intensity-dependent dose-effect on sensorimotor activity modulation over time, with below-motor-threshold intensities causing cortical inhibition and above-motor-threshold intensities causing cortical facilitation. Our results highlight the relevance of intensity and dose of NMES, and show that these parameters can influence the recruitment of the sensorimotor pathways from the muscle to the brain, which should be carefully considered for the design of novel neuromodulation interventions based on NMES. This study was funded by the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053), AMORSA (FKZ 16SV7754), and the Fortüne-Program of the University of Tübingen (2422-0-1 and 2556-0-0

Neuromuscular electrical stimulation (NMES) of the nervous system has been extensively used in neurorehabilitation due to its capacity to engage the muscle fibers, improving muscle tone, and the neural pathways, sending afferent volleys toward the brain. Although different neuroimaging tools suggested the capability of NMES to regulate the excitability of sensorimotor cortex and corticospinal circuits, how the intensity and dose of NMES can neuromodulate the brain oscillatory activity measured with electroencephalography (EEG) is still unknown to date. We quantified the effect of NMES parameters on brain oscillatory activity of 12 healthy participants who underwent stimulation of wrist extensors during rest. Three different NMES intensities were included, two below and one above the individual motor threshold, fixing the stimulation frequency to 35 Hz and the pulse width to 300 μs. Firstly, we efficiently removed stimulation artifacts from the EEG recordings. Secondly, we analyzed the effect of amplitude and dose on the sensorimotor oscillatory activity. On the one hand, we observed a significant NMES intensity-dependent modulation of brain activity, demonstrating the direct effect of afferent receptor recruitment. On the other hand, we described a significant NMES intensity-dependent dose-effect on sensorimotor activity modulation over time, with below-motor-threshold intensities causing cortical inhibition and above-motor-threshold intensities causing cortical facilitation. Our results highlight the relevance of intensity and dose of NMES, and show that these parameters can influence the recruitment of the sensorimotor pathways from the muscle to the brain, which should be carefully considered for the design of novel neuromodulation interventions based on NMES.

Brain-controlled neuromodulation therapies have emerged as a promising tool to promote functional recovery in patients with motor disabilities. This neuromodulatory strategy is exploited by brain-machine interfaces and could be used for restoring lower limb muscle activity or alleviating gait deficits. Towards a non-invasive approach for leg neurorehabilitation, we present a set-up that combines acquisition of electroencephalographic (EEG) activity to volitionally control trans-spinal magnetic stimulation (ts-MS). We engineered, for the first time, a non-invasive brain-spine interface (BSI) to contingently connect motor cortical activation during leg motor imagery with the activation of leg muscles via ts-MS. This novel brain-controlled stimulation was validated with 10 healthy participants who underwent one session including different ts-MS conditions. After a short screening of their cortical activation during lower limb motor imagery, the participants used the closed-loop system at different stimulation intensities and scored system usability and comfort. We demonstrate the efficiency and robustness of the developed system to remove online stimulation artifacts from EEG regardless of ts-MS intensity used. All the participants reported absence of pain due to ts-MS and good usability. Our results also revealed that ts-MS controlled afferent and efferent intensity-dependent modulation of the nervous system. The here presented system represents a novel non-invasive means to neuromodulate peripheral nerve activity of lower limb using brain-controlled spinal stimulation.

Background. Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients. Objective. Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention. Methods. A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2). Results. The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P = .015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions. Conclusions. BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.

Objective : In light of the shortcomings of current restorative brain–computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements. Methods : We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training. Results : The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher ( p $ 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes. Conclusion : A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size. Significance : This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.

BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies. This study was funded by the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ13GW0053)andAMORSA(FKZ16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the fortüne-Program of the University of Tübingen (2422-0-0 and 2452-0-0), and the Basque GovernmentScienceProgram(EXOTEK:KK2016/00083). NIL was supported by the Basque Government’s scholarship for predoctoral students.

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Objective Freezing phenomena in idiopathic Parkinson’s disease (PD) constitute an important unaddressed therapeutic need. Changes in cortical neurophysiological signatures may precede a single freezing episode and indicate the evolution of abnormal motor network processes. Here, we hypothesize that the movement-related power modulation in the beta-band observed during regular finger tapping, deteriorates in the transition period before upper limb freezing (ULF). Methods We analyzed a 36-channel EEG of 13 patients with PD during self-paced repetitive tapping of the right index finger. In offline analysis, we compared the transition period immediately before ULF (‘transition’) with regular tapping regarding movement-related power modulation and interregional phase synchronization. Results From time-frequency analyses, we observed that the tap cycle related beta-band power modulation over the left sensorimotor area was diminished in the transition period before ULF. Furthermore, increased beta-band power was observed in the transition period compared to regular tapping centered over the left centro-parietal and right frontal areas. Phase synchronization between the left fronto-parietal areas and the left sensorimotor area was elevated during transition compared to regular tapping. Conclusion Together, these results indicate that diminished beta band power modulation and increased phase synchronization precede ULF. Significance We demonstrate that pathological cortical motor processing is present in the transition phase from regular tapping to an ULF episode.

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.

Magnetic stimulation using commercial transcranial magnetic stimulators (TMS) and coils is becoming an established tool for neurostimulation. However, when applied at the lumbar region it is not clear which neural structures are stimulated and especially, if the spinal cord (SC) can be stimulated. Computational modeling with realistic human body models is a promising tool to understand better the basic mechanisms of the stimulation. In this study we have used a realistic model to calculate the current density (J) distribution and magnitude under different output power levels of a commercial stimulator to describe the electromagnetic effects on the different tissues. Our results suggest that spinal cord stimulation with TMS is possible. However, significant muscle contraction is produced due to the high stimulation needed, which might make this stimulation non-practical. The spatial resolution of this technology is very poor to stimulate specific parts of the SC only. Although the stimulation aims at SC structures, we observed that most of the current does not reach the SC, but the cerebrospinal fluid (CSF). All together, these results represent a first step towards understanding and optimizing magnetic transpinal stimulation.

Electroencephalography (EEG)-based brain-machine interfaces (BMI) have been proven effective for motor rehabilitation of severely paralyzed patients. The brain activity is classified and translated into a go vs no-go feedback (i.e., mobilizing, or not, the paralyzed limb). Patients performing the same movements but unrelated to their brain activity showed poorer or no recovery, which suggests that an accurate feedback expedites motor recovery. Being able to decode different movements from the EEG would allow providing a more accurate feedback, maximizing the rehabilitative potential. However, a dynamic rehabilitative environment with different types of movements would likely be accompanied by involuntary motions with the eyes and the head, which can contaminate the measured EEG signals. In this study we analyze how external movements associated with the task (i.e., eye or head movements) influence the performance of an EEG-based decoder of reaching movements. Our results reveal that different reaching directions could only be decoded when eye and head movements occur and only using low frequency features (delta band). In summary, this paper highlights the importance of carefully designing protocols to avoid eye and head movements to contaminate EEG signals.

Modern approaches to motor rehabilitation of severe upper limb paralysis in chronic stroke decode movements from electromyography for controlling rehabilitation orthoses. Muscle fatigue is a phenomenon that influences these neurophysiological signals and may diminish the decoding quality. Characterization of these potential signal changes during movement patterns of rehabilitation training could therefore help improve the decoding accuracy. In the present work we investigated how electromyographic indices of muscle fatigue in the Deltoid Anterior muscle evolve during typical forward reaching movements of a rehabilitation training in healthy subjects and a stroke patient. We found that muscle fatigue in healthy subjects changed the neurophysiological signal. In the patient, however, no consistent change was observed over several sessions.

Current brain-machine-interface (BMI) rehabilitation approaches typically focus on a specific aspect of neural activity. Auxiliary signals, derived from independent measures of neural activity and recorded in parallel might be useful in quantifying and tracking a subjects mental state and performance. In this work, we demonstrate that event-related potentials can be reliably observed in stroke survivors with chronic paralysis during a BMI intervention. The averaged evoked response remains stable over sessions and varies between subjects. A prominent negativity, positivity complex emerges whose features can be tracked across subjects and sessions.

During the last decades, spinal cord stimulation (SCS) has attracted much attention due to its capability to modulate the motor and sensory networks. The potential of this technique has been proved, and several investigations have focused on applying it for restoring lower limb function. The majority of SCS approaches are based on electrical stimulation, and few studies have explored magnetic fields for non-invasive SCS. This paper presents a trans-spinal magnetic stimulation (ts-MS) protocol and studies its effects on spinal circuits with seven healthy subjects, considering central and peripheral nervous systems. Motor evoked potentials (MEP) and trans-spinal motor evoked potentials (ts-MEP) were assessed before and after the ts-MS intervention to characterize excitatory responses. After the intervention, we found an increase of almost 30% (not statistically significant) in MEP amplitude, but no changes in ts-MEP amplitude. Further research is required to confirm, in a larger population of subjects, the potential of this technology, which could be used to improve rehabilitation therapies for patients with motor disabilities.

Hybrid brain-machine interfaces (BMIs) combining brain and muscle activity are a promising therapeutic alternative for rehabilitation of stroke patients with severe paralysis. In this study, we compare different approaches utilizing electroencephalographic (EEG) and electromyographic (EMG) activity to detect movement attempts of stroke patients with complete hand paralysis. Data of 20 patients with a chronic stroke involving the motor cortex were analyzed, and the performance of EEG-based, EMG-based or hybrid classifiers were simulated offline. We show that the combination of EEG and EMG improves the accuracy of movement detection, but that muscles unrelated to the task can also provide high accuracies, reflecting compensatory mechanisms. This result underscores the importance of appropriate designs of hybrid BMIs to maximize their rehabilitative potential.

Purpose Assessment of temporal and spatial relations between spontaneous mechanical activities in musculature (SMAM) at rest as revealed by diffusion-weighted imaging (DWI) and electrical muscular activities in surface EMG (sEMG). Potential influences of static and radiofrequency magnetic fields on muscular activity on sEMG measurements at rest were examined systematically. Methods Series of diffusion-weighted stimulated echo planar imaging were recorded with concurrent sEMG measurements. Electrical activities in sEMG were analyzed by non-parametric Friedman and two-sample Kolmogorov–Smirnov test. Direct correlation of both modalities was investigated by temporal mapping of electrical activity in sEMG to DWI repetition interval. Results Electrical activities in sEMG and number of visible SMAMs in DWI showed a strong correlation (ρ = 0.9718). High accordance between sEMG activities and visible SMAMs in DWI in a near-surface region around sEMG electrodes was achieved. Characteristics of sEMG activities were almost similar under varying magnetic field conditions. Conclusion Visible SMAMs in DWI have shown a close and direct relation to concurrent signals recorded by sEMG. MR-related magnetic fields had no significant effects on findings in sEMG. Hence, appearance of SMAMs in DWI should not be considered as imaging artifact or as effects originating from the special conditions of MR examinations. Spatial and temporal distributions of SMAMs indicate characteristics of spontaneous (microscopic) mechanical muscular action at rest. Therefore, DWI techniques should be considered as non-invasive tools for studying physiology and pathophysiology of spontaneous activities in resting muscle. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

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 stroke rehabilitation, they do not address this issue, which might result in inefficient interventions. Here, we present a novel paradigm that relies on the correction of the pathological muscle activity as a way to elicit rehabilitation, even in patients with complete paralysis. Previous studies demonstrated that there are no substantial inter-limb differences in the muscle synergy organization of healthy individuals. We propose building a subject-specific model of muscle activity from the healthy limb and mirroring it to use it as a learning tool for the patient to reproduce the same healthy myoelectric patterns on the paretic limb during functional task training. Here, we aim at understanding how this myoelectric model, which translates muscle activity into continuous movements of a 7-degree of freedom upper limb exoskeleton, could transfer between sessions, arms and tasks. The experiments with 8 healthy individuals and 2 chronic stroke patients proved the feasibility and effectiveness of such myoelectric interface. We anticipate the proposed method to become an efficient strategy for the correction of maladaptive muscle activity and the rehabilitation of stroke patients. This study was funded by the Baden-Württemberg Stiftung (GRUENS ROB-1), the Deutsche Forschungsgemeinschaft (DFG, Koselleck), the Fortüne-Program of the University of Tübingen (2422-0-0), and the Bundes Ministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053), AMORSA (FKZ 16SV7754), Gipuzkoa Regional Government (INKRATEK), Ministry of Science of the Basque Country (Elkartek: EXOTEK). A. Sarasola-Sanz’s work was supported by La Caixa-DAAD scholarship and N. Irastorza-Landa’s work by the Basque Government and IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.

Dynamic modulations of large-scale network activity and synchronization are inherent to a broad spectrum of cognitive processes and are disturbed in neuropsychiatric conditions including Parkinson's disease. Here, we set out to address the motor network activity and synchronization in Parkinson's disease and its modulation with subthalamic stimulation. To this end, 20 patients with idiopathic Parkinson's disease with subthalamic nucleus stimulation were analysed on externally cued right hand finger movements with 1.5-s interstimulus interval. Simultaneous recordings were obtained from electromyography on antagonistic muscles (right flexor digitorum and extensor digitorum) together with 64-channel electroencephalography. Time-frequency event-related spectral perturbations were assessed to determine cortical and muscular activity. Next, cross-spectra in the time-frequency domain were analysed to explore the cortico-cortical synchronization. The time-frequency modulations enabled us to select a time-frequency range relevant for motor processing. On these time-frequency windows, we developed an extension of the phase synchronization index to quantify the global cortico-cortical synchronization and to obtain topographic differentiations of distinct electrode sites with respect to their contributions to the global phase synchronization index. The spectral measures were used to predict clinical and reaction time outcome using regression analysis. We found that movement-related desynchronization of cortical activity in the upper alpha and beta range was significantly facilitated with 'stimulation on' compared to 'stimulation off' on electrodes over the bilateral parietal, sensorimotor, premotor, supplementary-motor, and prefrontal areas, including the bilateral inferior prefrontal areas. These spectral modulations enabled us to predict both clinical and reaction time improvement from subthalamic stimulation. With 'stimulation on', interhemispheric cortico-cortical coherence in the beta band was significantly attenuated over the bilateral sensorimotor areas. Similarly, the global cortico-cortical phase synchronization was attenuated, and the topographic differentiation revealed stronger desynchronization over the (ipsilateral) right-hemispheric prefrontal, premotor and sensorimotor areas compared to 'stimulation off'. We further demonstrated that the cortico-cortical phase synchronization was largely dominated by genuine neuronal coupling. The clinical improvement with 'stimulation on' compared to 'stimulation off' could be predicted from this cortical decoupling with multiple regressions, and the reduction of synchronization over the right prefrontal area showed a linear univariate correlation with clinical improvement. Our study demonstrates wide-spread activity and synchronization modulations of the cortical motor network, and highlights subthalamic stimulation as a network-modulating therapy. Accordingly, subthalamic stimulation may release bilateral cortical computational resources by facilitating movement-related desynchronization. Moreover, the subthalamic nucleus is critical to balance inhibitory and facilitatory cortical players within the motor program.

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 and induce neuroplasticity. Several offline and online studies have already demonstrated that discriminable information related to movement can be decoded from low-frequency EEG activity. However, there is still not a well-established procedure to guarantee that this activity is optimally filtered from the background noise. This work compares different configurations of non-causal (i.e., offline) and causal (i.e., online) filters to classify movement-related cortical potentials (MRCP) with six healthy subjects during reaching movements. Our results reveal important differences in MRCP decoding accuracy dependent on the selected frequency band for both offline and online approaches. In summary, this paper underlines the importance of optimally choosing filter parameters, since their variable response has an impact on the classification of low EEG frequencies for BMI.

Recent studies have shown the feasibility of spinal cord stimulation (SCS) for motor rehabilitation. Currently, there is an increasing interest in developing closed-loop systems employing SCS for lower-limb recovery. These closed-loop systems are based on the use of neurophysiological signals to modulate the stimulation. It is known that electromagnetic stimulation can introduce undesirable noise to the electrophysiological recordings. However, there is little evidence about how electroencephalographic (EEG) or electromyographic (EMG) activities are corrupted when a trans-spinal magnetic stimulation is applied. This paper studies the effects of magnetic SCS in EEG and EMG activity. Furthermore, a median filter is proposed to ameliorate the effects of the artifacts, and to preserve the neural activity. Our results show that SCS can affect both EEG and EMG, and that, while the median filter works well to clean the EEG activity, it did not improve the contaminations of the EMG activity. The obtained results underline the need of cleaning EMG and EEG signals contaminated by SCS, which is essential for optimal closed-loop rehabilitation.

Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements. This paper evaluates the effects of removing artifacts from the data used to train a BMI decoder on a dataset of 28 severely paralyzed stroke patients. We show that cleaning the training datasets reduces the global BMI performance for decoding attempts of movement. Further, we demonstrate that this performance drop especially affects the test trials contaminated by artifacts (i.e., trials that might not reflect cortical activity but noise), but not the clean test trials (i.e., trials representing correct cortical activity). This paper underlines the importance of cleaning the datasets used to train BMI systems to improve their efficacy for decoding movement intention and maximize their neurorehabilitative potential.

Recent studies have demonstrated the efficacy of brain-machine interfaces (BMI) for motor rehabilitation after stroke, especially for those patients with severe paralysis. However, a cerebro-vascular accident can affect the brain in many different manners, and lesions in diverse areas, even from significantly different volumes, can lead to similar or equal motor deficits. The location of the insult influences the way the brain activates when moving or attempting to move a paralyzed limb. Since the essence of a rehabilitative BMI is to precisely decode motor commands from the brain, it is crucial to characterize how lesion location affects the measured signals and if and how it influences BMI performance. This paper compares the performances of an electroencephalography (EEG)-based movement intention decoder in two groups of severely paralyzed chronic stroke patients: 14 with subcortical lesions and 14 with mixed (i.e., cortical and subcortical) lesions. We show that the lesion location influences the performance of the BMI when decoding the movement attempts of the paretic arm. The obtained results underline the need for further developments for a better individualization of BMI-based rehabilitative therapies for stroke patients.

Myoelectric control of rehabilitation devices engages active recruitment of muscles for motor task accomplishment, which has been proven to be essential in motor rehabilitation. Unfortunately, most electromyographic (EMG) activity-based controls are limited to one single degree-of-freedom (DoF), not permitting multi-joint functional tasks. On the other hand, discrete EMG-triggered approaches fail to provide continuous feedback about muscle recruitment during movement. For such purposes, myoelectric interfaces for continuous recognition of functional movements are necessary. Here we recorded EMG activity using 5 bipolar electrodes placed on the upper-arm in 8 healthy participants while they performed reaching movements in 8 different directions. A pseudo on-line system was developed to continuously predict movement intention and attempted arm direction. We evaluated two hierarchical classification approaches. Movement intention detection triggered different movement direction classifiers (4 or 8 classes) that were trained and tested over a 5-fold cross validation. We also investigated the effect of 3 different window lengths to extract EMG features on classification. We obtained classification accuracies above 70% for both hierarchical approaches. These results highlight the viability of classifying online 8 upper-arm different directions using surface EMG activity of 5 muscles and represent a first step towards an online EMG-based control for rehabilitation devices.

Objective Regardless of the paradigm used to implement a brain–computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. Methods EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. Results Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. Conclusion Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. Significance This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).

OBJECTIVE: Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study. METHODS: Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention. RESULTS: A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. INTERPRETATION: The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation.

Brain machine interfaces (BMIs) have previously been utilized to control rehabilitation robots with promising results. The design and development of more dexterous and user-friendly rehabilitation platforms is the next challenge to be tackled. We built a novel platform that uses an electro-encephalograpy-based BMI to control a multi-degree of freedom exoskeleton in a rehabilitation framework. Its applicability to a clinical scenario is validated here with six healthy subjects and a chronic stroke patient using motor imagery and movements attempts. Therefore, this study presents a potential system to carry out fully-featured motor rehabilitation therapies.

Myoelectric control constitutes a promising interface for robot-aided motor rehabilitation therapies. The development of accurate classifiers and suitable training protocols for this purpose are still challenging. In this study, eight healthy participants underwent electromyography (EMG) recordings while they performed reaching movements in four directions and five different hand movements wearing an exoskeleton on their right upper-limb. We developed an offline classifier based on a back-propagation artificial neural network (ANN) trained with the waveform length as time-domain feature extracted from EMG signals to classify discrete movements. A maximum overall classification performance of 75.54 % ± 5.17 and 67.37 % ± 8.75 were achieved for reaching and hand movements, respectively. We demonstrated that similar or better classification results could be achieved using a small number of electrodes placed over the main muscles involved in the movement instead of a large set of electrodes. This work is a first step towards a discrete decoding-based myoelectric control for a motor rehabilitation exoskeleton.

Goal: Stroke survivors usually require motor rehabilitation therapy as, due to the lesion, they completely or partially loss mobility in the limbs. Brain–computer interface technology offers the possibility of decoding the attempt to move paretic limbs in real time to improve existing motor rehabilitation. However, a major difficulty for the practical application of the BCI to stroke survivors is that the brain rhythms that encode the motor states might be diminished due to the lesion. This study investigates the continuous decoding of natural attempt to move the paralyzed upper limb in stroke survivors from electroencephalographic signals of the unaffected contralesional motor cortex. Results: Experiments were carried out with the aid of six severely affected chronic stroke patients performing/attempting self-selected reaching movements of the unaffected/affected upper limb. The electroencephalographic (EEG) analysis showed significant cortical activation on the uninjured motor cortex when moving the contralateral unaffected arm and in the attempt to move the ipsilateral affected arm. Using this activity, significant continuous decoding of movement was obtained in six out of six participants in movements of the unaffected limb, and in four out of six participants in the attempt to move the affected limb. Conclusion: This study showed that it is possible to construct a decoder of the attempt to move the paretic arm for chronic stroke patients using the EEG activity of the healthy contralesional motor cortex. Significance: This decoding model could provide to stroke survivors with a natural, easy, and intuitive way to achieve control of BCIs or robot-assisted rehabilitation devices.

Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI–FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.

In recent years, there has been an increasing interest in using electroencephalographic (EEG) activity to close the loop between brain oscillations and movement to induce functional motor rehabilitation. Rehabilitation robots or exoskeletons have been controlled using EEG activity. However, all studies have used a 2-class or one-dimensional decoding scheme. In this study we investigated EEG decoding of 5 functional movements of the same limb towards an online scenario. Six healthy participants performed a three-dimensional center-out reaching task based on direction movements (four directions and rest) wearing a 32-channel EEG cap. A BCI design based on multiclass extensions of Spectrally Weighted Common Spatial Patterns (Spec-CSP) and a linear discriminant analysis (LDA) classifier was developed and tested offline. The decoding accuracy was 5-fold cross-validated. A decoding accuracy of 39.5% on average for all the six subjects was obtained (chance level being 20%). The results of the current study demonstrate multiple functional movements decoding (significantly higher than chance level) from the same limb using EEG data. This study represents first steps towards a same limb multi degree of freedom (DOF) online EEG based BCI for motor restoration.

Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery.

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, the development of a dexterous EMG-based control interface including multiple degrees of freedom (DOFs) of the upper limb still remains a challenge. Another persistent issue in surface myoelectric control is the non-stationarity of EMG signals across sessions. In this work, the decoding of 7 distal and proximal DOFs' kinematics during coordinated upper-arm, fore-arm and hand movements was performed. The influence of the EMG non-stationarity was tested by training a continuous EMG decoder in three different scenarios. Moreover, the generalization characteristics of two algorithms (ridge regression and Kalman filter) were compared in the aforementioned scenarios. Eight healthy participants underwent EMG and kinematics recordings while performing three functional tasks. We demonstrated that ridge regression significantly outperformed the Kalman filter, indicating a superior generalization ability. Furthermore, we proved that the performance drop caused by the session-to-session non-stationarities could be significantly mitigated by including a short re-calibration phase. Although further tests should be performed, these preliminary findings constitute a step forward towards the non-invasive control of the next generation of upper limb rehabilitation robotics.

In this work, we used single electrooculogram (EOG) signal to perform automatic sleep scoring. Deep belief network (DBN) and combination of DBN and Hidden Markov Models (HMM) are employed to discriminate sleep stages. Under the leave-one-out protocol, the average accuracy of DBN and DBN-HMM are 77.7% and 83.3% for all sleep stages, respectively. On the other hand, we found the EOG signal not only contribute to identify stages of Awake and rapid eye movement, also contribute to discriminate stage 2 and slow wave sleep stage.

Noninvasive brain-computer-interfaces (BCI) coupled with prosthetic devices were recently introduced in the rehabilitation of chronic stroke and other disorders of the motor system. These BCI systems and motor rehabilitation in general involve several motor tasks for training. This study investigates the neurophysiological bases of an EEG-oscillation-driven BCI combined with a neuroprosthetic device to define the specific oscillatory signature of the BCI task. Controlling movements of a hand robotic orthosis with motor imagery of the same movement generates sensorimotor rhythm oscillation changes and involves three elements of tasks also used in stroke motor rehabilitation: passive and active movement, motor imagery, and motor intention. We recorded EEG while nine healthy participants performed five different motor tasks consisting of closing and opening of the hand as follows: 1) motor imagery without any external feedback and without overt hand movement, 2) motor imagery that moves the orthosis proportional to the produced brain oscillation change with online proprioceptive and visual feedback of the hand moving through a neuroprosthetic device (BCI condition), 3) passive and 4) active movement of the hand with feedback (seeing and feeling the hand moving), and 5) rest. During the BCI condition, participants received contingent online feedback of the decrease of power of the sensorimotor rhythm, which induced orthosis movement and therefore proprioceptive and visual information from the moving hand. We analyzed brain activity during the five conditions using time-frequency domain bootstrap-based statistical comparisons and Morlet transforms. Activity during rest was used as a reference. Significant contralateral and ipsilateral event-related desynchronization of sensorimotor rhythm was present during all motor tasks, largest in contralateral-postcentral, medio-central, and ipsilateral-precentral areas identifying the ipsilateral precentral cortex as an integral part of motor regulation. Changes in task-specific frequency power compared with rest were similar between motor tasks, and only significant differences in the time course and some narrow specific frequency bands were observed between motor tasks. We identified EEG features representing active and passive proprioception (with and without muscle contraction) and active intention and passive involvement (with and without voluntary effort) differentiating brain oscillations during motor tasks that could substantially support the design of novel motor BCI-based rehabilitation therapies. The BCI task induced significantly different brain activity compared with the other motor tasks, indicating neural processes unique to the use of body actuators control in a BCI context.

Locked-in syndrome (LIS) as a result of brainstem lesions or progressive neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS), is a severe medical condition in which a person is fully conscious but unable to move or talk. LIS can transition into complete locked-in syndrome (CLIS) in which residual abilities to communicate through muscle twitches are entirely lost. It is unknown how CLIS affects circadian rhythm and sleep/wake patterns. Here we report a 39-year-old ALS patient who transitioned from LIS to CLIS while brain activity was continuously recorded using electrocorticography (ECoG) over one month. While we found no circadian rhythm in heart rate and body temperature, transition into CLIS was associated with increased fragmentation of slow wave sleep (SWS) across the day. Total time in SWS did not change. SWS fragmentation might reflect progressive circadian system impairment and should be considered as a factor further limiting communication capabilities in these patients.

Contains fulltext : 135633.pdf (Publisher’s version ) (Open Access) Objective: Stroke is a leading cause of long-term motor disability. Stroke patients with severe hand weakness do not profit from rehabilitative treatments. Recently, brain-controlled robotics and sequential functional electrical stimulation allowed some improvement. However, for such therapies to succeed, it is required to decode patients' intentions for different arm movements. Here, we evaluated whether residual muscle activity could be used to predict movements from paralyzed joints in severely impaired chronic stroke patients. Methods: Muscle activity was recorded with surface-electromyography (EMG) in 41 patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores of 2.93 ± 2.7), in order to decode their intention to perform six different motions of the affected arm, required for voluntary muscle activity and to control neuroprostheses. Decoding of paretic and nonparetic muscle activity was performed using a feed-forward neural network classifier. The contribution of each muscle to the intended movement was determined. Results: Decoding of up to six arm movements was accurate (>65%) in more than 97% of nonparetic and 46% of paretic muscles. Interpretation: These results demonstrate that some level of neuronal innervation to the paretic muscle remains preserved and can be used to implement neurorehabilitative treatments in 46% of patients with severe paralysis and extensive cortical and/or subcortical lesions. Such decoding may allow these patients for the first time after stroke to control different motions of arm prostheses through muscle-triggered rehabilitative treatments. 11 p.

Objective. Brain–computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients. This study was funded by the Baden-Württemberg Stiftung (GRUENS), the Deutsche Forschungsgemeinschaft (DFG, Koselleck and SP-1533/2-1), Bundes Ministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053), the fortune-Program of the University of Tübingen (2422-0-0), and AMORSA (FKZ 16SV7754). A Sarasola-Sanz’s work is supported by the La Caixa-DAAD scholarship, and N IrastorzaLanda’s work by the Basque Government and IKERBASQUE, Basque Foundation for Science.

Objective. Recently, there have been several approaches to utilize a brain–computer interface (BCI) for rehabilitation with stroke patients or as an assistive device for the paralyzed. In this study we investigated whether up to seven different hand movement intentions can be decoded from epidural electrocorticography (ECoG) in chronic stroke patients. Approach. In a screening session we recorded epidural ECoG data over the ipsilesional motor cortex from four chronic stroke patients who had no residual hand movement. Data was analyzed offline using a support vector machine (SVM) to decode different movement intentions. Main results. We showed that up to seven hand movement intentions can be decoded with an average accuracy of 61% (chance level 15.6%). When reducing the number of classes, average accuracies up to 88% can be achieved for decoding three different movement intentions. Significance. The findings suggest that ipsilesional epidural ECoG can be used as a viable control signal for BCI-driven neuroprosthesis. Although patients showed no sign of residual hand movement, brain activity at the ipsilesional motor cortex still shows enough intention-related activity to decode different movement intentions with sufficient accuracy.

Movement-related slow cortical potentials (SCPs) are proposed as reliable and immediate indicators of cortical reorganization in motor learning. SCP amplitude and latency have been reported as markers for the brain's computational effort, attention and movement planning. SCPs have been used as an EEG signature of motor control and as a main feature in Brain-Machine-Interfaces (BMIs). Some reports suggest SCPs are modified following stroke. In this study, we investigated movement-related SCPs in severe chronic stroke patients with no residual paretic hand movements preceding and during paretic (when they try to move) and healthy hand movements. The aim was to identify SCP signatures related to cortex integrity and complete paralysis due to stroke in the chronic stage. Twenty severely impaired (no residual finger extension) chronic stoke patients, of whom ten presented subcortical and ten cortical and subcortical lesions, underwent EEG and EMG recordings during a cue triggered hand movement (open/close) paradigm. SCP onset appeared and peaked significantly earlier during paretic hand movements than during healthy hand movements. Amplitudes were significantly larger over the midline (Cz, Fz) for paretic hand movements while contralateral (C4, F4) and midline (Cz, Fz) amplitudes were significantly larger than ipsilateral activity for healthy hand movements. Dividing the participants into subcortical only and mixed lesioned patient groups, no significant differences observed in SCP amplitude and latency between groups. This suggests lesions in the thalamocortical loop as the main factor in SCP changes after stroke. Furthermore, we demonstrated how, after long-term complete paralysis, post-stroke intention to move a paralyzed hand resulted in longer and larger SCPs originating in the frontal areas. These results suggest SCP are a valuable feature that should be incorporated in the design of new neurofeedback strategies for motor neurorehabilitation.

This study tested two hypotheses: (1) compared with amateurs and young elite, expert table tennis players are characterized by enhanced cortical activation in the motor and fronto-parietal cortex during motor imagery in response to table tennis videos; (2) in elite athletes, world rank points are associated with stronger cortical activation. To this aim, electroencephalographic data were recorded in 14 expert, 15 amateur and 15 young elite right-handed table tennis players. All subjects watched videos of a serve and imagined themselves responding with a specific table tennis stroke. With reference to a baseline period, power decrease/increase of the sensorimotor rhythm (SMR) during the pretask- and task period indexed the cortical activation/deactivation (event-related desynchronization/synchronization, ERD/ERS). Regarding hypothesis (1), 8–10 Hz SMR ERD was stronger in elite athletes than in amateurs with an intermediate ERD in young elite athletes in the motor cortex. Regarding hypothesis (2), there was no correlation between ERD/ERS in the motor cortex and world rank points in elite experts, but a weaker ERD in the fronto-parietal cortex was associated with higher world rank points. These results suggest that motor skill in table tennis is associated with focused excitability of the motor cortex during reaction, movement planning and execution with high attentional demands. Among elite experts, less activation of the fronto-parietal attention network may be necessary to become a world champion.

Background Standard assessment instruments cannot differentiate patients with minimal residual hand function after stroke. As a result, changes in motor recovery are difficult to document using currently-available tests. In a controlled study with chronic stroke patients without residual finger extension, a new hand function test has been developed. This instrument, called Broetz Hand Test (BzH), allows to assess small variations in hand function in severely paralyzed stoke patients. The instrument is easy to use, and was developed using principles of motor learning and behavioral assessment. Methods The instrument consists of seven daily life-oriented tasks, each of which asks for movement of the paralyzed hand. BzH of 20 patients after stroke was evaluated before and after a behavioral physiotherapy treatment. Sensitivity, inter-observer reliability, test-retest reliability and construct validity was calculated. Results Two-tailed paired-samples t-test before and after treatment demonstrated sufficient sensitivity. Mean agreement between the raters resulted in an excellent interrater-reliability. Test-retest reliability between the pre- and post-treatment scores was 0.9. The correlation between BzH and standard test scores was statistically significant and demonstrated sufficient validity. Conclusion The BzH is a valid and reliable tool to assess changes in hand function in severely paralyzed patients after stroke.

Movement related cortical potentials (MRCPs) have been studied for many years and proposed as reliable and immediate indicators of cortical reorganizations in motor learning and after stroke. It has been reported that decrease in amplitude and later onset of MRCPs reflect less mental effort and shorter planning time during a motor task. In this study MRCPs preceding hand movements in severe chronic stroke were investigated in an EEG screening paradigm (patients performed hand open and close for paretic and healthy hand) before and after a one-month online-EEG-Brain-Machine-Interface neurorehabilitation intervention coupled with physiotherapy. Five severely impaired (no residual finger extension) chronic stoke patients were enrolled in the study. We observed that MRCPs peak amplitude over Cz during paretic hand movement attempts decreased significantly after compared to before intervention. Furthermore, MRCP onset was significantly later over central regions during paretic hand movements after compared to before intervention. There were no significant pre-post differences during healthy hand movements. Our results suggest that our patients needed less mental effort and shorter planning time after intervention. We demonstrated for the first time significant MRCP changes after a neurorehabilitation intervention (BMI + physiotherapy) in severe chronic stroke patients.

Stroke is the main cause of hemiparesis in developed countries. Very often upper limbs are compromised and the hemiparesis is characterized by abnormal muscle activations especially at the level of the wrist and fingers (distal muscles). In this study we investigated the stability and strength of paretic upper limb muscle activity during different bilateral movements eliciting different postures and muscle recruitment. We recorded surface EMG of 45 severe chronic stroke patients without residual finger extension during six bimanual movements. Sixteen bipolar-EMG electrodes were placed on both upper limbs at the level of proximal and distal areas. We extracted the waveform length from the EMG data, in order to investigate muscle activity level at the paralyzed muscles during all movements. Our results indicated that different positions during multi-joint movements of the upper limb facilitated the contraction of the affected muscles (forearm extensors) involuntarily during the movement in which this activation was not expected (e.g. abduction of the upper arm) in more than 64% of the patients. Here, we show that severe affected chronic stroke patients can induce a higher activation of the paretic muscles of the forearm by changing the upper limb posture. This might be an important hint to design multi-joint coordinated movements involving proximal and distal musculature for stroke motor rehabilitation.