Pre-movement muscle co-contraction associated with motor performance deterioration under high reward conditions.

Reward usually enhances task performance, but exceptionally large rewards can impede performance due to psychological pressure. In this study, we investigated motor activity changes in high-reward situations and identified indicators for performance decline. Fourteen healthy adults practiced a velocity-dependent right-hand motor task for three days, followed by a test day with varying monetary reward for each trial. Participants were divided into low performers (LPs) and high performers (HPs) according to whether success rate decreased or increased, respectively, on the highest reward trials compared to lower reward trials. Both LPs and HPs demonstrated increased hand velocity during higher reward trials, but only LPs exhibited a significant increase in velocity variance. There was also a negative correlation between the pre-movement co-contraction index (CCI) of the biceps and triceps muscles and success rate on the highest reward trials. This correlation was confirmed in a second experiment with 12 newly recruited participants, suggesting that pre-movement CCI is a marker for performance decline caused by high reward. These findings suggest that interventions to reduce pre-movement CCI such as biofeedback training could be useful for preventing the paradoxical decline in motor performance associated with high rewards.

EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.

Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain-computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI-based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation.

Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent event-related desynchronization (SMR-ERD) while attempting unilateral and movement. The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization. The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closed-loop neurofeedback training.

Two common issues in synchronized multimodal recordings with EEG: Jitter and latency.

Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., muscle activities, eye movement, pupil diameters, or body kinematics data) is ubiquitous in human neuroscience research. However, the precise time alignment of multiple data from heterogeneous sources (i.e., devices) is often arduous due to variable recording parameters of commercially available research devices and complex experimental setups. In this review, we introduced the versatility of a Lab Streaming Layer (LSL)-based application that can overcome two common issues in measuring multimodal data: jitter and latency. We discussed the issues of jitter and latency in multimodal recordings and the benefits of time-synchronization when recording with multiple devices. In addition, a computer simulation was performed to highlight how the millisecond-order jitter readily affects the signal-to-noise ratio of the electrophysiological outcome. Together, we argue that the LSL-based system can be used for research requiring precise time-alignment of datasets. Studies that detect stimulus-induced transient neural responses or test hypotheses regarding temporal relationships of different functional aspects with multimodal data would benefit most from LSL-based systems.

Behavioral and physiological fatigue-related factors influencing timing and force control learning in pianists.

Optimizing the training regimen depending on neuromuscular fatigue is crucial for the well-being of professionals intensively practicing motor skills, such as athletes and musicians, as persistent fatigue can hinder learning and cause neuromuscular injuries. However, accurate assessment of fatigue is challenging because of the dissociation between subjective perception and its impact on motor and cognitive performance. To address this issue, we investigated the interplay between fatigue and learning development in 28 pianists during three hours of auditory-motor training, dividing them into two groups subjected to different resting conditions. Changes in behavior and muscle activity during training were measured to identify potential indicators capable of detecting fatigue before subjective awareness. Our results indicate that motor learning and fatigue development are independent of resting frequency and timing. Learning indices, such as reduction in force and timing errors throughout training, did not differ between the groups. No discernible distinctions emerged in fatigue-related behavioral and physiological indicators between the groups. Regression analysis revealed that several fatigue-related indicators, such as tapping speed variability and electromyogram amplitude per unit force, could explain the learning of timing and force control. Our findings suggest the absence of a universal resting schedule for optimizing auditory-motor learning.

High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing.

Real-time functional imaging of human neural activity and its closed-loop feedback enable voluntary control of targeted brain regions. In particular, a brain-computer interface (BCI), a direct bridge of neural activities and machine actuation is one promising clinical application of neurofeedback. Although a variety of studies reported successful self-regulation of motor cortical activities probed by scalp electroencephalogram (EEG), it remains unclear how neurophysiological, experimental conditions or BCI designs influence variability in BCI learning. Here, we provide the EEG data during using BCIs based on sensorimotor rhythm (SMR), consisting of 4 separate datasets. All EEG data were acquired with a high-density scalp EEG setup containing 128 channels covering the whole head. All participants were instructed to perform motor imagery of right-hand movement as the strategy to control BCIs based on the task-related power attenuation of SMR magnitude, that is event-related desynchronization. This dataset would allow researchers to explore the potential source of variability in BCI learning efficiency and facilitate follow-up studies to test the explicit hypotheses explored by the dataset.

Thirty-minute motor imagery exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of sensorimotor cortices: a double-blind sham-controlled study.

Neurofeedback training using electroencephalogram (EEG)-based brain-computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.

Modulation of sensorimotor oscillation by sensory gating in the frontal cortex.

OBJECTIVES: To reveal that nonprimary motor-related areas located in the upper stream of the sensorimotor area are associated with self-regulated local electroencephalogram changes in the sensorimotor area during motor tasks. METHODS: Among healthy participants, we measured the gating of somatosensory-evoked potentials (SEPs) in nonprimary motor-related areas and the sensorimotor area, and event-related desynchronisation, which reflects the excitability changes of the neurons localised in the sensorimotor area during motor execution and imagery. RESULTS: We confirmed significant correlations between beta-band event-related desynchronisation and the somatosensory-evoked potential gating of frontal N30 during motor imagery and execution (motor imagery: r = 0.723; P < 0.05; motor execution: r = 0.873; P < 0.05), and nonsignificant correlations between beta-band event-related desynchronisation and the somatosensory-evoked potential gating of N20 (motor imagery: r = 0.079; P > 0.05; motor execution: r = 0.449; P > 0.05). CONCLUSIONS: The N30 gating of SEPs, with which the beta-band event-related desynchronisation was associated, implies that they modulate sensory input to the supplementary motor area/premotor cortex during motor tasks, the beta-band self-regulated local electroencephalogram changes in the sensorimotor area related sensory input to the supplementary motor area/premotor cortex, and not to primary sensory area derived from N20 gating. This study suggests that some motor commands are derived from sensory gating in the supplementary motor area/premotor cortex.

Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability.

Human behavior is not performed completely as desired, but is influenced by the inherent rhythmicity of the brain. Here we show that anti-phase bimanual coordination stability is regulated by the dynamics of pre-movement neural oscillations in bi-hemispheric primary motor cortices (M1) and supplementary motor area (SMA). In experiment 1, pre-movement bi-hemispheric M1 phase synchrony in beta-band (M1-M1 phase synchrony) was online estimated from 129-channel scalp electroencephalograms. Anti-phase bimanual tapping preceded by lower M1-M1 phase synchrony exhibited significantly longer duration than tapping preceded by higher M1-M1 phase synchrony. Further, the inter-individual variability of duration was explained by the interaction of pre-movement activities within the motor network; lower M1-M1 phase synchrony and spectral power at SMA were associated with longer duration. The necessity of cortical interaction for anti-phase maintenance was revealed by sham-controlled repetitive transcranial magnetic stimulation over SMA in another experiment. Our results demonstrate that pre-movement cortical oscillatory coupling within the motor network unknowingly influences bimanual coordination performance in humans after consolidation, suggesting the feasibility of augmenting human motor ability by covertly monitoring preparatory neural dynamics.

De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex.

Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition.

Human behavior requires inter-regional crosstalk to employ the sensorimotor processes in the brain. Although external neuromodulation techniques have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We addressed this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable the participants to modulate their bilateral sensorimotor activities. Here, we report that participants learn to up- and down-regulate the ipsilateral excitability to the imagined hand while maintaining constant contralateral excitability; this modulates the magnitude of interhemispheric inhibition (IHI) assessed by the paired-pulse transcranial magnetic stimulation (TMS) paradigm. Further physiological analyses revealed that the manipulation capability of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results show an interesting approach for neuromodulation, which might identify new treatment opportunities, e.g., in patients suffering from a stroke.

Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain.

Concomitant with the development of deep learning, brain-computer interface (BCI) decoding technology has been rapidly evolving. Convolutional neural networks (CNNs), which are generally used as electroencephalography (EEG) classification models, are often deployed in BCI prototypes to improve the estimation accuracy of a participant's brain activity. However, because most BCI models are trained, validated, and tested via within-subject cross-validation and there is no corresponding generalization model, their applicability to unknown participants is not guaranteed. In this study, to facilitate the generalization of BCI model performance to unknown participants, we trained a model comprising multiple layers of residual CNNs and visualized the reasons for BCI classification to reveal the location and timing of neural activities that contribute to classification. Specifically, to develop a BCI that can distinguish between rest, left-hand movement, and right-hand movement tasks with high accuracy, we created multilayers of CNNs, inserted residual networks into the multilayers, and used a larger dataset than in previous studies. The constructed model was analyzed with gradient-class activation mapping (Grad-CAM). We evaluated the developed model via subject cross-validation and found that it achieved significantly improved accuracy (85.69 ± 1.10%) compared with conventional models or without residual networks. Grad-CAM analysis of the classification of cases in which our model produced correct answers showed localized activity near the premotor cortex. These results confirm the effectiveness of inserting residual networks into CNNs for tuning BCI. Further, they suggest that recording EEG signals over the premotor cortex and some other areas contributes to high classification accuracy.

Brain-machine Interface (BMI)-based Neurorehabilitation for Post-stroke Upper Limb Paralysis.

Because recovery from upper limb paralysis after stroke is challenging, compensatory approaches have been the main focus of upper limb rehabilitation. However, based on fundamental and clinical research indicating that the brain has a far greater potential for plastic change than previously thought, functional restorative approaches have become increasingly common. Among such interventions, constraint-induced movement therapy, task-specific training, robotic therapy, neuromuscular electrical stimulation (NMES), mental practice, mirror therapy, and bilateral arm training are recommended in recently published stroke guidelines. For severe upper limb paralysis, however, no effective therapy has yet been established. Against this background, there is growing interest in applying brain-machine interface (BMI) technologies to upper limb rehabilitation. Increasing numbers of randomized controlled trials have demonstrated the effectiveness of BMI neurorehabilitation, and several meta-analyses have shown medium to large effect sizes with BMI therapy. Subgroup analyses indicate higher intervention effects in the subacute group than the chronic group, when using movement attempts as the BMI-training trigger task rather than using motor imagery, and using NMES as the external device compared with using other devices. The Keio BMI team has developed an electroencephalography-based neurorehabilitation system and has published clinical and basic studies demonstrating its effectiveness and neurophysiological mechanisms. For its wider clinical application, the positioning of BMI therapy in upper limb rehabilitation needs to be clarified, BMI needs to be commercialized as an easy-to-use and cost-effective medical device, and training systems for rehabilitation professionals need to be developed. A technological breakthrough enabling selective modulation of neural circuits is also needed.

Treadmill Training for Common Marmoset to Strengthen Corticospinal Connections After Thoracic Contusion Spinal Cord Injury.

Spinal cord injury (SCI) leads to locomotor dysfunction. Locomotor rehabilitation promotes the recovery of stepping ability in lower mammals, but it has limited efficacy in humans with a severe SCI. To explain this discrepancy between different species, a nonhuman primate rehabilitation model with a severe SCI would be useful. In this study, we developed a rehabilitation model of paraplegia caused by a severe traumatic SCI in a nonhuman primate, common marmoset (Callithrix jacchus). The locomotor rating scale for marmosets was developed to accurately assess the recovery of locomotor functions in marmosets. All animals showed flaccid paralysis of the hindlimb after a thoracic contusive SCI, but the trained group showed significant locomotor recovery. Kinematic analysis revealed significantly improved hindlimb stepping patterns in trained marmosets. Furthermore, intracortical microstimulation (ICMS) of the motor cortex evoked the hindlimb muscles in the trained group, suggesting the reconnection between supraspinal input and the lumbosacral network. Because rehabilitation may be combined with regenerative interventions such as medicine or cell therapy, this primate model can be used as a preclinical test of therapies that can be used in human clinical trials.

Preserved intersegmental coordination during locomotion after cervical spinal cord injury in common marmosets.

It is known that primates including human regain some locomotor function after a partial spinal cord injury, but the locomotor pattern is different from before the injury. Although these observations have many implications for improving rehabilitative strategies, these mechanisms are not well understood. In this study, we used a common marmoset hemisection SCI model to examine temporal changes in locomotor pattern, in particular, intersegmental coordination of left hindlimb. Marmoset showed loss of detectable function in the left forelimb and hindlimb after left unilateral hemisection of cervical spinal cord. At two weeks after injury, weight-bearing of the left forelimb during locomotion was limited, but the left hindlimb was able to plantar step. Then marmosets showed gradual recovery in walking ability, but kinematics analysis showed differences in the endpoint trajectory and joint angle movement. Furthermore, intersegmental coordination in left hindlimb represented by planar covariation was preserved over time after the injury. Previous studies have reported that planar covariance is disrupted in patients with stroke or SCI, and that improvement in planarity correlates with recovery in walking ability after rehabilitation. In this study, quadrupedal marmosets were able to walk without loss of balance even after SCI; the different balance needs of bipedal and quadrupedal walkers may lead to differences in planar covariation. Our results show that planar covariation was preserved at all time points after the cervical unilateral hemisection.

Portable rehabilitation system with brain-computer interface for inpatients with acute and subacute stroke: A feasibility study.

The feasibility and safety of brain-computer interface (BCI) systems for patients with acute/subacute stroke have not been established. The aim of this study was to firstly demonstrate the feasibility and safety of a bedside BCI system for inpatients with acute/subacute stroke in a small cohort of inpatients. Four inpatients with early-phase hemiplegic stroke (7-24 days from stroke onset) participated in this study. The portable BCI system showed real-time feedback of sensorimotor rhythms extracted from scalp electroencephalograms (EEGs). Patients attempted to extend the wrist on their affected side, and neuromuscular electrical stimulation was applied only when the system detected significant movement intention-related changes in EEG. Between 120 and 200 training trials per patient were successfully and safely conducted at the bedside over 2-4 days. Our results clearly indicate that the proposed bedside BCI system is feasible and safe. Larger clinical studies are needed to determine the clinical efficacy of the system and its effect size in the population of patients with acute/subacute post-stroke hemiplegia.

Markerless analysis of hindlimb kinematics in spinal cord-injured mice through deep learning.

Rodent models are commonly used to understand the underlying mechanisms of spinal cord injury (SCI). Kinematic analysis, an important technique to measure dysfunction of locomotion after SCI, is generally based on the capture of physical markers placed on bony landmarks. However, marker-based studies face significant experimental hurdles such as labor-intensive manual joint tracking, alteration of natural gait by markers, and skin error from soft tissue movement on the knee joint. Although the pose estimation strategy using deep neural networks can solve some of these issues, it remains unclear whether this method is adaptive to SCI mice with abnormal gait. In the present study, we developed a deep learning based markerless method of 2D kinematic analysis to automatically track joint positions. We found that a relatively small number (< 200) of manually labeled video frames was sufficient to train the network to extract trajectories. The mean test error was on average 3.43 pixels in intact mice and 3.95 pixels in SCI mice, which is comparable to the manual tracking error (3.15 pixels, less than 1 mm). Thereafter, we extracted 30 gait kinematic parameters and found that certain parameters such as step height and maximal hip joint amplitude distinguished intact and SCI locomotion.

Functional reorganization of locomotor kinematic synergies reflects the neuropathology in a mouse model of spinal cord injury.

Spinal cord injury (SCI) disrupts motor commands to modular structures of the spinal cord, limiting the ability to walk. Evidence suggests that these modules are conserved across species from rodent to human and subserve adaptive walking by controlling coordinated joint movements (kinematic synergies). Since SCI causes uncoordinated joint movements of the lower limbs during walking, there may be a disorder of the modular structures that control them. To gain insights into this complex process, we recorded the kinematics of intact and SCI mice when walking on a treadmill and applied principal component analysis to extract kinematic synergies. Most SCI mice walked stably on the treadmill, but their kinematic synergies were generally different from those of intact mice. We classified the kinematic synergies of SCI mice into three groups based on the similarity of the extracted first three synergy components. We found that these three groups had different degrees of spinal cord damage. This suggests that differences in kinematic synergies reflect underlying SCI neuropathology. These results may help guide the development of different rehabilitation approaches and future physiological experiments to understand the mechanisms of motor control and recovery.

Long-term selective stimulation of transplanted neural stem/progenitor cells for spinal cord injury improves locomotor function.

In cell transplantation therapy for spinal cord injury (SCI), grafted human induced pluripotent stem cell-derived neural stem/progenitor cells (hiPSC-NS/PCs) mainly differentiate into neurons, forming synapses in a process similar to neurodevelopment. In the developing nervous system, the activity of immature neurons has an important role in constructing and maintaining new synapses. Thus, we investigate how enhancing the activity of transplanted hiPSC-NS/PCs affects both the transplanted cells themselves and the host tissue. We find that chemogenetic stimulation of hiPSC-derived neural cells enhances cell activity and neuron-to-neuron interactions in vitro. In a rodent model of SCI, consecutive and selective chemogenetic stimulation of transplanted hiPSC-NS/PCs also enhances the expression of synapse-related genes and proteins in surrounding host tissues and prevents atrophy of the injured spinal cord, thereby improving locomotor function. These findings provide a strategy for enhancing activity within the graft to improve the efficacy of cell transplantation therapy for SCI.

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity.

BACKGROUND: Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters. NEW METHOD: We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter. RESULTS: The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal. COMPARISON WITH EXISTING METHODS: Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR. CONCLUSIONS: Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.