Motor Execution Research Articles

Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study.

Motor imagery-based brain-computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.

When Corticospinal Inhibition Favors an Efficient Motor Response

Many daily activities involve responding to the actions of other people. However, the functional relationship between the motor preparation and execution phases still needs to be clarified. With the combination of different and complementary experimental techniques (i.e., motor excitability measures, reaction times, electromyography, and dyadic 3-D kinematics), we investigated the behavioral and neurophysiological signatures characterizing different stages of a motor response in contexts calling for an interactive action. Participants were requested to perform an action (i.e., stirring coffee or lifting a coffee cup) following a co-experimenter's request gesture. Another condition, in which a non-interactive gesture was used, was also included. Greater corticospinal inhibition was found when participants prepared their motor response after observing an interactive request, compared to a non-interactive gesture. This, in turn, was associated with faster and more efficient action execution in kinematic terms (i.e., a social motor priming effect). Our results provide new insights on the inhibitory and facilitatory drives guiding social motor response generation. Altogether, the integration of behavioral and neurophysiological indexes allowed us to demonstrate that a more efficient action execution followed a greater corticospinal inhibition. These indexes provide a full picture of motor activity at both planning and execution stages.

Recognition of unilateral lower limb movement based on EEG signals with ERP-PCA analysis

It has been confirmed that motor imagery (MI) and motor execution (ME) share a subset of mechanisms underlying motor cognition. In contrast to the well-studied laterality of upper limb movement, the laterality hypothesis of lower limb movement also exists, but it needs to be characterized by further investigation. This study used electroencephalographic (EEG) recordings of 27 subjects to compare the effects of bilateral lower limb movement in the MI and ME paradigms. Event-related potential (ERP) recorded was decomposed into meaningful and useful representatives of the electrophysiological components, such as N100 and P300. Principal components analysis (PCA) was used to trace the characteristics of ERP components temporally and spatially, respectively. The hypothesis of this study is that the functional opposition of unilateral lower limbs of MI and ME should be reflected in the different alterations of the spatial distribution of lateralized activity. Meanwhile, the significant ERP-PCA components of the EEG signals as identifiable feature sets were applied with support vector machine to identify left and right lower limb movement tasks. The average classification accuracy over all subjects is up to 61.85% for MI and 62.94% for ME. The proportion of subjects with significant results are 51.85% for MI and 59.26% for ME, respectively. Therefore, a potential new classification model for lower limb movement can be applied on brain computer interface (BCI) systems in the future.

Dealing with uncertainty in sensorimotor control in sports: A systematic review

Introduction
 In complex sensorimotor behavior, uncertainty arises from ambiguity in the sensory inputs of the environment (e.g. Kersten et al., 2004) as well as from noise in sensory detection and motor execution (e.g. Todorov & Jordan, 2002). Moreover, the observable sensory inputs are delayed and there are typically multiple solutions to solve a motor task (e.g. Franklin & Wolpert, 2011). In sports, where human capacity reaches its limit with these challenges, dealing with uncertainty is crucial. Thus, the question arises how humans are able to deal with uncertainty in sensorimotor control in naturalistic situations such as sports.
 Methods
 A systematic search for original articles was conducted in six scientific databases with the following terms: (uncertainty OR noise) AND (sensor* OR motor) AND control AND sport AND movement. After independently screening 4,309 articles and additional reference lists by two raters, 70 articles remained for the review.
 Results
 There is clear evidence that prior knowledge (or similar contextual information) and multiple sensory information affect perception, action and performance. Moreover, there is considerable evidence that prior knowledge and multiple sensory information get integrated according to their reliability in order to reduce uncertainty. Furthermore, it has been reported that gaze behavior is close to the optimal compared to an optimal Bayesian observer in an expected and unexpected uncertain environment. There is also considerable evidence that not all variables have to be controlled and rather uncertainty is accepted in variables that do not impair overall performance. Some findings show that inherent noise is taken into account when planning motor execution so that the potential costs and rewards of the movement outcome are optimized. Finally, some findings show that unforeseen perturbation can be absorbed by means of higher muscle stiffness resulting from muscular co-contraction.
 Discussion
 The strategies to deal with uncertainty mentioned in the results section can be summarized under the umbrella of Bayesian decision theory (Franklin & Wolpert, 2011). Only a few alternative models were referenced that, however, were conceptually quite similar to Bayesian models. Therefore, Bayesian decision theory seems to offer a promising framework for future research on uncertainty in sensorimotor control in sport science.
 References
 Franklin, D. W., & Wolpert, D. M. (2011). Computational mechanisms of sensorimotor control. Neuron, 72(3), 425-442. https://doi.org/10.1016/j.neuron.2011.10.006
 Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271-304. https://doi.org/10.1146/annurev.psych.55.090902.142005
 Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5(11), 1226-1235. https://doi.org/10.1038/nn963

Multimodal assessment of the spatial correspondence between fNIRS and fMRI hemodynamic responses in motor tasks

Functional near-infrared spectroscopy (fNIRS) provides a cost-efficient and portable alternative to functional magnetic resonance imaging (fMRI) for assessing cortical activity changes based on hemodynamic signals. The spatial and temporal underpinnings of the fMRI blood-oxygen-level-dependent (BOLD) signal and corresponding fNIRS concentration of oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT) measurements are still not completely clear. We aim to analyze the spatial correspondence between these hemodynamic signals, in motor-network regions. To this end, we acquired asynchronous fMRI and fNIRS recordings from 9 healthy participants while performing motor imagery and execution. Using this multimodal approach, we investigated the ability to identify motor-related activation clusters in fMRI data using subject-specific fNIRS-based cortical signals as predictors of interest. Group-level activation was found in fMRI data modeled from corresponding fNIRS measurements, with significant peak activation found overlapping the individually-defined primary and premotor motor cortices, for all chromophores. No statistically significant differences were observed in multimodal spatial correspondence between HbO, HbR, and HbT, for both tasks. This suggests the possibility of translating neuronal information from fMRI into an fNIRS motor-coverage setup with high spatial correspondence using both oxy and deoxyhemoglobin data, with the inherent benefits of translating fMRI paradigms to fNIRS in cognitive and clinical neuroscience.

Contribution of the stereoscopic representation of motion-in-depth during visually guided feedback control.

Considerable studies have focused on the neural basis of visually guided tracking movement in the frontoparallel plane, whereas the neural process in real-world circumstances regarding the influence of binocular disparity and motion-in-depth (MID) perception is less understood. Although the role of stereoscopic versus monoscopic MID information has been extensively described for visual processing, its influence on top-down regulation for motor execution has not received much attention. Here, we orthogonally varied the visual representation (stereoscopic versus monoscopic) and motion direction (depth motion versus bias depth motion versus frontoparallel motion) during visually guided tracking movements, with simultaneous functional near-infrared spectroscopy recordings. Results show that the stereoscopic representation of MID could lead to more accurate movements, which was supported by specific neural activity pattern. More importantly, we extend prior evidence about the role of frontoparietal network in brain-behavior relationship, showing that occipital area, more specifically, visual area V2/V3 was also robustly involved in the association. Furthermore, by using the stereoscopic representation of MID, it is plausible to detect robust brain-behavior relationship even with small sample size at low executive task demand. Taken together, these findings highlight the importance of the stereoscopic representation of MID for investigating neural correlates of visually guided feedback control.

Rhythmic tapping difficulties in adults who stutter: A deficit in beat perception, motor execution, or sensorimotor integration?

The study aims to better understand the rhythmic abilities of people who stutter and to identify which processes potentially are impaired in this population: (1) beat perception and reproduction; (2) the execution of movements, in particular their initiation; (3) sensorimotor integration. Finger tapping behavior of 16 adults who stutter (PWS) was compared with that of 16 matching controls (PNS) in five rhythmic tasks of various complexity: three synchronization tasks - a simple 1:1 isochronous pattern, a complex non-isochronous pattern, and a 4 tap:1 beat isochronous pattern -, a reaction task to an aperiodic and unpredictable pattern, and a reproduction task of an isochronous pattern after passively listening. PWS were able to reproduce an isochronous pattern on their own, without external auditory stimuli, with similar accuracy as PNS, but with increased variability. This group difference in variability was observed immediately after passive listening, without prior motor engagement, and was not enhanced or reduced after several seconds of tapping. Although PWS showed increased tapping variability in the reproduction task as well as in synchronization tasks, this timing variability did not correlate significantly with the variability in reaction times or tapping force. Compared to PNS, PWS exhibited larger negative mean asynchronies, and increased synchronization variability in synchronization tasks. These group differences were not affected by beat hierarchy (i.e., "strong" vs. "weak" beats), pattern complexity (non-isochronous vs. isochronous) or presence versus absence of external auditory stimulus (1:1 vs. 1:4 isochronous pattern). Differences between PWS and PNS were not enhanced or reduced with sensorimotor learning, over the first taps of a synchronization task. Our observations support the hypothesis of a deficit in neuronal oscillators coupling in production, but not in perception, of rhythmic patterns, and a larger delay in multi-modal feedback processing for PWS.

Cortical Patterns Shift from Sequence Feature Separation during Planning to Integration during Motor Execution.

Performing sequences of movements from memory and adapting them to changing task demands is a hallmark of skilled human behavior, from handwriting to playing a musical instrument. Prior studies showed a fine-grained tuning of cortical primary motor, premotor, and parietal regions to motor sequences: from the low-level specification of individual movements to high-level sequence features, such as sequence order and timing. However, it is not known how tuning in these regions unfolds dynamically across planning and execution. To address this, we trained 24 healthy right-handed human participants (14 females, 10 males) to produce four five-element finger press sequences with a particular finger order and timing structure in a delayed sequence production paradigm entirely from memory. Local cortical fMRI patterns during preparation and production phases were extracted from separate No-Go and Go trials, respectively, to tease out activity related to these perimovement phases. During sequence planning, premotor and parietal areas increased tuning to movement order or timing, regardless of their combinations. In contrast, patterns reflecting the unique integration of sequence features emerged in these regions during execution only, alongside timing-specific tuning in the ventral premotor, supplementary motor, and superior parietal areas. This was in line with the participants' behavioral transfer of trained timing, but not of order to new sequence feature combinations. Our findings suggest a general informational state shift from high-level feature separation to low-level feature integration within cortical regions for movement execution. Recompiling sequence features trial-by-trial during planning may enable flexible last-minute adjustment before movement initiation.SIGNIFICANCE STATEMENT Musicians and athletes can modify the timing and order of movements in a sequence trial-by-trial, allowing for a vast repertoire of flexible behaviors. How does the brain put together these high-level sequence features into an integrated whole? We found that, trial-by-trial, the control of sequence features undergoes a state shift from separation during planning to integration during execution across a network of motor-related cortical areas. These findings have implications for understanding the hierarchical control of skilled movement sequences, as well as how information in brain areas unfolds across planning and execution.

Bandwidth-Efficient Distributed Neural Network Architectures With Application to Neuro-Sensor Networks.

In this paper, we describe a design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with communication bandwidth constraints. The different sensor channels are distributed across multiple sensor devices, which have to exchange data over bandwidth-limited communication channels to solve a classification task. Our design methodology starts from a centralized neural network and transforms it into a distributed architecture in which the channels are distributed over different nodes. The distributed network consists of two parallel branches, whose outputs are fused at the fusion center. The first branch collects classification results from local, node-specific classifiers while the second branch compresses each node's signal and then reconstructs the multi-channel time series for classification at the fusion center. We further improve bandwidth gains by dynamically activating the compression path when the local classifications do not suffice. We validate this method on a motor execution task in an emulated EEG sensor network and analyze the resulting bandwidth-accuracy trade-offs. Our experiments show that the proposed framework enables up to a factor 20 in bandwidth reduction and factor 9 in power reduction with minimal loss (up to 2%) in classification accuracy compared to the centralized baseline on the demonstrated task. The proposed method offers a way to transform a centralized architecture to a distributed, bandwidth-efficient network amenable for low-power sensor networks. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.

Action representations in prevention behavior: Evidence from motor execution

Human actions sometimes aim at preventing an event from occurring. How these to-be-prevented events are represented, however, is poorly understood. Recent proposals in the literature point to a possible divide between effect-producing, operant actions, and effect-precluding, prevention actions, suggesting that the control of operant actions relies on codes of environment-related effects whereas prevention actions do not. Here we report two experiments on this issue, showing that spatial features (Experiment 1) as well as temporal features (Experiment 2) of to-be-prevented events influence actions in the same way as corresponding features of to-be-produced effects. This implies that selecting and executing prevention actions relies on anticipated environmental changes, comparable to operant actions.

Gravity matters for the neural representations of action semantics.

The dynamic relationship between the neural representation of action word semantics and specific sensorimotor experience remains controversial. Here, we temporarily altered human subjects' sensorimotor experience in a 15-day head-down tilt bed rest setting, a ground-based analog of microgravity that disproportionally affects sensorimotor experiences of the lower limbs, and examined whether such effector-dependent activity deprivation specifically affected the neural processes of comprehending verbs of lower-limb actions (e.g. to kick) relative to upper-limb ones (e.g. to pinch). Using functional magnetic resonance imaging, we compared the multivoxel neural patterns for such action words prior to and after bed rest. We found an effector-specific (lower vs. upper limb) experience modulation in subcortical sensorimotor-related and anterior temporal regions. The neural action semantic representations in other effector-specific verb semantic regions (e.g. left lateral posterior temporal cortex) and motor execution regions were robust against such experience alterations. These effector-specific, sensorimotor-experience-sensitive and experience-independent patterns of verb neural representation highlight the multidimensional and dynamic nature of semantic neural representation, and the broad influence of microgravity (hence gravity) environment on cognition.

SmartRolling: A human–machine interface for wheelchair control using EEG and smart sensing techniques

Smart wheelchairs based on brain–computer interface (BCI) have been widely utilized recently to address certain mobility problems for people with disability. In this paper, we present SmartRolling, an intuitive human–machine interaction approach for the direct control of robotic wheelchair that jointly leverages EEG signals and motion sensing techniques. Specifically, SmartRolling offers two wheelchair-actuation modes for users with different physical conditions: (1) head motion only — people who are severely disabled but able to do basic tasks using eyes and head, and (2) head and hands motion — in addition to type 1, people who can use functioning hands/arms for extra tasks. The system issues operation commands by recognizing different EEG patterns elicited by motor execution (ME) tasks including eye blink, jaw clench, and fist open/close, while at the same time estimates users’ steering intentions based on their facing direction by leveraging inertial measurements and computer vision techniques. The experiment results demonstrate that the proposed system is robust and effective to meets the individual’s needs and has great potential to promote better health.

Spatial cognition training rapidly induces cortical plasticity in blind navigation: Transfer of training effect & Granger causal connectivity analysis.

How is the cortical navigation network reorganized by the Likova Cognitive-Kinesthetic Navigation Training? We measured Granger-causal connectivity of the frontal-hippocampal-insular-retrosplenial-V1 network of cortical areas before and after this one-week training in the blind. Primarily top-down influences were seen during two tasks of drawing-from-memory (drawing complex maps and drawing the shortest path between designated map locations), with the dominant role being congruent influences from the egocentric insular to the allocentric spatial retrosplenial cortex and the amodal-spatial sketchpad of V1, with concomitant influences of the frontal cortex on these areas. After training, and during planning-from-memory of the best on-demand path, the hippocampus played a much stronger role, with the V1 sketchpad feeding information forward to the retrosplenial region. The inverse causal influences among these regions generally followed a recursive feedback model of the opposite pattern to a subset of congruent influences. Thus, this navigational network reorganized its pattern of causal influences with task demands and the navigation training, which produced marked enhancement of the navigational skills.

Distinct neural representations of hand movement direction between motor imagery and execution in the presupplementary motor area

Motor simulation theory proposes a functional equivalence between motor execution (ME) and its simulation, suggesting that motor imagery (MI) is the self-intentioned simulation of one’s actions. This study used functional magnetic resonance imaging (fMRI) with multivoxel pattern analysis to test whether the direction of hand movement is represented with a similar neural code between ME and MI. In our study, participants used their right hand to move an on-screen cursor in the left–right direction with a joystick or imagined the same movement without execution. The results indicated that the left–right direction as well as their modality (ME or MI) could be decoded significantly above the chance level in the presupplementary motor area (pre-SMA) and primary visual cortex (V1). Next, we used activation patterns of ME as inputs to the decoder to predict hand move directions in MI sessions and found a significantly higher-than-chance accuracy only in V1, not in pre-SMA. Moreover, the representational similarity analysis showed similar activation patterns for the same directions between ME and MI in V1 but not in pre-SMA. This study’s finding indicates distinct spatial activation patterns for movement directions between ME and MI in pre-SMA.

Expression of LIM domain-binding 3 (LDB3), a striated muscle Z-band alternatively spliced PDZ-motif protein in the nervous system

LIM domain-binding 3 (LDB3) is a member of the Enigma family of PDZ–LIM proteins. LDB3 has been reported as a striated muscle-specific Z-band alternatively spliced protein that plays an important role in mechanosensory actin cytoskeleton remodeling. This study shows that LDB3 is broadly expressed in the central and peripheral nervous system of human and mouse. LDB3 is predominantly expressed in the adult stages compared to early development and at a significantly higher level in the spinal cord than in the brain. As in skeletal muscle and heart, LDB3 is extensively alternatively spliced in the neurons. Three novel splice isoforms were identified suggesting splicing-dependent regulation of LDB3 expression in the nervous system. Expression of LDB3 in the motor cortex, cerebellum, spinal motor neuron, peripheral nerve, and neuromuscular junction in addition to skeletal muscle indicates important roles for this PDZ–LIM family protein in motor planning and execution. Moreover, expression in the hippocampal neurons suggests roles for LDB3 in learning and memory. LDB3 interactors filamin C and myotilin are also expressed in the spinal motor neuron, nerve, and neuromuscular junction, thereby providing the basis for neurogenic manifestations in myopathies associated with mutations in these so-called muscle proteins.

Perspective: Acknowledging complexity to advance the understanding of developmental coordination disorder.

Developmental Coordination Disorder (DCD) is a heterogeneous neurodevelopmental disorder known for primary symptoms of motor learning and execution difficulties. Recent research has consistently suggested DCD symptoms span broadly beyond motor difficulties, yet a majority of research and practice approaches the investigation, diagnosis, and treatment of DCD with a reductionist framework. Therefore, this paper suggests the paradigm of complexity theory as a means for better conceptualization, assessment, and treatment of DCD. First, the perspective of complexity theory and its relevance to DCD is described. Then, examples from recent research which attempt to acknowledge and capture the complex nature of DCD are highlighted. Finally, suggestions for considering and measuring complexity of DCD in future research and practice are provided. Overall, the perspective of complexity can propel the research forward and improve the understanding of DCD relevant to assessment and treatment. The complexity paradigm is highly relevant to describing the evolving and multidimensional picture of DCD, understanding heterogeneous symptom profiles, making connections to interconnected secondary symptoms, and beyond.

MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements.

Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients' residual or intact movement functions, is a more intuitive and natural paradigm. Based on the ME paradigm, we can decode voluntary hand movement intentions from electroencephalography (EEG) signals. Numerous studies have investigated EEG-based unimanual movement decoding. Moreover, some studies have explored bimanual movement decoding since bimanual coordination is important in daily-life assistance and bilateral neurorehabilitation therapy. However, the multi-class classification of the unimanual and bimanual movements shows weak performance. To address this problem, in this work, we propose a neurophysiological signatures-driven deep learning model utilizing the movement-related cortical potentials (MRCPs) and event-related synchronization/ desynchronization (ERS/D) oscillations for the first time, inspired by the finding that brain signals encode motor-related information with both evoked potentials and oscillation components in ME. The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. Results show that our proposed model has superior performance to the baseline methods. Six-class classification accuracies of unimanual and bimanual movements achieved 80.3%. Besides, each feature module of our model contributes to the performance. This work is the first to fuse the MRCPs and ERS/D oscillations of ME in deep learning to enhance the multi-class unimanual and bimanual movements' decoding performance. This work can facilitate the neural decoding of unimanual and bimanual movements for neurorehabilitation and assistance.

Frontoparietal Dysconnection in Covert Bipedal Activity for Enhancing the Performance of the Motor Preparation-based Brain-Computer Interface.

Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated. Since MP is the precedence phase before MI or ME, MP-BCI could potentially detect brain commands at an earlier state. This study proposes a bipedal MP-BCI system, which is actuated by the reduction in frontoparietal connectivity strength. Three substudies, including bipedal classification, neurofeedback, and post-stroke analysis, were performed to validate the performance of our proposed model. In bipedal classification, functional connectivity was extracted by Pearson's correlation model from electroencephalogram (EEG) signals recorded while the subjects were performing MP and MI. The binary classification of MP achieved short-lived peak accuracy of 73.73(±7.99)% around 200-400 ms post-cue. The peak accuracy was found synchronized to the MP-related potential and the decrement in frontoparietal connection strength. The connection strengths of the right frontal and left parietal lobes in the alpha range were found negatively correlated to the classification accuracy. In the subjective neurofeedback study, the majority of subjects reported that motor preparation instead of the motor imagery activated the frontoparietal dysconnection. Post-stroke study also showed that patients exhibit lower frontoparietal connections compared to healthy subjects during both MP and ME phases. These findings suggest that MP reduced alpha band functional frontoparietal connectivity and the EEG signatures of left and right foot MP could be discriminated more effectively during this phase. A neurofeedback paradigm based on the frontoparietal network could also be utilized to evaluate post-stroke rehabilitation training.

A New Compound-limbs Paradigm: Integrating upper-limb swing improves lower-limb stepping intention decoding from EEG.

Brain-computer interface (BCI) systems based on spontaneous electroencephalography (EEG) hold the promise to implement human voluntary control of lower-extremity powered exoskeletons. However, current EEG-BCI paradigms do not consider the cooperation of upper and lower limbs during walking, which is inconsistent with natural human stepping patterns. To deal with this problem, this study proposed a stepping-matched human EEG-BCI paradigm that involved actions of both unilateral lower and contralateral upper limbs (also referred to as compound-limbs movement). Experiments were conducted in motor execution (ME) and motor imagery (MI) conditions to validate the feasibility. Common spatial pattern (CSP) proposed subject-specific CSP (SSCSP), and filter-bank CSP (FBCSP) methods were applied for feature extraction, respectively. The best average classification results based on SSCSP indicated that the accuracies of compound-limbs paradigms in ME and MI conditions achieved 89.02% ± 12.84% and 73.70% ± 12.47%, respectively. Although they were 2.03% and 5.68% lower than those of the single-upper-limb mode that does not match human stepping patterns, they were 24.30% and 11.02% higher than those of the single-lower-limb mode. These findings indicated that the proposed compound-limbs EEG-BCI paradigm is feasible for decoding human stepping intention and thus provides a potential way for natural human control of walking assistance devices.

Os exergames promovem alterações neurofuncionais na doença de Parkinson? Um estudo clínico piloto

Abstract Introduction Previous studies have demonstrated beneficial effects in people with Parkinson´s disease trained with exergames. However, to the best of our knowledge, none of them evaluated whether these effects are sustained by neurofunctional changes. Objective To evaluate neurofunctional effects of a training, by means of functional magnetic resonance imaging, in people with Parkinson´s disease. Methods This study was a blind, randomized, and controlled pilot clinical trial with crossover design. The participants were submitted to an evaluation including cognitive performance and functional magnetic resonance imaging before and after the WiiTM or control trainings. Trainings were applied for 10 days, in two consecutive weeks. Participants starting with WiiTM training were then moved to the control training and vice versa. A wash-out period of 45 days between the trainings was respected. Results Memory, executive and visuo-spatial functions, and attention were significantly improved compared to baseline (p < 0.05). No differences were observed in cognition compared to the control training. Though not significant, results of functional magnetic resonance imaging analyses suggested that WiiTM training could promote improvements on the brain functional connectivity especially in areas involved in motor execution, planning, visual, memory and somatosensory functions. Conclusion In people with Parkinson´s disease, an intensive WiiTM training improved cognitive performance that underlined neurofunctional changes in areas involved in cognitive processing.