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Showing papers on "Motor imagery published in 2015"


Journal ArticleDOI
TL;DR: In this paper, the most common brain areas for fNIRS-based BCI are the primary motor cortex and prefrontal cortex, and the motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided.
Abstract: A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

715 citations


Journal ArticleDOI
TL;DR: Brain–computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI, and the efficacy of BCI‐monitored MI practice as add‐on intervention to usual rehabilitation care was evaluated in subacute stroke patients.
Abstract: Objective Motor imagery (MI) is assumed to enhance poststroke motor recovery, yet its benefits are debatable. Brain–computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI. The efficacy of BCI-monitored MI practice as add-on intervention to usual rehabilitation care was evaluated in a randomized controlled pilot study in subacute stroke patients. Methods Twenty-eight hospitalized subacute stroke patients with severe motor deficits were randomized into 2 intervention groups: 1-month BCI-supported MI training (BCI group, n = 14) and 1-month MI training without BCI support (control group; n = 14). Functional and neurophysiological assessments were performed before and after the interventions, including evaluation of the upper limbs by Fugl–Meyer Assessment (FMA; primary outcome measure) and analysis of oscillatory activity and connectivity at rest, based on high-density electroencephalographic (EEG) recordings. Results Better functional outcome was observed in the BCI group, including a significantly higher probability of achieving a clinically relevant increase in the FMA score (p < 0.03). Post-BCI training changes in EEG sensorimotor power spectra (ie, stronger desynchronization in the alpha and beta bands) occurred with greater involvement of the ipsilesional hemisphere in response to MI of the paralyzed trained hand. Also, FMA improvements (effectiveness of FMA) correlated with the changes (ie, post-training increase) at rest in ipsilesional intrahemispheric connectivity in the same bands (p < 0.05). Interpretation The introduction of BCI technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke patients with severe motor impairments. Ann Neurol 2015;77:851–865

465 citations


Journal ArticleDOI
22 May 2015-Science
TL;DR: In this article, the posterior parietal cortex (PPC) was found to represent high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.
Abstract: Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.

452 citations


01 Jan 2015
TL;DR: Recording neural population activity with arrays of microelectrodes implanted in the posterior parietal cortex of a tetraplegic subject indicates that the PPC of humans represents high-level, cognitive aspects of action and that thePPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.
Abstract: Brain imagination to control external devices Studies in monkeys have implicated the brain's posterior parietal cortex in high-level coding of planned and imagined actions. Aflalo et al. implanted two microelectrode arrays in the posterior parietal cortex of a tetraplegic patient (see the Perspective by Pruszynski and Diedrichsen). They asked the patient to imagine various types of limb or eye movements. As predicted, motor imagery involved the same types of neural population activity involved in actual movements, which could potentially be exploited in prosthetic limb control. Science, this issue p. 906; see also p. 860 Neurons in the human posterior parietal cortex encode high-level aspects of imagined movements. [Also see Perspective by Pruszynski and Diedrichsen] Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.

401 citations


Journal ArticleDOI
TL;DR: A possible strategic approach to deal with performance variation in brain-computer interface systems is proposed, which may contribute to improving the reliability of BCI.

248 citations


Journal ArticleDOI
TL;DR: The results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs and concurrently measure and discriminate fNirS signals evoked by three different mental activities.

244 citations


Journal ArticleDOI
01 Mar 2015-Cortex
TL;DR: How the brain is activated during imagination and observation of different postural tasks is elaborated to provide recommendations about the conception of non-physical balance training and showed that mainly AO + MI, but also MI, activate brain regions important for balance control.

180 citations


Journal ArticleDOI
TL;DR: A complex relationship between MI EEG signals and sensorimotor cortical activity, whereby both are similarly modulated by EEG neurofeedback is indicated, which supports the potential of MI EEG Neurofeedback for motor rehabilitation and helps to better understand individual differences in MI BCI performance.

148 citations


Journal ArticleDOI
TL;DR: The use of brain computer interfaces (BCI) for neuro-rehabilitation has been discussed in this article. But the main objective is to promote the recruitment of selected brain areas involved and to facilitate neural plasticity.

145 citations


Journal ArticleDOI
TL;DR: This Special Interest article discusses those priming paradigms that are supported by the greatest amount of evidence, including (i) stimulation-based priming, (ii) motor imagery and action observation, (iii) sensory primed, (iv) movement-basedPriming, and (v) pharmacological priming.
Abstract: Priming is a type of implicit learning wherein a stimulus prompts a change in behavior. Priming has been long studied in the field of psychology. More recently, rehabilitation researchers have studied motor priming as a possible way to facilitate motor learning. For example, priming of the motor cortex is associated with changes in neuroplasticity that are associated with improvements in motor performance. Of the numerous motor priming paradigms under investigation, only a few are practical for the current clinical environment, and the optimal priming modalities for specific clinical presentations are not known. Accordingly, developing an understanding of the various types of motor priming paradigms and their underlying neural mechanisms is an important step for therapists in neurorehabilitation. Most importantly, an understanding of the methods and their underlying mechanisms is essential for optimizing rehabilitation outcomes. The future of neurorehabilitation is likely to include these priming methods, which are delivered prior to or in conjunction with primary neurorehabilitation therapies. In this Special Interest article, we discuss those priming paradigms that are supported by the greatest amount of evidence, including (i) stimulation-based priming, (ii) motor imagery and action observation, (iii) sensory priming, (iv) movement-based priming, and (v) pharmacological priming.Video Abstract available. (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A86) for more insights from the authors.

125 citations


Journal ArticleDOI
TL;DR: A theoretical account that relates KMI to predictive motor control theories assuming that it is based on internal activation of anticipatory images of action effects and it is suggested that experience with motor performance improves the ability to correctly infer the goals of others, in particular in penalty blocking in soccer.
Abstract: Kinesthetic Motor Imagery (KMI) is an important technique to acquire and refine motor skills. KMI is widely used by professional athletes as an effective way to improve motor performance without overt motor output. Despite this obvious relevance, the functional mechanisms and neural circuits involved in KMI in sports are still poorly understood. In the present article, which aims at bridging the sport sciences and cognitive neurophysiology literatures, we give a brief overview of relevant research in the field of KMI. Furthermore, we develop a theoretical account that relates KMI to predictive motor control theories assuming that it is based on internal activation of anticipatory images of action effects. This mechanism allows improving motor performance solely based on internal emulation of action. In accordance with previous literature, we propose that this emulation mechanism is implemented in brain regions that partially overlap with brain areas involved in overt motor performance including the posterior parietal cortex, the cerebellum, the basal ganglia and the premotor cortex. Finally, we outline one way to test the heuristic value of our theoretical framework for KMI; we suggest that experience with motor performance improves the ability to correctly infer the goals of others, in particular in penalty blocking in soccer.

Journal ArticleDOI
TL;DR: A novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI and proposes a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system.

Journal ArticleDOI
TL;DR: The results suggest a role for tDCS in facilitating motor imagery in stroke and online accuracies of the evaluation part from the tDCS group were significantly higher than those from the sham group.

Proceedings ArticleDOI
01 Aug 2015
TL;DR: This work proposed an architecture that inputs a dynamic energy representation of EEG data and utilizes convolutional neural networks for classification and compared with SVM on a multi-class motor imagery dataset (BCI competition dataset IV-2a).
Abstract: Deep learning, recently, has been successfully applied to image classification, object recognition and speech recognition. However, the benefits of deep learning and accompanying architectures have been largely unknown for BCI applications. In motor imagery-based BCI, an energy-based feature, typically after spatial filtering, is commonly used for classification. Although this feature corresponds to the estimate of event-related synchronization/desynchronization in the brain, it neglects energy dynamics which may contain valuable discriminative information. Because traditional classiication methods, such as SVM, cannot handle this dynamical property, we proposed an architecture that inputs a dynamic energy representation of EEG data and utilizes convolutional neural networks for classification. By combining this network with a static energy network, we saw a significant increase in performance. We evaluated the proposed method and compared with SVM on a multi-class motor imagery dataset (BCI competition dataset IV-2a). Our method outperforms SVM with static energy features significantly (p < 0.01).

Journal ArticleDOI
TL;DR: The network mechanisms of the MI-BCI are revealed and may help to find new strategies for improving MI- BCI performance and may be a source of inspiration for practical BCI applications beyond the laboratory.
Abstract: Objective. Motor imagery-based brain–computer interface (MI-BCI) systems hold promise in motor function rehabilitation and assistance for motor function impaired people. But the ability to operate an MI-BCI varies across subjects, which becomes a substantial problem for practical BCI applications beyond the laboratory. Approach. Several previous studies have demonstrated that individual MI-BCI performance is related to the resting state of brain. In this study, we further investigate offline MI-BCI performance variations through the perspective of resting-state electroencephalography (EEG) network. Main results. Spatial topologies and statistical measures of the network have close relationships with MI classification accuracy. Specifically, mean functional connectivity, node degrees, edge strengths, clustering coefficient, local efficiency and global efficiency are positively correlated with MI classification accuracy, whereas the characteristic path length is negatively correlated with MI classification accuracy. The above results indicate that an efficient background EEG network may facilitate MI-BCI performance. Finally, a multiple linear regression model was adopted to predict subjects' MI classification accuracy based on the efficiency measures of the resting-state EEG network, resulting in a reliable prediction. Significance. This study reveals the network mechanisms of the MI-BCI and may help to find new strategies for improving MI-BCI performance.

Journal ArticleDOI
TL;DR: It is found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere, and the intervention improved the regional connectivity among the motor areas during both the tasks, suggesting that PMC and M1 play a crucial role during motor-imagery as wellAsDuring motor-execution task.

Journal ArticleDOI
01 Apr 2015-PLOS ONE
TL;DR: A 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements is proposed, and it is demonstrated that same-limb motor imagery classification is possible and that goal-oriented imaginary elbows lead to a better classification performance compared to simple imaginary elbows.
Abstract: The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area There is, however, a pressing need to succeed in this task The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI) In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated To the best of our knowledge, both problems have not been explored in the literature Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 669% For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 607%, which is greater than the random classification accuracy of 333% Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises

Journal ArticleDOI
12 May 2015
TL;DR: Clinical efficacy of using BCI as an alternate neurorehabilitation for stroke patients with chronic stroke, detecting MI to trigger a feedback, and detecting MI with a robot to provide concomitant MI and PP are demonstrated.
Abstract: Current rehabilitation therapies for stroke rely on physical practice (PP) by the patients. Motor imagery (MI), the imagination of movements without physical action, presents an alternate neurorehabilitation for stroke patients without relying on residue movements. However, MI is an endogenous mental process that is not physically observable. Recently, advances in brain–computer interface (BCI) technology have enabled the objective detection of MI that spearheaded this alternate neurorehabilitation for stroke. In this review, we present two strategies of using BCI for neurorehabilitation after stroke: detecting MI to trigger a feedback, and detecting MI with a robot to provide concomitant MI and PP. We also present three randomized control trials that employed these two strategies for upper limb rehabilitation. A total of 125 chronic stroke patients were screened over six years. The BCI screening revealed that 103 (82%) patients can use electroencephalogram-based BCI, and 75 (60%) performed well with accuracies above 70%. A total of 67 patients were recruited to complete one of the three RCTs ranging from two to six weeks of which 26 patients, who underwent BCI neurorehabilitation that employed these two strategies, had significant motor improvement of 4.5 measured by Fugl-Meyer Motor Assessment of the upper extremity. Hence, the results demonstrate clinical efficacy of using BCI as an alternate neurorehabilitation for stroke.

Journal ArticleDOI
TL;DR: Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNirS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.
Abstract: Objective. In order to increase the number of states classified by a brain–computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching. Approach. The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs). Main results. In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG–fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% ± 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature. Significance. Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG–fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.

Journal ArticleDOI
TL;DR: A hybrid BCI paradigm combining motor imagery and steady-state visually evoked potentials (SSVEPs) has been proposed to provide effective continuous feedback for motor imagery training and can effectively identify the intentions of the subjects.
Abstract: Goal : Motor imagery-related mu/beta rhythms, which can be voluntarily modulated by subjects, have been widely used in EEG-based brain computer interfaces (BCIs). Moreover, it has been suggested that motor imagery-specific EEG differences can be enhanced by feedback training. However, the differences observed in the EEGs of naive subjects are typically not sufficient to provide reliable EEG control and thus result in unintended feedback. Such feedback can frustrate subjects and impede training. In this study, a hybrid BCI paradigm combining motor imagery and steady-state visually evoked potentials (SSVEPs) has been proposed to provide effective continuous feedback for motor imagery training. Methods : During the initial training sessions, subjects must focus on flickering buttons to evoke SSVEPs as they perform motor imagery tasks. The output/feedback of the hybrid BCI is based on hybrid features consisting of motor imagery- and SSVEP-related brain signals. In this context, the SSVEP plays a more important role than motor imagery in generating feedback. As the training progresses, the subjects can gradually decrease their visual attention to the flickering buttons, provided that the feedback is still effective. In this case, the feedback is mainly based on motor imagery. Results : Our experimental results demonstrate that subjects generate distinguishable brain patterns of hand motor imagery after only five training sessions lasting approximately 1.5 h each. Conclusion : The proposed hybrid feedback paradigm can be used to enhance motor imagery training. Significance : This hybrid BCI system with feedback can effectively identify the intentions of the subjects.

Journal ArticleDOI
TL;DR: Results suggest a possible biomarker for the absence of intentional movement in covertly aware patients (ie, specific damage to motor thalamocortical fibers), highlight the importance of the thalamus for the execution of intentional movements, and may provide a target for restorative therapies in behaviorally nonresponsive patients.
Abstract: Importance It is well accepted that a significant number of patients in a vegetative state are covertly aware and capable of following commands by modulating their neural responses in motor imagery tasks despite remaining nonresponsive behaviorally. To date, there have been few attempts to explain this dissociation between preserved covert motor behavior and absent overt motor behavior. Objectives To investigate the differential neural substrates of overt and covert motor behavior and assess the structural integrity of the underlying networks in behaviorally nonresponsive patients. Design, Setting, and Participants A case-control study was conducted at an academic center between February 7, 2012, and November 6, 2014. Data analysis was performed between March 2014 and June 2015. Participants included a convenience sample of 2 patients with severe brain injury: a paradigmatic patient who fulfilled all clinical criteria for the vegetative state but produced repeated evidence of covert awareness (patient 1) and, as a control case, a patient with similar clinical variables but capable of behavioral command following (patient 2). Fifteen volunteers participated in the study as a healthy control group. Main Outcomes and Measures We used dynamic causal modeling of functional magnetic resonance imaging to compare voluntary motor imagery and motor execution. We then used fiber tractography to assess the structural integrity of the fibers that our functional magnetic resonance imaging study revealed as essential for successful motor execution. Results The functional magnetic resonance imaging study revealed that, in contrast to mental imagery, motor execution was associated with an excitatory coupling between the thalamus and primary motor cortex (Bayesian model selection; winning model Bayes factors >17). Moreover, we detected a selective structural disruption in the fibers connecting these 2 regions in patient 1 (fractional anisotropy, 0.294; P = .047) but not in patient 2 (fractional anisotropy, 0.413; P = .35). Conclusions and Relevance These results suggest a possible biomarker for the absence of intentional movement in covertly aware patients (ie, specific damage to motor thalamocortical fibers), highlight the importance of the thalamus for the execution of intentional movements, and may provide a target for restorative therapies in behaviorally nonresponsive patients.

Journal ArticleDOI
TL;DR: Findings indicate that proprioceptive feedback is more suitable than visual feedback to entrain the motor network architecture during the interplay between motor imagery and feedback processing thus resulting in better volitional control of regional brain activity.

Journal ArticleDOI
TL;DR: It is demonstrated that MI practice lead to the development of neuroplasticity, as it affected the PAS25- and PAS10- induced plasticity in M1.
Abstract: Several investigations suggest that actual and mental actions trigger similar neural substrates. Motor learning via physical practice results in long-term potentiation (LTP)-like plasticity processes, namely potentiation of M1 and a temporary occlusion of additional LTP-like plasticity. However, whether this neuroplasticity process contributes to improve motor performance through mental practice remains to be determined. Here, we tested skill learning-dependent changes in primary motor cortex (M1) excitability and plasticity by means of transcranial magnetic stimulation (TMS) in subjects trained to physically execute or mentally perform a sequence of finger opposition movements. Before and after physical practice and motor-imagery practice, M1 excitability was evaluated by measuring the input-output (IO) curve of motor evoked potentials. M1 LTP and long-term depression (LTD)-like plasticity was assessed with paired-associative stimulation (PAS) of the median nerve and motor cortex using an interstimulus interval of 25 ms (PAS25) or 10 ms (PAS10), respectively. We found that even if after both practice sessions subjects significantly improved their movement speed, M1 excitability and plasticity were differentially influenced by the two practice sessions. First, we observed an increase in the slope of IO curve after physical but not after MI practice. Second, there was a reversal of the PAS25 effect from LTP-like plasticity to LTD-like plasticity following physical and MI practice. Third, LTD-like plasticity (PAS10 protocol) increased after physical practice, whilst it was occluded after MI practice. In conclusion, we demonstrated that MI practice lead to the development of neuroplasticity, as it affected the PAS25- and PAS10- induced plasticity in M1. These results, expanding the current knowledge on how MI training shapes M1 plasticity, might have a potential impact in rehabilitation.

Journal ArticleDOI
TL;DR: The review of available evidences indicated that MI ability and AO feasibility are substantially preserved in PD subjects and could be used as parts of rehabilitation protocols for PD patients.
Abstract: Parkinson's disease (PD) is characterized by a progressive impairment of motor skills with deterioration of autonomy in daily living activities. Physiotherapy is regarded as an adjuvant to pharmacological and neurosurgical treatment and may provide small and short-lasting clinical benefits in PD patients. However, the development of innovative rehabilitation approaches with greater long-term efficacy is a major unmet need. Motor imagery (MI) and action observation (AO) have been recently proposed as a promising rehabilitation tool. MI is the ability to imagine a movement without actual performance (or muscle activation). The same cortical-subcortical network active during motor execution is engaged in MI. The physiological basis of AO is represented by the activation of the "mirror neuron system." Both MI and AO are involved in motor learning and can induce improvements of motor performance, possibly mediated by the development of plastic changes in the motor cortex. The review of available evidences indicated that MI ability and AO feasibility are substantially preserved in PD subjects. A few preliminary studies suggested the possibility of using MI and AO as parts of rehabilitation protocols for PD patients.

Journal ArticleDOI
TL;DR: An ecological BCI-based device to assist motor imagery practice was found to be feasible as an add-on intervention and tolerable by patients who were exposed to the system in the rehabilitation environment.

Journal ArticleDOI
TL;DR: This study shows for the first time hand-shape decoding from human PPC and can show that distinct neuronal populations are activated for the visual cue and the imagined hand shape and found that auditory and visual stimuli that cue the same hand shape are processed differently in PPC.
Abstract: Humans shape their hands to grasp, manipulate objects, and to communicate. From nonhuman primate studies, we know that visual and motor properties for grasps can be derived from cells in the posterior parietal cortex (PPC). Are non-grasp-related hand shapes in humans represented similarly? Here we show for the first time how single neurons in the PPC of humans are selective for particular imagined hand shapes independent of graspable objects. We find that motor imagery to shape the hand can be successfully decoded from the PPC by implementing a version of the popular Rock-Paper-Scissors game and its extension Rock-Paper-Scissors-Lizard-Spock. By simultaneous presentation of visual and auditory cues, we can discriminate motor imagery from visual information and show differences in auditory and visual information processing in the PPC. These results also demonstrate that neural signals from human PPC can be used to drive a dexterous cortical neuroprosthesis.

Journal ArticleDOI
TL;DR: A brain-robot-interface might offer a way to bridge the gap between these networks, opening thereby a backdoor to the motor execution system, which might promote patient screening and lead to novel treatment strategies, e.g. for the rehabilitation of hemiparesis after stroke.

Journal ArticleDOI
TL;DR: The results demonstrate that corticospinal excitability during mental simulation of balance tasks is influenced by both the type of mental simulation and the task difficulty, and suggest best training/rehabilitation effects when combining MI with AO during challenging postural tasks.

Journal ArticleDOI
TL;DR: It is found that cM1 activity was positively correlated to improvements in accuracy as well as overall performance improvements, whereas other regions in the sensorimotor network were not, and indicates that subjects with stronger cM 1 activation during MI may benefit more from MI training, with implications toward targeted neurotherapy.
Abstract: Motor imagery (MI) has shown effectiveness in enhancing motor performance. This may be due to the common neural mechanisms underlying MI and motor execution (ME). The main region of the ME network, the primary motor cortex (M1), has been consistently linked to motor performance. However, the activation of M1 during motor imagery is controversial, which may account for inconsistent rehabilitation therapy outcomes using MI. Here, we examined the relationship between contralateral M1 (cM1) activation during MI and changes in sensorimotor performance. To aid cM1 activity modulation during MI, we used real-time fMRI neurofeedback-guided MI based on cM1 hand area blood oxygen level dependent (BOLD) signal in healthy subjects, performing kinesthetic MI of pinching. We used multiple regression analysis to examine the correlation between cM1 BOLD signal and changes in motor performance during an isometric pinching task of those subjects who were able to activate cM1 during motor imagery. Activities in premotor and parietal regions were used as covariates. We found that cM1 activity was positively correlated to improvements in accuracy as well as overall performance improvements, whereas other regions in the sensorimotor network were not. The association between cM1 activation during MI with performance changes indicates that subjects with stronger cM1 activation during MI may benefit more from MI training, with implications toward targeted neurotherapy.

Journal ArticleDOI
TL;DR: This paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far, in order to evaluate the suitability and reliability of this technology.