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Showing papers on "Motor imagery published in 2016"


Journal ArticleDOI
TL;DR: Eight chronic spinal cord injury paraplegics were subjected to long-term training with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion, resulting in unprecedented neurological recovery.
Abstract: Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.

274 citations


Journal ArticleDOI
TL;DR: The rehabilitative program for PD should be "goal-based" (targeted to practicing and learning specific activities in the core areas), but a number of practice variables need to be identified and the program should tailored to the individual patients' characteristics.

272 citations


Journal ArticleDOI
TL;DR: This study extends previous EEG source imaging work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation, and suggests ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.
Abstract: Goal: Sensorimotor-based brain–computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device. Methods: We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation. Results: We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method. Conclusion: ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks. Significance: This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.

257 citations


Journal ArticleDOI
TL;DR: It is argued that motor and cognitive processes are functionally related and most likely share a similar evolutionary history and this is supported by clinical and neural data showing that some brain regions integrate both motor and Cognitive functions.
Abstract: In this article, we argue that motor and cognitive processes are functionally related and most likely share a similar evolutionary history. This is supported by clinical and neural data showing that some brain regions integrate both motor and cognitive functions. In addition, we also argue that cognitive processes coincide with complex motor output. Further, we also review data that support the converse notion that motor processes can contribute to cognitive function, as found by many rehabilitation and aerobic exercise training programs. Support is provided for motor and cognitive processes possessing dynamic bidirectional influences on each other.

220 citations


Journal ArticleDOI
TL;DR: This article first reviews the available neurophysiological and behavioral evidence for the effects of combined action observation and motor imagery (AO+MI) on motor processes, and advocates a more integrated approach to AO+ MI techniques than has previously been adopted by movement scientists and practitioners alike.
Abstract: Motor imagery (MI) and action observation (AO) have traditionally been viewed as two separate techniques, which can both be used alongside physical practice to enhance motor learning and rehabilitation. Their independent use has been shown to be effective, and there is clear evidence that the two processes can elicit similar activity in the motor system. Building on these well-established findings, research has now turned to investigate the effects of their combined use. In this article, we first review the available neurophysiological and behavioral evidence for the effects of combined action observation and motor imagery (‘AO+MI’) on motor processes. We next describe a conceptual framework for their combined use, and then discuss several areas for future research into AO+MI processes. In this review, we advocate a more integrated approach to AO+MI techniques than has previously been adopted by movement scientists and practitioners alike. We hope this early review of an emergent body of research, along with a related set of research questions, can inspire new work in this area. We are optimistic that future research will further confirm if, how, and when this combined approach to AO+MI can be more effective in motor learning and rehabilitation settings, relative to the more traditional application of AO or MI independently.

178 citations


Journal ArticleDOI
TL;DR: An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed, and the brain connectomes during left and right hand, feet, and tongue MI movements are discussed.
Abstract: Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface MI-BCI for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic EEG sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.

159 citations


Journal ArticleDOI
TL;DR: Results showed that the content of motor imagery could be decoded significantly above chance level from the spatial patterns of BOLD signals in both frontal (PMC, M1) and parietal areas (SPL, IPL, IPS).
Abstract: How motor maps are organized while imagining actions is an intensely debated issue. It is particularly unclear whether motor imagery relies on action-specific representations in premotor and posterior parietal cortices. This study tackled this issue by attempting to decode the content of motor imagery from spatial patterns of Blood Oxygen Level Dependent (BOLD) signals recorded in the frontoparietal motor imagery network. During fMRI-scanning, 20 right-handed volunteers worked on three experimental conditions and one baseline condition. In the experimental conditions, they had to imagine three different types of right-hand actions: an aiming movement, an extension-flexion movement, and a squeezing movement. The identity of imagined actions was decoded from the spatial patterns of BOLD signals they evoked in premotor and posterior parietal cortices using multivoxel pattern analysis. Results showed that the content of motor imagery (i.e., the action type) could be decoded significantly above chance level from the spatial patterns of BOLD signals in both frontal (PMC, M1) and parietal areas (SPL, IPL, IPS). An exploratory searchlight analysis revealed significant clusters motor- and motor-associated cortices, as well as in visual cortices. Hence, the data provide evidence that patterns of activity within premotor and posterior parietal cortex vary systematically with the specific type of hand action being imagined.

120 citations


Journal ArticleDOI
TL;DR: A synthesis approach based on optimum allocation system and Naive Bayes (NB) algorithm for detecting mental states based on EEG signals implies that it can be reliably used to detect EEG based MI activity and can be a promising avenue for EEG based BCI applications.

105 citations


Journal ArticleDOI
TL;DR: Neural representations of MI are neither the same nor totally distinct but exhibit a similar structural geometry with respect to different types of action within the frontoparietal motor network.
Abstract: Simulation theory proposes motor imagery (MI) to be a simulation based on representations also used for motor execution (ME). Nonetheless, it is unclear how far they use the same neural code. We use multivariate pattern analysis (MVPA) and representational similarity analysis (RSA) to describe the neural representations associated with MI and ME within the frontoparietal motor network. During functional magnetic resonance imaging scanning, 20 volunteers imagined or executed 3 different types of right-hand actions. Results of MVPA showed that these actions as well as their modality (MI or ME) could be decoded significantly above chance from the spatial patterns of BOLD signals in premotor and posterior parietal cortices. This was also true for cross-modal decoding. Furthermore, representational dissimilarity matrices of frontal and parietal areas showed that MI and ME representations formed separate clusters, but that the representational organization of action types within these clusters was identical. For most ROIs, this pattern of results best fits with a model that assumes a low-to-moderate degree of similarity between the neural patterns associated with MI and ME. Thus, neural representations of MI and ME are neither the same nor totally distinct but exhibit a similar structural geometry with respect to different types of action.

102 citations


Journal ArticleDOI
TL;DR: A main focus on the specificities of cortico-spinal modulations during MI, investigated by TMS, is provided and a brief overview of sub-cortical mechanisms gives importance to the activation of peripheral neural structures during MI.
Abstract: Motor imagery (MI) has received a lot of interest during the last decades as its chronic or acute use has demonstrated several effects on improving sport performances or skills. The development of neuroimagery techniques also helped further our understanding of the neural correlates underlying MI. While some authors showed that MI, motor execution and action observation activated similar motor cortical regions, transcranial magnetic stimulation (TMS) studies brought great insights on the role of the primary motor cortex and on the activation of the cortico-spinal pathway during MI. After defining MI and describing the TMS technique, a short report of MI activities only at cortical level is provided. Then, a main focus on the specificities of cortico-spinal modulations during MI, investigated by TMS, is provided. Finally, a brief overview of sub-cortical mechanisms gives importance to the activation of peripheral neural structures during MI.

102 citations


Journal ArticleDOI
01 Aug 2016
TL;DR: This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs, and shows a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods.
Abstract: A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain---computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods.

Proceedings ArticleDOI
01 Dec 2016
TL;DR: A deep learning approach for classification of MI-BCI that uses adaptive method to determine the threshold and it is found that the proposed framework outperforms all other competing methods in terms of reducing the maximum error.
Abstract: Over the last few decades, the use of electroencephalography (EEG) signals for motor imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep learning have also gained widespread attention and used in various application such as natural language processing, computer vision and speech processing. However, deep learning has been rarely used for MI EEG signal classification. In this paper, we present a deep learning approach for classification of MI-BCI that uses adaptive method to determine the threshold. The widely used common spatial pattern (CSP) method is used to extract the variance based CSP features, which is then fed to the deep neural network for classification. Use of deep neural network (DNN) has been extensively explored for MI-BCI classification and the best framework obtained is presented. The effectiveness of the proposed framework has been evaluated using dataset IVa of the BCI Competition III. It is found that the proposed framework outperforms all other competing methods in terms of reducing the maximum error. The framework can be used for developing BCI systems using wearable devices as it is computationally less expensive and more reliable compared to the best competing methods.

Journal ArticleDOI
TL;DR: An integrative approach of online and offline learning resulting from intense MIP in healthy participants, and underline research avenues in the motor learning/clinical domains are concluded.
Abstract: There is now compelling evidence that motor imagery (MI) promotes motor learning. While MI has been shown to influence the early stages of the learning process, recent data revealed that sleep also contributes to the consolidation of the memory trace. How such “online” and “offline” processes take place and how they interact to impact the neural underpinnings of movements has received little attention. The aim of the present review is twofold: i) providing an overview of recent applied and fundamental studies investigating the effects of MI practice on motor learning, and ii) detangling applied and fundamental findings in support of a sleep contribution to motor consolidation after MI practice. We conclude with an integrative approach of online and offline learning resulting from intense MI practice in healthy participants, and underline research avenues in the motor learning/clinical domains.

Journal ArticleDOI
TL;DR: Sorting out factors characterizing motor imagery may explain some discrepancies and variability in the findings from previous studies and will help to optimize a study design in accordance with the purpose of each study in the future.

Journal ArticleDOI
TL;DR: The role of multimodal virtual reality simulations and motor priming in an upper limb motor-imagery BCI task is investigated in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.
Abstract: The use of Brain–Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training. In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence. Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience. Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user’s profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.

Journal ArticleDOI
TL;DR: It is proposed that poor BMI control cannot be ascribed only to intrinsic limitations of EEG recordings and that specific questionnaires and mental chronometry can be used as predictors of BMI performance (without the need to record EEG activity).
Abstract: Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related to differences in MI ability. We assessed whether differences in kinesthetic and visual MI, in the behavioral accuracy of MI, and in electroencephalographic variables, were able to differentiate between high- versus low-aptitude BMI users. High-aptitude BMI users showed higher MI accuracy as captured by subjective and behavioral measurements, pointing to a prominent role of kinesthetic rather than visual imagery. Additionally, for the first time, we applied mental chronometry, a measure quantifying the degree to which imagined and executed movements share a similar temporal profile. We also identified enhanced lateralized μ-band oscillations over sensorimotor cortices during MI in high- versus low-aptitude BMI users. These findings reveal that subjective, behavioral, and EEG measurements of MI are intimately linked to BMI control. We propose that poor BMI control cannot be ascribed only to intrinsic limitations of EEG recordings and that specific questionnaires and mental chronometry can be used as predictors of BMI performance (without the need to record EEG activity).

Journal ArticleDOI
TL;DR: Systematic search of all clinical studies published in the main scientific databases concerning mental practice in stroke rehabilitation found 23 clinical trials testing different MP protocols in patients with hemiparesis, finding MP is effective when used in conjunction with conventional physical therapy for functional rehabilitation of both upper and lower limbs.
Abstract: Introduction In recent decades, many stroke rehabilitation methods have been developed. Mental practice (MP) is a dynamic state in which the subject evokes an imaginary representation of a motor action or skill in order to learn or perfect that action. Although functional imaging has shown that MP produces similar cortical activation patterns to those of movement, the clinical effectiveness of such methods in rehabilitation and functional recovery has yet to be demonstrated. Development Systematic search of all clinical studies published in the main scientific databases between December 2011 and October 2012 concerning mental practice in stroke rehabilitation. We selected 23 clinical trials testing different MP protocols in patients with hemiparesis. Conclusions MP is effective when used in conjunction with conventional physical therapy for functional rehabilitation of both upper and lower limbs, as well as for the recovery of daily activities and skills. Owing to the heterogeneity of the studies with regard to the intervention protocol, specific imagery technique, time spent practicing, patient characteristics, etc., more studies are needed in order to determine the optimal treatment protocol and patient profile.

Journal ArticleDOI
TL;DR: This study recorded multi-modal datasets consisting of MI electroencephalography signals, T1-weighted structural and resting-state functional MRI data for each subject, and a correlation analysis was used to elucidate the relationships between the fronto-parietal attention network (FPAN) and MI-BCI performance.

Journal ArticleDOI
TL;DR: The present quantitative meta-analysis summarizes and amends previous descriptions of the brain network related to MR and shows how it is modulated by top-down and bottom-up experimental factors.
Abstract: We could predict how an object would look like if we were to see it from different viewpoints. The brain network governing mental rotation (MR) has been studied using a variety of stimuli and tasks instructions. By using activation likelihood estimation (ALE) meta-analysis we tested whether different MR networks can be modulated by the type of stimulus (body vs. non body parts) or by the type of tasks instructions (motor imagery-based vs. non-motor imagery-based MR instructions). Testing for the bodily and non-bodily stimulus axis revealed a bilateral sensorimotor activation for bodily-related as compared to non bodily-related stimuli and a posterior right lateralized activation for non bodily-related as compared to bodily-related stimuli. A top-down modulation of the network was exerted by the MR tasks instructions frame with a bilateral (preferentially sensorimotor left) network for motor imagery- vs. non-motor imagery-based MR instructions and the latter activating a preferentially posterior right occipito-temporal-parietal network. The present quantitative meta-analysis summarizes and amends previous descriptions of the brain network related to MR and shows how it is modulated by top-down and bottom-up experimental factors.

Journal ArticleDOI
TL;DR: Compared with MIAAO, MISAO can enhance the excitation of sensorimotor cortex more effectively and lead to a more rapid neurorehabilitation of stroke patients.
Abstract: Background: Action observation (AO) has the potential to improve motor imagery (MI) practice in stroke patients. However, currently only a few results are available on how to use AO effectively.Objective: The aim of this study is to investigate whether MI practice can be improved more effectively by synchronous AO than by asynchronous AO.Methods: Ten patients with upper limb motor dysfunction following stroke were selected as the participants. They were divided into two groups to perform MI practice combined with a daily conventional rehabilitation for four consecutive weeks. The control group was asked to perform MI guided by asynchronous AO (MIAAO), and the experimental group was asked to perform the same MI but guided by synchronous AO (MISAO). The event-related power decrease (ERD) in sensorimotor rhythms of electroencephalograph was calculated to reflect the sensorimotor cortex activation and to assess the cortex excitability during MI. Fugl-Meyer assessment (FMA) and pinch strength test (PST) were u...

Journal ArticleDOI
TL;DR: The proposed algorithm uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals to provide an important feature for the classification of the left- and right-hand motor imagery tasks.
Abstract: Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram EEG during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition MEMD in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns SUTCCSP algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.

Journal ArticleDOI
TL;DR: During training or rehabilitation programs, actual movements should be executed and/or imagined movement duration should be controlled to avoid the negative effects of mental fatigue on motor performance.

Journal ArticleDOI
TL;DR: In conclusion, the BRI intervention induced a complex pattern of modulated corticospinal excitability, which may boost subsequent motor learning during physiotherapy.

Journal ArticleDOI
TL;DR: GF increased significantly their BOLD self‐regulation from day‐1 to day‐2 and GF,R showed the highest BOLD signal amplitude in SMA during the training, showing the varied influences of feedback, reward, and instructions on the brain.
Abstract: The learning process involved in achieving brain self-regulation is presumed to be related to several factors, such as type of feedback, reward, mental imagery, duration of training, among others. Explicitly instructing participants to use mental imagery and monetary reward are common practices in real-time fMRI (rtfMRI) neurofeedback (NF), under the assumption that they will enhance and accelerate the learning process. However, it is still not clear what the optimal strategy is for improving volitional control. We investigated the differential effect of feedback, explicit instructions and monetary reward while training healthy individuals to up-regulate the blood-oxygen-level dependent (BOLD) signal in the supplementary motor area (SMA). Four groups were trained in a two-day rtfMRI-NF protocol: GF with NF only, GF,I with NF + explicit instructions (motor imagery), GF,R with NF + monetary reward, and GF,I,R with NF + explicit instructions (motor imagery) + monetary reward. Our results showed that GF increased significantly their BOLD self-regulation from day-1 to day-2 and GF,R showed the highest BOLD signal amplitude in SMA during the training. The two groups who were instructed to use motor imagery did not show a significant learning effect over the 2 days. The additional factors, namely motor imagery and reward, tended to increase the intersubject variability in the SMA during the course of training. Whole brain univariate and functional connectivity analyses showed common as well as distinct patterns in the four groups, representing the varied influences of feedback, reward, and instructions on the brain. Hum Brain Mapp 37:3153-3171, 2016. © 2016 Wiley Periodicals, Inc.

Journal ArticleDOI
TL;DR: This study is the first to show that the quiet eye becomes longer in novices practicing a motor action, suggesting that perceptual and cognitive adaptations co-occur over the course of motor learning.
Abstract: Despite the wealth of research on differences between experts and novices with respect to their perceptual-cognitive background (e.g., mental representations, gaze behavior), little is known about the change of these perceptual-cognitive components over the course of motor learning. In the present study, changes in one’s mental representation, quiet eye behavior, and outcome performance were examined over the course of skill acquisition as it related to physical and mental practice. Novices (N = 45) were assigned to one of three conditions: physical practice, physical practice plus mental practice, and no practice. Participants in the practice groups trained on a golf putting task over the course of three days, either by repeatedly executing the putt, or by both executing and imaging the putt. Findings revealed improvements in putting performance across both practice conditions. Regarding the perceptual-cognitive changes, participants practicing mentally and physically revealed longer quiet eye durations as well as more elaborate representation structures in comparison to the control group, while this was not the case for participants who underwent physical practice only. Thus, in the present study, combined mental and physical practice led to both formation of mental representations in long-term memory and longer quiet eye durations. Interestingly, the length of the quiet eye directly related to the degree of elaborateness of the underlying mental representation, supporting the notion that the quiet eye reflects cognitive processing. This study is the first to show that the quiet eye becomes longer in novices practicing a motor action. Moreover, the findings of the present study suggest that perceptual and cognitive adaptations co-occur over the course of motor learning.

Proceedings ArticleDOI
01 Jan 2016
TL;DR: Wavelet-based methods specifically the energy-entropy feature set, gave promising results for both the classifiers among all employed feature extraction techniques.
Abstract: This paper focuses on the classification of motor imagery of the left-right hand movements from a healthy subject. Elliptic Bandpass filters are used to discard the unwanted signals. Our study was on C3 and C4 electrodes particularly for the left-right limb movements. We deployed various feature extraction techniques on the EEG data. Statistical-based, wavelet-based energy-entropy aamp; RMS, PSD based average power and bad power were performed to form the desired feature vectors. Variants of Support Vector Machines (SVM) were employed for classification and the results were also compared with Multi-layered Perceptron (MLP). Empirical results show that both SVM and MLP were suitable for such motor imagery classifications with the accuracy of 85% and 85.71% respectively. Among all employed feature extraction techniques wavelet-based methods specifically the energy-entropy feature set, gave promising results for both the classifiers.

Journal ArticleDOI
TL;DR: An abnormal motor network after stroke was revealed and the FC could serve as a biomarker of motor function recovery in stroke patients with hemiplegia and the relationship between FC and motor function assessment was studied using the resting-state fMRI.
Abstract: Resting-state functional magnetic resonance imaging (fMRI) has been used to examine the brain mechanisms of stroke patients with hemiplegia, but the relationship between functional connectivity (FC) and treatment-induced motor function recovery has not yet been fully investigated. This study aimed to identify the brain FC changes in stroke patients and study the relationship between FC and motor function assessment using the resting-state fMRI. Seventeen stroke patients with hemiplegia and fifteen healthy control subjects (HCSs) were recruited in this study. We compared the FC between the ipsilesional primary motor cortex (M1) and the whole brain of the patients with the FC of the HCSs and studied the FC changes in the patients before and after conventional rehabilitation and motor imagery therapy. Additionally, correlations between the FC change and motor function of the patients were studied. Compared to the HCSs, the FC in the patient group was significantly increased between the ipsilesional M1 and the ipsilesional inferior parietal cortex, frontal gyrus, supplementary motor area (SMA), and contralesional angular and decreased between the ipsilesional M1 and bilateral M1. After the treatment, the FC between the ipsilesional M1 and contralesional M1 increased while the FC between the ipsilesional M1 and ipsilesional SMA and paracentral lobule decreased. A statistically significant correlation was found between the FC change in the bilateral M1 and the Fugl-Meyer assessment (FMA) score change. Our results revealed an abnormal motor network after stroke and suggested that the FC could serve as a biomarker of motor function recovery in stroke patients with hemiplegia.

Journal ArticleDOI
TL;DR: The results confirmed a recently described co-activation based parcellation supporting the idea of functionally distinct subregions of left area 44 and suggested a hyperactive mechanism to stop speech motor responses.

Journal ArticleDOI
TL;DR: Results showed that the imagery protocol was equally effective as PMT in promoting motor skill acquisition, with moderate-to-large effect sizes, and support the use of motor imagery protocols in the treatment of DCD.

Posted Content
TL;DR: In this article, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively during motor imagery, where pictures of the left or right hand were used as cues for subjects to finish the motor imagery task.
Abstract: Motor imagery (MI) is a mental representation of motor behavior that has been widely used as a control method for a brain-computer interface (BCI), allowing communication for the physically impaired. The performance of MI based BCI mainly depends on the subject's ability to self-modulate EEG signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involves abstract motor tasks and is time-consuming. To improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively. In this new paradigm, pictures of the left or right hand were used as cues for subjects to finish the motor imagery task. Fourteen healthy subjects (11 male, aged 22-25 years, mean 23.6+/-1.16) participated in this study. The task was to imagine writing a Chinese character. Specifically, subjects could imagine hand movements following the sequence of writing strokes in the Chinese character. This paradigm was meant to find an effective and familiar action for most Chinese people, to provide them with a specific, extensively practiced task and help them modulate brain activity. Results showed that the writing task paradigm yielded significantly better performance than the traditional arrow paradigm (p<0.001). Questionnaire replies indicated that most subjects thought the new paradigm was easier and more comfortable. The proposed new motor imagery paradigm could guide subjects to help them modulate brain activity effectively. Results showed that there were significant improvements using new paradigm, both in classification accuracy and usability.