Right Hand Movement Research Articles

Comparison of EEG signal decomposition methods in classification of motor-imagery BCI

A brain–computer interface (BCI) provides a link between the human brain and a computer. The task of discriminating four classes (left and right hands and feet) of motor imagery movements of a simple limb-based BCI is still challenging because most imaginary movements in the motor cortex have close spatial representations. We aimed to classify binary limb movements, rather than the direction of movement within one limb. We also investigated joint time-frequency methods to improve classification accuracies. Neither of these, to our knowledge, has been investigated previously in BCI. We recorded EEG data from eleven participants, and demonstrated the classification of four classes of simple-limb motor imagery with an accuracy of 91.46% using intrinsic time-scale decomposition and 88.99% using empirical mode decomposition. In binary classifications, we achieved average accuracies of 89.90% when classifying imaginary movements of left hand versus right hand, 93.1% for left hand versus right foot, 94.00% for left hand versus left foot, 83.82% for left foot versus right foot, 97.62% for right hand versus left foot, and 95.11% for right hand versus right foot. The results show that the binary classification performance is slightly better than that of four-class classification. Our results also show that there is no significant difference in terms of spatial distribution between left and right foot motor imagery movements. There is also no difference in classification performances involving left or right foot movement. This work demonstrates that binary and four-class movements of the left and right feet and hands can be classified using recorded EEG signals of the motor cortex, and an intrinsic time-scale decomposition (ITD) feature extraction method can be used for real time brain computer interface.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another.

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee's hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee's hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

Quantitative Assessment of the Arm/Hand Movements in Parkinson's Disease Using a Wireless Armband Device.

We present an approach for quantitative assessment of the arm/hand movements in patients with Parkinson’s disease (PD), from sensor data acquired with a wearable, wireless armband device (Myo sensor). We propose new Movement Performance Indicators that can be adopted by practitioners for the quantitative evaluation of motor performance and support their clinical evaluations. In addition, specific Movement Performance Indicators can indicate the presence of the bradykinesia symptom. The study includes seventeen PD patients and sixteen age-matched controls. A set of representative arm/hand movements is defined under the supervision of movement disorder specialist. In order to assist the evaluations, and for progress monitoring purposes, as well as for assessing the amount of bradykinesia in PD, a total set of 84 Movement Performance Indicators are computed from the sensor readings. Subsequently, we investigate whether wireless armband device, with the use of the proposed Movement Performance Indicators can be utilized: (1) for objective and precise quantitative evaluation of the arm/hand movements of Parkinson’s patients, (2) for assessment of the bradykinesia motor symptom, and (3) as an adequate low-cost alternative for the sensor glove. We conducted extensive analysis of proposed Movement Performance Indicators and results are indicating following clinically relevant characteristics: (i) adequate reliability as measured by ICC; (ii) high accuracy in discrimination between the patients and controls, and between the disease stages (support to disease diagnosis and progress monitoring, respectively); (iii) substantial difference in comparison between the left-hand and the right-hand movements across controls and patients, as well as between disease stage groups; (iv) statistically significant correlation with clinical scales (tapping test and UPDRS-III Motor Score); and (v) quantitative evaluation of bradykinesia symptom. Results suggest that the proposed approach has a potential to be adopted by physicians, to afford them with quantitative, objective and precise methods and data during clinical evaluations and support the assessment of bradykinesia.

Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain-computer interface.

Motor imagery is one of the most investigated paradigms in the field of brain-computer interfaces (BCIs). The present study explored the feasibility of applying a common spatial pattern (CSP)-based algorithm for a functional near-infrared spectroscopy (fNIRS)-based motor imagery BCI. Ten participants performed kinesthetic imagery of their left- and right-hand movements while 20-channel fNIRS signals were recorded over the motor cortex. The CSP method was implemented to obtain the spatial filters specific for both imagery tasks. The mean, slope, and variance of the CSP filtered signals were taken as features for BCI classification. Results showed that the CSP-based algorithm outperformed two representative channel-wise methods for classifying the two imagery statuses using either data from all channels or averaged data from imagery responsive channels only (oxygenated hemoglobin: CSP-based: 75.3±13.1%; all-channel: 52.3±5.3%; averaged: 64.8±13.2%; deoxygenated hemoglobin: CSP-based: 72.3±13.0%; all-channel: 48.8±8.2%; averaged: 63.3±13.3%). Furthermore, the effectiveness of the CSP method was also observed for the motor execution data to a lesser extent. A partial correlation analysis revealed significant independent contributions from all three types of features, including the often-ignored variance feature. To our knowledge, this is the first study demonstrating the effectiveness of the CSP method for fNIRS-based motor imagery BCIs.

Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks.

.Neurofeedback is a method for using neural activity displayed on a computer to regulate one’s own brain function and has been shown to be a promising technique for training individuals to interact with brain–machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications. We hypothesized that fNIRS neurofeedback would enhance activity in motor cortex during a motor imagery task. Twenty-two participants performed active and imaginary right-handed squeezing movements using an elastic ball while wearing a 98-channel fNIRS device. Neurofeedback traces representing localized cortical hemodynamic responses were graphically presented to participants in real time. Participants were instructed to observe this graphical representation and use the information to increase signal amplitude. Neural activity was compared during active and imaginary squeezing with and without neurofeedback. Active squeezing resulted in activity localized to the left premotor and supplementary motor cortex, and activity in the motor cortex was found to be modulated by neurofeedback. Activity in the motor cortex was also shown in the imaginary squeezing condition only in the presence of neurofeedback. These findings demonstrate that real-time fNIRS neurofeedback is a viable platform for brain–machine interface applications.

EEG Signals Analysis for motor imagery based on Curvelet Transform

EEG-based brain-computer interface is a computer-based system provides effective communication and control channels between human brain and computer to carry out a desired action. However, classification of single-trial EEG signals and controlling a device continuously during motor imagery is a difficult task. In this paper, we propose feature extraction method for a single trial online motor imagery using curvelet transform. These curvelet coefficients were used to extract the characters from the motor imagery EEG and classify the pattern of left and right hand movement imagery by Bayesian analysis with Gaussian model. The performance of motor imagery tested by the eye dataset for BCI competition 2003. The hypothetical results presented highest classification accuracy of 96% and superior information transfer rate is obtained. Keywords: Electroencephalograph (EEG), Curvelet coefficients, Motor imagery, Bayesian classifier, Gaussian model, Brain-computer interface (BCI).

The inimitable mouth: task-dependent kinematic differences are independent of terminal precision.

Previous studies in our lab have described kinematic difference between grasp-to-eat and grasp-to-place movements, whereby participants produce smaller maximum grip apertures (MGAs) when grasping to bring the item to the mouth than when grasping to bring the item to a container near the mouth. This task difference is limited to right-handed movements, regardless of handedness; it has, therefore, been interpreted as evidence of left-hemisphere lateralization of the grasp-to-eat and other hand-to-mouth grasping movements. However, the difference in end-goal aperture may have accounted for both the kinematic signature (smaller MGAs) and their lateralized expression. Specifically, if the right hand is more sensitive to the precision requirements of secondary movements, it may have produced more precise MGAs for actions whose ultimate goal is the small-aperture mouth rather than a comparatively large aperture container. The current study addresses this question by replacing the previously-used bib with a small drinking glass whose aperture more closely resembles that of the mouth. 25 adult participants reached-to-grasp small cereal items to either (a) eat them, or (b) place them into a small-aperture glass hanging beneath their chin. Results once more showed a lateralised kinematic signature in the form of smaller MGAs for the eat action, demonstrating that the signature is not a result of lateralized sensitivity to a movement's secondary precision requirements. We discuss these results in terms of their impact on predominant theories regarding visual guidance of grasping movements.

The cortical signature of symptom laterality in Parkinson's disease.

Patients with Parkinson's disease (PD) often present with unilateral motor symptoms that eventually spread to the other side. This symptom lateralization is diagnostically important, as it serves to distinguish PD from other motor disorders with overlapping symptom profiles. Further, recent studies have shown that the side of symptom onset is important for prognosis, as there are differences in the rate of disease progression and the incidence of secondary symptoms between right- and left-dominant (RD, LD) patients. Physiologically, previous studies have shown asymmetrical decline in structure and metabolism throughout the basal ganglia, although connecting this directly to motor function has been difficult. To identify the neurophysiological basis of symptom laterality in PD, we recorded magnetoencephalography (MEG) during left- and right-hand movement paradigms in patients with PD who exhibited either RD or LD symptomatology. The beta oscillations serving these movements were then imaged using beamforming methods, and we extracted the time series of the peak voxel in the left and right primary motor cortices for each movement. In addition, each patient's symptom asymmetry was quantitated using the Unified Parkinson's Disease Rating Scale (UPDRS), which allowed the relationship between symptom asymmetry and neural asymmetry to be assessed. We found that LD patients had stronger beta suppression during movement, as well as greater post-movement beta rebound compared to patients with RD symptoms, independent of the hand that was moved. Interestingly, the asymmetry of beta activity during right-hand movement uniquely correlated with symptom asymmetry, such that the more LD the symptom profile, the more left-lateralized (i.e., contralateral to movement) the beta response; conversely, the more RD the symptom profile, the more right-lateralized (i.e., ipsilateral to movement) the beta response. This study is the first to directly probe the relationship between symptom asymmetry and the laterality of neural activity during movement in patients with PD, and suggests that LD patients have a fundamentally different and more “healthy” oscillatory pattern relative to RD patients.

Neural Network Underlying Intermanual Skill Transfer in Humans

Physical practice with one hand results in performance gains of the other (un-practiced) hand, yet the role of sensory feedback and underlying neurophysiology is unclear. Healthy subjects learned sequences of finger movements by physical training with their right hand while receiving real-time movement-based visual feedback via 3D virtual reality devices as if their immobile left hand was training. This manipulation resulted in significantly enhanced performance gain with the immobile hand, which was further increased when left-hand fingers were yoked to passively follow right-hand voluntary movements. Neuroimaging data show that, during training with manipulated visual feedback, activity in the left and right superior parietal lobule and their degree of coupling with motor and visual cortex, respectively, correlate with subsequent left-hand performance gain. These results point to a neural network subserving short-term motor skill learning and may have implications for developing new approaches for learning and rehabilitation in patients with unilateral motor deficits.

An Activation Likelihood Estimation Meta-Analysis Study of Simple Motor Movements in Older and Young Adults.

The functional neuroanatomy of finger movements has been characterized with neuroimaging in young adults. However, less is known about the aging motor system. Several studies have contrasted movement-related activity in older versus young adults, but there is inconsistency among their findings. To address this, we conducted an activation likelihood estimation (ALE) meta-analysis on within-group data from older adults and young adults performing regularly paced right-hand finger movement tasks in response to external stimuli. We hypothesized that older adults would show a greater likelihood of activation in right cortical motor areas (i.e., ipsilateral to the side of movement) compared to young adults. ALE maps were examined for conjunction and between-group differences. Older adults showed overlapping likelihoods of activation with young adults in left primary sensorimotor cortex (SM1), bilateral supplementary motor area, bilateral insula, left thalamus, and right anterior cerebellum. Their ALE map differed from that of the young adults in right SM1 (extending into dorsal premotor cortex), right supramarginal gyrus, medial premotor cortex, and right posterior cerebellum. The finding that older adults uniquely use ipsilateral regions for right-hand finger movements and show age-dependent modulations in regions recruited by both age groups provides a foundation by which to understand age-related motor decline and motor disorders.

Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI

This paper presents the influence of the voting strategy to enhance the classification rates in motor imagery of brain–computer interface (BCI) systems. The motor imagery is the three-class problem of left-hand movement imagination, right-hand movement imagination, and word generation. An algorithm based on neural networks and fuzzy theory (S-dFasArt) is used to classify spontaneous mental activities from electroencephalogram signals, in order to operate a noninvasive BCI. This algorithm allows obtaining several prediction models. The voting among these prediction results improves the success rates of the classifier method. The number of models and the size of the data set have been analyzed obtaining some recommendation rules for practitioners. An improvement of more than 12% can be expected.

Toward brain-actuated car applications: Self-paced control with a motor imagery-based brain-computer interface

This study presented a paradigm for controlling a car using an asynchronous electroencephalogram (EEG)-based brain-computer interface (BCI) and presented the experimental results of a simulation performed in an experimental environment outside the laboratory. This paradigm uses two distinct MI tasks, imaginary left- and right-hand movements, to generate a multi-task car control strategy consisting of starting the engine, moving forward, turning left, turning right, moving backward, and stopping the engine. Five healthy subjects participated in the online car control experiment, and all successfully controlled the car by following a previously outlined route. Subject S1 exhibited the most satisfactory BCI-based performance, which was comparable to the manual control-based performance. We hypothesize that the proposed self-paced car control paradigm based on EEG signals could potentially be used in car control applications, and we provide a complementary or alternative way for individuals with locked-in disorders to achieve more mobility in the future, as well as providing a supplementary car-driving strategy to assist healthy people in driving a car.

A Study on the Effect of Electrical Stimulation as a User Stimuli for Motor Imagery Classification in Brain-Machine Interface.

Functional Electrical Stimulation (FES) provides a neuroprosthetic interface to non-recovered muscle groups by stimulating the affected region of the human body. FES in combination with Brain-machine interfacing (BMI) has a wide scope in rehabilitation because this system directly links the cerebral motor intention of the users with its corresponding peripheral muscle activations. In this paper, we examine the effect of FES on the electroencephalography (EEG) during motor imagery (left- and right-hand movement) training of the users. Results suggest a significant improvement in the classification accuracy when the subject was induced with FES stimuli as compared to the standard visual one.

Central Neuronal Modulations of Motor Behaviour in Right-handed Grasping Movements at Laboratory versus Everyday Kinematics

Central Neuronal Modulations of Motor Behaviour in Right-handed Grasping Movements at Laboratory versus Everyday Kinematics

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination.

Flicker-noise spectroscopy (FNS) is a general phenomenological approach to analyzing dynamics of complex nonlinear systems by extracting information contained in chaotic signals. The main idea of FNS is to describe an information hidden in correlation links, which are present in the chaotic component of the signal, by a set of parameters. In the paper, FNS is used for the analysis of electroencephalography signal related to the hand movement imagination. The signal has been parametrized in accordance with the FNS method, and significant changes in the FNS parameters have been observed, at the time when the subject imagines the movement. For the right-hand movement imagination, abrupt changes (visible as a peak) of the parameters, calculated for the data recorded from the left hemisphere, appear at the time corresponding to the initial moment of the imagination. In contrary, for the left-hand movement imagination, the meaningful changes in the parameters are observed for the data recorded from the right hemisphere. As the motor cortex is activated mainly contralaterally to the hand, the analysis of the FNS parameters allows to distinguish between the imagination of the right- and left-hand movement. This opens its potential application in the brain–computer interface.

ID 13 – Motor cortex connectivity in cervical dystonia: An fMRI study

Objective To better understand motor cortex position in dystonia pathophysiology by its connectivity in normal population and cervical dystonia patients. Methods Functional magnetic resonance imaging has been performed in 25 healthy controls and 25 patients (all right-handed) in a motor timing task requiring simple movement of right hand – key press – to intercept a moving target. Left side premotor cortex was used as the seed for the psychophysiologic interaction model, based on the area showing decreased activity in dystonia patients compared to the healthy controls. Results Decreased connectivity to bilateral parietal cortex (mainly area 7) was found in cervical dystonia patients in comparison to the healthy controls. This difference is suggestive of lower associative information exchange in the disease, possibly leading to the lower behavioral performance in the task. Conclusions Our results point to possible impairment of communication between premotor cortex and parietal associative areas in cervical dystonia patients. This notion is well in accord with the newly emerging dystonia definition as a multiple node network pathology involving basal ganglia, cerebellum and specific cortical areas. Key message Our findings support the role of a complex pathways pathway disruption, even of multiple cortical information processing levels, in dystonia pathophysiology.

Enhancing training performance for brain–computer interface with object-directed 3D visual guidance

The accuracy of the classification of user intentions is essential for motor imagery (MI)-based brain-computer interface (BCI). Effective and appropriate training for users could help us produce the high reliability of mind decision making related with MI tasks. In this study, we aimed to investigate the effects of visual guidance on the classification performance of MI-based BCI. In this study, leveraging both the single-subject and the multi-subject BCI paradigms, we train and classify MI tasks with three different scenarios in a 3D virtual environment, including non-object-directed scenario, static-object-directed scenario, and dynamic object-directed scenario. Subjects are required to imagine left-hand or right-hand movement with the visual guidance. We demonstrate that the classification performances of left-hand and right-hand MI task have differences on these three scenarios, and confirm that both static-object-directed and dynamic object-directed scenarios could provide better classification accuracy than the non-object-directed case. We further indicate that both static-object-directed and dynamic object-directed scenarios could shorten the response time as well as be suitable applied in the case of small training data. In addition, experiment results demonstrate that the multi-subject BCI paradigm could improve the classification performance comparing with the single-subject paradigm. These results suggest that it is possible to improve the classification performance with the appropriate visual guidance and better BCI paradigm. We believe that our findings would have the potential for improving classification performance of MI-based BCI and being applied in the practical applications.

EEG pattern decoding of rhythmic individual finger imaginary movements of one hand

The results of four-class classification of the motor imagery EEG patterns corresponding to the right hand finger movements (little finger, thumb, index and middle fingers) of eight healthy subjects are presented in this study. The motor imagery of individual right-hand finger movements was executed by the subjects in a prescribed rhythm and the trials contained no external stimuli. Classification was performed by means of a specially developed two-level committee of classifiers on the basis of support vector machine and artificial neural networks at the first level and by generalizing an artificial neural network at the second level. The area under the EEG signal curve and the curve length calculated in a sliding time window for sites F3, C3, and Cz of the International 10?20 System were selected as the key features of signals from the sensorimotor and adjoining frontal cortical areas contralateral to the movements. The average accuracy of four-class singletrial classification for all subjects was 50 ± 7 [SD] (maximum, 58%) for the pair of sites F3–C3 and 46 ± 11% [SD] (maximum 62%) for the pair of sites C3–Cz with a theoretical guessing level 25%.