Showing papers on "Motor imagery published in 2011"
TL;DR: Results suggest that mental practice with motor imagery does not enhance motor recovery in patients early post-stroke, and it remains to be seen whether mental practicewith motor imagery is a valid rehabilitation technique in its own right.
Abstract: This randomized controlled trial evaluated the therapeutic benefit of mental practice with motor imagery in stroke patients with persistent upper limb motor weakness. There is evidence to suggest that mental rehearsal of movement can produce effects normally attributed to practising the actual movements. Imagining hand movements could stimulate restitution and redistribution of brain activity, which accompanies recovery of hand function, thus resulting in a reduced motor deficit. Current efficacy evidence for mental practice with motor imagery in stroke is insufficient due to methodological limitations. This randomized controlled sequential cohort study included 121 stroke patients with a residual upper limb weakness within 6 months following stroke (on average <3 months post-stroke). Randomization was performed using an automated statistical minimizing procedure. The primary outcome measure was a blinded rating on the Action Research Arm test. The study analysed the outcome of 39 patients involved in 4 weeks of mental rehearsal of upper limb movements during 45-min supervised sessions three times a week and structured independent sessions twice a week, compared to 31 patients who performed equally intensive non-motor mental rehearsal, and 32 patients receiving normal care without additional training. No differences between the treatment groups were found at baseline or outcome on the Action Research Arm Test (ANCOVA statistical P=0.77, and effect size partial η2=0.005) or any of the secondary outcome measures. Results suggest that mental practice with motor imagery does not enhance motor recovery in patients early post-stroke. In light of the evidence, it remains to be seen whether mental practice with motor imagery is a valid rehabilitation technique in its own right.
271 citations
TL;DR: The majority of stroke patients could use EEG-based motor imagery BCI, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment.
Abstract: Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environ ment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (µ=0.74) was significantly lower than finger tapping by 8 patients (µ=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (µ=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (µ=0.76) were not significantly different from the first session (µ=0.72, p=0.16), or from the on-line accuracies of the third independent test session (µ=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.
251 citations
TL;DR: It is demonstrated that self-modulation of cortico-subcortical motor circuits can be achieved by PD patients through neurofeedback and may result in clinical benefits that are not attainable by motor imagery alone.
Abstract: Self-regulation of brain activity in humans based on real-time feedback of functional magnetic resonance imaging (fMRI) signal is emerging as a potentially powerful, new technique. Here, we assessed whether patients with Parkinson's disease (PD) are able to alter local brain activity to improve motor function. Five patients learned to increase activity in the supplementary motor complex over two fMRI sessions using motor imagery. They attained as much activation in this target brain region as during a localizer procedure with overt movements. Concomitantly, they showed an improvement in motor speed (finger tapping) and clinical ratings of motor symptoms (37% improvement of the motor scale of the Unified Parkinson's Disease Rating Scale). Activation during neurofeedback was also observed in other cortical motor areas and the basal ganglia, including the subthalamic nucleus and globus pallidus, which are connected to the supplementary motor area (SMA) and crucial nodes in the pathophysiology of PD. A PD control group of five patients, matched for clinical severity and medication, underwent the same procedure but did not receive feedback about their SMA activity. This group attained no control of SMA activation and showed no motor improvement. These findings demonstrate that self-modulation of cortico-subcortical motor circuits can be achieved by PD patients through neurofeedback and may result in clinical benefits that are not attainable by motor imagery alone.
246 citations
TL;DR: The results affirm the effective, three-dimensional control of the motor imagery based BCI system, and suggest its potential applications in biological navigation, neuroprosthetics, and other applications.
Abstract: Brain-computer interfaces (BCIs) allow a user to interact with a computer system using thought. However, only recently have devices capable of providing sophisticated multi-dimensional control been achieved non-invasively. A major goal for non-invasive BCI systems has been to provide continuous, intuitive, and accurate control, while retaining a high level of user autonomy. By employing electroencephalography (EEG) to record and decode sensorimotor rhythms (SMRs) induced from motor imaginations, a consistent, user-specific control signal may be characterized. Utilizing a novel method of interactive and continuous control, we trained three normal subjects to modulate their SMRs to achieve three-dimensional movement of a virtual helicopter that is fast, accurate, and continuous. In this system, the virtual helicopter's forward-backward translation and elevation controls were actuated through the modulation of sensorimotor rhythms that were converted to forces applied to the virtual helicopter at every simulation time step, and the helicopter's angle of left or right rotation was linearly mapped, with higher resolution, from sensorimotor rhythms associated with other motor imaginations. These different resolutions of control allow for interplay between general intent actuation and fine control as is seen in the gross and fine movements of the arm and hand. Subjects controlled the helicopter with the goal of flying through rings (targets) randomly positioned and oriented in a three-dimensional space. The subjects flew through rings continuously, acquiring as many as 11 consecutive rings within a five-minute period. In total, the study group successfully acquired over 85% of presented targets. These results affirm the effective, three-dimensional control of our motor imagery based BCI system, and suggest its potential applications in biological navigation, neuroprosthetics, and other applications.
222 citations
TL;DR: Brain-computer interface training appears to have yielded some improvement in motor function and brain plasticity in outpatients with chronic stroke.
Abstract: Objective To explore the effectiveness of neurorehabilitative training using an electroencephalogram-based brain- computer interface for hand paralysis following stroke. Design A case series study. Subjects Eight outpatients with chronic stroke demonstrating moderate to severe hemiparesis. Methods Based on analysis of volitionally decreased amplitudes of sensory motor rhythm during motor imagery involving extending the affected fingers, real-time visual feedback was provided. After successful motor imagery, a mechanical orthosis partially extended the fingers. Brain-computer interface interventions were carried out once or twice a week for a period of 4-7 months, and clinical and neurophysiological examinations pre- and post-intervention were compared. Results New voluntary electromyographic activity was measured in the affected finger extensors in 4 cases who had little or no muscle activity before the training, and the other participants exhibited improvement in finger function. Significantly greater suppression of the sensory motor rhythm over both hemispheres was observed during motor imagery. Transcranial magnetic stimulation showed increased cortical excitability in the damaged hemisphere. Success rates of brain-computer interface training tended to increase as the session progressed in 4 cases. Conclusion Brain-computer interface training appears to have yielded some improvement in motor function and brain plasticity. Further controlled research is needed to clarify the role of the brain-computer interface system.
211 citations
TL;DR: Empirical evidence is presented that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention, which supports the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.
Abstract: The combination of brain–computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.
175 citations
TL;DR: The results demonstrated more circuits of effective connectivity among the selected seed regions during right- hand performance than during left-hand performance, implying the influences of brain asymmetry of right-handedness on effective connectivity networks.
162 citations
TL;DR: The results demonstrate that acquisition of a sensorim motor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.
159 citations
TL;DR: The findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.
Abstract: The main purpose of electroencephalography (EEG)-based brain–computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naive participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22–29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.
156 citations
TL;DR: This review examines the measurement of motor imagery processes and explains how physiological indices of the autonomic nervous system can measure MI and how these indices may be combined to produce a measure of MI quality called the Motor Imagery Index.
Abstract: This review examines themeasurement of motor imagery (MI) processes. First, self-report measures of MI are evaluated. Next, mental chronometry measures areconsidered. Then, we explain how physiological indices of the autonomic nervous system can measure MI. Finally, we show how theseindices may be combined to produce a measure of MI quality called the Motor Imagery Index. Key Words: motor imagery, mentalimagery, psychometric measures, mental chronometry, autonomic nervous system, electrodermal and cardiac activities
154 citations
TL;DR: A control method where the beta rebound after brisk feet MI is used to control the grasp function, and a two-class SSVEP-BCI the elbow function of a 2 degrees-of-freedom artificial upper limb suggests that this is feasible.
Abstract: A Brain–Computer Interface (BCI) is a device that transforms brain signals, which are intentionally modulated by a user, into control commands. BCIs based on motor imagery (MI) and steady-state visual evoked potentials (SSVEP) can partially restore motor control in spinal cord injured patients. To determine whether these BCIs can be combined for grasp and elbow function control independently, we investigated a control method where the beta rebound after brisk feet MI is used to control the grasp function, and a two-class SSVEP-BCI the elbow function of a 2 degrees-of-freedom artificial upper limb. Subjective preferences for the BCI control were assessed with a questionnaire. The results of the initial evaluation of the system suggests that this is feasible.
TL;DR: In this article, the authors examined the potential impacts of movement duration on event-related desynchronization/synchronization (ERD/ERS) during motor imagery tasks with varying movement duration.
TL;DR: A novel method of selecting subject and class specific frequency bands based on the analysis of a channel‐frequency matrix, which is applicable to other kinds of single‐trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery‐based BCI applications.
Abstract: EEG-based discrimination among motor imagery states has been widely studied for brain-computer interfaces (BCIs) due to the great potential for real-life applications. However, in terms of designing a motor imagery-based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel method of selecting subject and class specific frequency bands based on the analysis of a channel-frequency matrix, which we call a channel-frequency map. We operate the classification process for each frequency band individually, i.e., spatial filtering, feature extraction, and classification, and determine a class label for an input EEG by considering the outputs from multiple classifiers together at the end. From our experiments on a public dataset of BCI Competition IV (2008) II-a that includes four motor imagery tasks from nine subjects, the proposed algorithm outperformed the common spatial pattern (CSP) algorithm in a broad band and a filter bank CSP algorithm on average in terms of cross-validation and session-to-session transfer rate. Furthermore, a considerable increase of classification accuracy has been achieved for certain subjects. We also would like to note that the proposed data-driven frequency bands selection method is applicable to other kinds of single-trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery-based BCI applications. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 123–130, 2011
(WCU (World Class University) Program (R31-10008-0) through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology.)
TL;DR: An offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument is investigated to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities.
Abstract: For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks. 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O2Hb] signal features (Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis). The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O2Hb] signal features comprising Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%. Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation.
TL;DR: It is shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect MI, and for working with stroke patients, a physiotherapy session could be used to obtain data for classifier set up and the BCI-rehabilitation training could start immediately.
Abstract: A new approach in motor rehabilitation after stroke is to use motor imagery (MI). To give feedback on MI performance BCIs can be used. This requires a fast and easy acquisition of a reliable classifier. Usually, for training a classifier, EEG data of motor imagery without feedback is used, but it would be advantageous if we could give feedback right from the beginning. The sensorimotor EEG changes of the motor cortex during active and passive movement and motor imagery are similar. The aim of this study is to explore, whether it is possible to use EEG data from active or passive movement to set up a classifier for the detection of motor imagery in a group of elderly persons. In addition, the activation patterns of the motor cortical areas of elderly persons were analysed during different motor tasks. EEG was recorded from three Laplacian channels over the sensorimotor cortex in a sample of 19 healthy elderly volunteers. Participants performed three different tasks in consecutive order, passive, active hand movement and hand motor imagery. Classifiers were calculated with data of every task. These classifiers were then used to detect ERD in the motor imagery data. ERD values, related to the different tasks, were calculated and analysed statistically. The performance of classifiers calculated from passive and active hand movement data did not differ significantly regarding the classification accuracy for detecting motor imagery. The EEG patterns of the motor cortical areas during the different tasks was similar to the patterns normally found in younger persons but more widespread regarding localization and frequency range of the ERD. In this study, we have shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect motor imagery. Hence, for working with stroke patients, a physiotherapy session could be used to obtain data for classifier setup and the BCI rehabilitation training could start immediately.
06 Oct 2011
TL;DR: A performance index devised in this study showed that one training day with 12 electrodes using the SVM-RFE method achieved the best balance between the number of electrodes and accuracy in the 20-session data.
Abstract: The brain-computer interface (BCI) system has been developed to assist people with motor disability. To make the system more user-friendly, it is a challenge to reduce the electrode preparation time and have a good reliability. This study aims to find a minimal set of electrodes for an individual stroke subject for motor imagery to control an assistive device using functional electrical stimulation for 20 sessions with accuracy higher than 90%. The characteristics of this minimal electrode set were evaluated with two popular algorithms: Fisher's criterion and support-vector machine recursive feature elimination (SVM-RFE). The number of calibration sessions for channel selection required for robust control of these 20 sessions was also investigated. Five chronic stroke patients were recruited for the study. Our results suggested that the number of calibration sessions for channel selection did not have a significant effect on the classification accuracy. A performance index devised in this study showed that one training day with 12 electrodes using the SVM-RFE method achieved the best balance between the number of electrodes and accuracy in the 20-session data. Generally, 8-36 channels were required to maintain accuracy higher than 90% in 20 BCI training sessions for chronic stroke patients.
TL;DR: The results are promising regarding the potential use of motor imagery practice in the rehabilitation of patients with PD.
Abstract: Background. Motor imagery has recently gained attention as a promising new rehabilitation method for patients with neurological disorders. Up to now, however, it has been unclear whether this pract...
TL;DR: It is concluded that the perceived vividness of MI is parametrically associated with neural activity within sensorimotor areas, corroborate the hypothesis that MI is an outcome of neural computations based on movement representations located within motor areas.
Abstract: The present study examined the neural basis of vivid motor imagery with parametrical functional magnetic resonance imaging. 22 participants performed motor imagery (MI) of six different right-hand movements that differed in terms of pointing accuracy needs and object involvement, i.e., either none, two big or two small squares had to be pointed at in alternation either with or without an object grasped with the fingers. After each imagery trial, they rated the perceived vividness of motor imagery on a 7-point scale. Results showed that increased perceived imagery vividness was parametrically associated with increasing neural activation within the left putamen, the left premotor cortex (PMC), the posterior parietal cortex of the left hemisphere, the left primary motor cortex, the left somatosensory cortex, and the left cerebellum. Within the right hemisphere, activation was found within the right cerebellum, the right putamen, and the right PMC. It is concluded that the perceived vividness of MI is parametrically associated with neural activity within sensorimotor areas. The results corroborate the hypothesis that MI is an outcome of neural computations based on movement representations located within motor areas.
11 Apr 2011
TL;DR: An overview of a multistage signal processing framework to tackle the main challenges in continuous control protocols for motor imagery based synchronous and self-paced BCIs and a new BCI-based game training paradigm which enables assessment of continuous control performance is introduced.
Abstract: This paper presents an overview of a multistage signal processing framework to tackle the main challenges in continuous control protocols for motor imagery based synchronous and self-paced BCIs. The BCI can be setup rapidly and automatically even when conducting an extensive search for subject-specific parameters. A new BCI-based game training paradigm which enables assessment of continuous control performance is also introduced. A range of offline results and online analysis of the new game illustrate the potential for the proposed BCI and the advantages of using the game as a BCI training paradigm.
TL;DR: A meta-analysis of previous neuroimaging studies on motor planning and different cognitive tasks showed overlap of all activations in the rostral premotor cortex, with a possible rostro-caudal functional gradient.
TL;DR: The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance (≥95% confidence level) in most cases.
Abstract: This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance (≥95% confidence level) in most cases.
TL;DR: Interestingly, the results revealed that both short and long naps resulted in similar delayed performance gains, which might suggest that the presence of slow wave and rapid eye movement sleep does not provide additional benefits for the sleep-dependent motor skill consolidation following MI practice.
Abstract: Sleep is known to contribute to motor memory consolidation. Recent studies have provided evidence that a night of sleep plays a similar functional role following motor imagery (MI), while the simple passage of time does not result in performance gains. Here, we examined the benefits of a daytime nap on motor memory consolidation after MI practice. Participants were trained by MI on an explicitly known sequence of finger movements at 11:00. Half of the participants were then subjected (at 14:00) to either a short nap (10 min of stage 2 sleep) or a long nap (60-90 min, including slow wave sleep and rapid eye movement sleep). We also collected data from both quiet and active rest control groups. All participants remained in the lab until being retested at 16:00. The data revealed that a daytime nap after imagery practice improved motor performance and, therefore, facilitated motor memory consolidation, as compared with spending a similar time interval in the wake state. Interestingly, the results revealed that both short and long naps resulted in similar delayed performance gains. The data might also suggest that the presence of slow wave and rapid eye movement sleep does not provide additional benefits for the sleep-dependent motor skill consolidation following MI practice.
TL;DR: Both left- and right-handed participants showed stronger end-state comfort effects for their right hand compared to their left hand, lending behavioral support to the left-hemisphere-dominance motion-planning hypothesis.
Abstract: Recent studies suggest that the left hemisphere is dominant for the planning of motor actions. This left-hemisphere specialization hypothesis was proposed in various lines of research, including patient studies, motor imagery studies, and studies involving neurophysiological techniques. However, most of these studies are primarily based on experiments involving right-hand-dominant participants. Here, we present the results of a behavioral study with left-hand-dominant participants, which follows up previous work in right-hand-dominant participants. In our experiment, participants grasped CD casings and replaced them in a different, pre-cued orientation. Task performance was measured by the end-state comfort effect, i.e., the anticipated degree of physical comfort associated with the posture that is planned to be adopted at movement completion. Both left- and right-handed participants showed stronger end-state comfort effects for their right hand compared to their left hand. These results lend behavioral support to the left-hemisphere-dominance motion-planning hypothesis.
TL;DR: The results demonstrated that motor imagery training could improve motor performance and more importantly, the brain functional alterations induced by training were found in the fusiform gyrus for both tasks.
TL;DR: Results indicate that other areas than the primary motor area may be more reliably activated during motor imagery and the premotor cortex may be a better area to implant an invasive BCI.
Abstract: For the development of minimally invasive brain-computer interfaces (BCIs), it is important to accurately localize the area of implantation. Using fMRI, we investigated which brain areas are involved in motor imagery. Twelve healthy subjects performed a motor execution and imagery task during separate fMRI and EEG measurements. fMRI results showed that during imagery, premotor and parietal areas were most robustly activated in individual subjects, but surprisingly, no activation was found in the primary motor cortex. EEG results showed that spectral power decreases in contralateral sensorimotor rhythms (8-24 Hz) during both movement and imagery. To further verify the involvement of the motor imagery areas found with fMRI, one epilepsy patient performed the same task during both fMRI and ECoG recordings. Significant ECoG low (8-24 Hz) and high (65-95 Hz) frequency power changes were observed selectively on premotor cortex and these co-localized with fMRI. During a subsequent BCI task, excellent performance (91%) was obtained based on ECoG power changes from the localized premotor area. These results indicate that other areas than the primary motor area may be more reliably activated during motor imagery. Specifically, the premotor cortex may be a better area to implant an invasive BCI.
TL;DR: It is argued that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of “potential actions.”
Abstract: In the last years, optimal control theory (OCT) has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioural neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the ‘degrees of freedom problem’, the common core of production, observation, reasoning, and learning of ‘actions’. OCT, directly derived from engineering design techniques of control systems quantifies task goals as ‘cost functions’ and uses the sophisticated formal tools of optimal control to obtain desired behaviour (and predictions). We propose an alternative ‘softer’ approach (PMP: Passive Motion Paradigm) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt and overt) are the consequences of an internal simulation process that ‘animates’ the body schema with the attractor dynamics of force fields induced by the goal and task specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task oriented constraints ‘at runtime’, hence solving the ‘degrees of freedom problem’ without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only to shape motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of ‘potential actions’. In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory, mirror neurons) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures.
TL;DR: A simple 2-class self-paced system is adequate with the novel control protocol, resulting in a better transition from offline training to online control, and the effectiveness of the proposed self- paced BCI for robotic wheelchair control is demonstrated.
Abstract: This paper presents a simple self-paced motor imagery based brain-computer interface (BCI) to control a robotic wheelchair. An innovative control protocol is proposed to enable a 2-class self-paced BCI for wheelchair control, in which the user makes path planning and fully controls the wheelchair except for the automatic obstacle avoidance based on a laser range finder when necessary. In order for the users to train their motor imagery control online safely and easily, simulated robot navigation in a specially designed environment was developed. This allowed the users to practice motor imagery control with the core self-paced BCI system in a simulated scenario before controlling the wheelchair. The self-paced BCI can then be applied to control a real robotic wheelchair using a protocol similar to that controlling the simulated robot. Our emphasis is on allowing more potential users to use the BCI controlled wheelchair with minimal training; a simple 2-class self paced system is adequate with the novel control protocol, resulting in a better transition from offline training to online control. Experimental results have demonstrated the usefulness of the online practice under the simulated scenario, and the effectiveness of the proposed self-paced BCI for robotic wheelchair control.
TL;DR: The study shows that motor imagery can recover in the first weeks after stroke, and indicates that a group of patients who might not be initially selected for mental practice can, still later in the rehabilitation process, participate in mental practice programs.
Abstract: Objective. To investigate whether motor imagery ability recovers in stroke patients and to see what the relationship is between different types of imagery and motor functioning after stroke. Methods. 12 unilateral stroke patients were measured at 3 and 6 weeks poststroke on 3 mental imagery tasks. Arm-hand function was evaluated using the Utrecht Arm-Hand task and the Brunnstrom Fugl-Meyer Scale. Age-matched healthy individuals (N = 10) were included as controls. Results. Implicit motor imagery ability and visual motor imagery ability improved significantly at 6 weeks compared to 3 weeks poststroke. Conclusion. Our study shows that motor imagery can recover in the first weeks after stroke. This indicates that a group of patients who might not be initially selected for mental practice can, still later in the rehabilitation process, participate in mental practice programs. Moreover, our study shows that mental imagery modalities can be differently affected in individual patients and over time.
TL;DR: Findings add to the general notion that there is a decline in the ability to mentally represent action with advanced age by estimating whether an object is within reach or out of grasp.
TL;DR: This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment.
Abstract: This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment. The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world. The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states, and state switch is achieved in a sequential manner. In the current system, imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously, while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm. The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI. Subjects obtained similar performances in the hybrid and single control tasks, which indicates the hybrid control strategy works well in the virtual environment.