Motor Imagery Paradigm Research Articles

Decoding complex imagery hand gestures.

Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy-intended grasp prediction probability-of 64.5% for 8 different hand gestures, more than 5 times the chance level.

Daily modulation of the speed–accuracy trade-off

Goal-oriented arm movements are characterized by a balance between speed and accuracy. The relation between speed and accuracy has been formalized by Fitts’ law and predicts a linear increase in movement duration with task constraints. Up to now this relation has been investigated on a short-time scale only, that is during a single experimental session, although chronobiological studies report that the motor system is shaped by circadian rhythms. Here, we examine whether the speed–accuracy trade-off could vary during the day. Healthy adults carried out arm-pointing movements as accurately and fast as possible toward targets of different sizes at various hours of the day, and variations in Fitts’ law parameters were scrutinized. To investigate whether the potential modulation of the speed–accuracy trade-off has peripheral and/or central origins, a motor imagery paradigm was used as well. Results indicated a daily (circadian-like) variation for the durations of both executed and mentally simulated movements, in strictly controlled accuracy conditions. While Fitts’ law was held for the whole sessions of the day, the slope of the relation between movement duration and task difficulty expressed a clear modulation, with the lowest values in the afternoon. This variation of the speed–accuracy trade-off in executed and mental movements suggests that, beyond execution parameters, motor planning mechanisms are modulated during the day. Daily update of forward models is discussed as a potential mechanism.

Complete Locked-in and Locked-in Patients: Command Following Assessment and Communication with Vibro-Tactile P300 and Motor Imagery Brain-Computer Interface Tools.

Many patients with locked-in syndrome (LIS) or complete locked-in syndrome (CLIS) also need brain-computer interface (BCI) platforms that do not rely on visual stimuli and are easy to use. We investigate command following and communication functions of mindBEAGLE with 9 LIS, 3 CLIS patients and three healthy controls. This tests were done with vibro-tactile stimulation with 2 or 3 stimulators (VT2 and VT3 mode) and with motor imagery (MI) paradigms. In VT2 the stimulators are fixed on the left and right wrist and the participant has the task to count the stimuli on the target hand in order to elicit a P300 response. In VT3 mode an additional stimulator is placed as a distractor on the shoulder and the participant is counting stimuli either on the right or left hand. In motor imagery mode the participant is instructed to imagine left or right hand movement. VT3 and MI also allow the participant to answer yes and no questions. Healthy controls achieved a mean assessment accuracy of 100% in VT2, 93% in VT3, and 73% in MI modes. They were able to communicate with VT3 (86.7%) and MI (83.3%) after 2 training runs. The patients achieved a mean accuracy of 76.6% in VT2, 63.1% in VT3, and 58.2% in MI modes after 1–2 training runs. 9 out of 12 LIS patients could communicate by using the vibro-tactile P300 paradigms (answered on average 8 out of 10 questions correctly) and 3 out of 12 could communicate with the motor imagery paradigm (answered correctly 4,7 out of 5 questions). 2 out of the 3 CLIS patients could use the system to communicate with VT3 (90 and 70% accuracy). The results show that paradigms based on non-visual evoked potentials and motor imagery can be effective for these users. It is also the first study that showed EEG-based BCI communication with CLIS patients and was able to bring 9 out of 12 patients to communicate with higher accuracies than reported before. More importantly this was achieved within less than 15–20 min.

Improving motor imagery BCI with user response to feedback

In brain-computer interface (BCI) systems, the non-stationarity of brain signals is known to be a challenge for training robust classifiers as other brain processes produce signals that coincide with those resulting from the desired brain activity. One source of interference is the user's cognitive response to the provided BCI feedback. In the case of motor imagery paradigms, this feedback can for instance be a cursor moving on the screen. The response to such feedback has been shown in general to be a source of noise that can add to the non-stationarity of the brain signal; however, in this work, we show that the user’s brain response to this feedback can be used to improve the BCI performance. We first show in a motor imagery task that the user’s brain responds to the direction of cursor movement, which is different for the cursor moving towards or away from the target (i.e. BCI feedback), and this feedback-related information is present in frequency bands similar to those used in motor imagery. Next, we propose a classifier that combines the user response to feedback together with the motor imagery signal itself, and show that this combined classifier can significantly outperform a conventional motor imagery classifier. Our results show an average of 11% and up to 22% improvement in classification accuracy across 10 participants.

Brain correlates to facial motor imagery and its somatotopy in the primary motor cortex.

Motor imagery (MI) has attracted increased interest for motor rehabilitation as many studies have shown that MI shares the same neural networks as motor execution (ME). Nevertheless, MI in terms of facial movement has not been studied extensively; thus, in the present study, we investigated shared neural networks between facial motor imagery (FMI) and facial motor execution (FME). In addition, FMI somatotopy within-face was investigated between the forehead and the mouth. Functional MRI was used to examine 34 healthy individuals with ME and MI paradigms for the forehead and the mouth. The general linear model and a paired t-test were performed to define the facial area in the primary motor cortex (M1) and this area has been used to investigate somatotopy between the forehead and mouth FMI. FMI recruited similar brain motor areas as FME, but showed less neural activity in all activated regions. The facial areas in M1 were distinguishable from other body movements such as finger movement. Further investigation of this area showed that forehead and mouth imagery tended to lack a somatotopic representation for position on M1, and yet had distinct characteristics in terms of neural activity level. FMI showed different characteristics from general MI as the former exclusively activated facial processing areas. In addition, FME and FMI showed different characteristics in terms of BOLD signal level, while sharing the same neural areas. The results imply a potential usefulness of MI training for rehabilitation of facial motor disease considering that forehead and mouth somatotopy showed no clear position difference, and yet showed a significant BOLD signal intensity variation.

Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement.

motor imagery (MI) is a mental representation of motor behavior. The MI-based brain computer interfaces (BCIs) can provide communication for the physically impaired. The performance of MI-based BCI mainly depends on the subject's ability to self-modulate electroencephalogram signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involve abstract motor tasks and are 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, and mean 23.6±1.16) participated in this study. The task was to imagine writing a Chinese character. Specifically, subjects could imagine hand movements corresponding to 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 that the new paradigm was easier. 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.

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.

Transfer Learning in Brain-Computer Interfaces Abstract\uFFFDThe performance of brain-computer interfaces (BCIs) improves with the amount of avail

The performance of brain-computer interfaces (BCIs) improves with the amount of available training data, the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.

Motor imagery training for children with developmental coordination disorder--study protocol for a randomized controlled trial.

BackgroundPrevious studies have shown that the predictive control of movements is impaired in children with Developmental Coordination Disorder (DCD), most likely due to a deficit in the internal modeling of movements. Motor imagery paradigms have been used to test this internal modeling deficit. The aim of the present study is to examine whether a training focused on the mental imagery of motor skills, can help to improve the motor abilities of children with DCD.Methods/DesignA pre-post design will be used to examine the motor performance, motor imagery and motor planning abilities before and after a training of 9 weeks. Two groups will be included in this study (1) one receiving motor imagery (MI) training focused on the forward modeling of purposive actions, (2) one receiving Cognitive Orientation to daily Occupational Performance (CO-OP) training focused on identifying effective cognitive strategies that will increase motor competence. MI training will be given with the use of instruction videos of the motor skill that will be trained. Both groups will participate in 9 individual sessions of 45 min (once a week) with a paediatric physical or occupational therapist, added with homework sessions. Inclusion criteria are: (1) aged 7–12 years, (2) meeting the DSM-V criteria for DCD (motor performance substantially low (score on the m-ABC ≤ 16th percentile) and motor problems that interfere with daily life (DCDQ, and request for help at a paediatric physical or occupational therapist)). Exclusion criteria are IQ < 70 and other medical conditions causing the motor impairment.DiscussionThe results of this study will help to make treatment protocols for children with DCD more evidence-based. This study will increase our knowledge about the efficacy of both the MI training and CO-OP training, and both children with DCD and therapists will benefit from this knowledge.Trial registrationwww.trialregister.nl/NTR5471.

Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

Phase synchronization in a brain computer interface based on Mu rhythm is evaluated by means of phase lag index and weighted phase lag index. In order to detect and classify the important features reflected in brain signals during execution of mental tasks (imagination of left and right hand movement), the proposed methods are implemented on two datasets. The classification is performed using linear discriminant classifier, quadratic discriminant classifier, Mahalanobis distance classifier, k nearest neighbor and support vector machine. Classification accuracies up to 74% and 61% for phase lag index and weighted phase lag index were achieved. The results indicate that phase synchronization measures are relevant for classifying mental tasks recorded in the active state and the relaxation state from additional motor area and from the sensorimotor area. Phase lag index and weighted phase lag index methods are easy to implement, efficient, provide relevant features for the classification and can be used as an offline methods for motor imagery paradigms.

Motor imagery ability and internal representation of movement in children with probable developmental coordination disorder

It has been hypothesised that deficits in the functioning of the mirror neuron system (MNS) and internal modelling may contribute to the motor impairments associated with DCD. These processes can be explored behaviourally through motor imagery paradigms. Motor imagery proficiency of children with and without probable DCD (pDCD) was examined using a complex hand rotation task to explore whether motor imagery strategies could be used during more complex tasks. Forty-four boys aged 7–13years participated, 22 with pDCD (mean=9.90years±1.57) and 22 controls (mean=9.68years±1.53). Participants completed the task twice: with and without motor imagery instructions. Stimuli were presented in two rotational axes – palm/back, and eight 45° rotational steps. Both groups showed evidence of following the biomechanical and postural constraints of actual movements. Responses of children with pDCD were slower and less accurate than controls, with group differences increasing alongside task complexity. A greater impact of biomechanical constraints for accuracy was observed in the DCD group. The response characteristics of children with pDCD likely reflects a reduced capacity to mentally manipulate a body schema and reduced visuo-motor processing capabilities. Behaviourally, these processes are linked to MNS and internal modelling function, suggesting deficits in these systems may contribute to the movement difficulties characteristic of DCD.

A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching

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

Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following.

Standard clinical characterization of patients with disorders of consciousness (DOC) relies on observation of motor output and may therefore lead to the misdiagnosis of vegetative state or minimally conscious state in patients with preserved cognition. We used conventional electroencephalographic (EEG) measures to assess a cohort of DOC patients with and without functional magnetic resonance imaging (fMRI)-based evidence of command-following, and correlated the findings with standard clinical behavioral evaluation and brain metabolic activity. We enrolled 44 patients with severe brain injury. Behavioral diagnosis was established using standardized clinical assessments. Long-term EEG recordings were analyzed to determine wakeful background organization and presence of elements of sleep architecture. A subset of patients had fMRI testing of command-following using motor imagery paradigms (26 patients) and resting brain metabolism measurement using (18) fluorodeoxyglucose positron emission tomography (31 patients). All 4 patients with fMRI evidence of covert command-following consistently demonstrated well-organized EEG background during wakefulness, spindling activity during sleep, and relative preservation of cortical metabolic activity. In the entire cohort, EEG organization and overall brain metabolism showed no significant association with bedside behavioral testing, except in a few cases when EEG was severely abnormal. These findings suggest that conventional EEG is a simple strategy that complements behavioral and imaging characterization of DOC patients. Preservation of specific EEG features may be used to assess the likelihood of unrecognized cognitive abilities in severely brain-injured patients with very limited or no motor responses.

Non-invasive detection of high gamma band activity during motor imagery.

High gamma oscillations (70–150 Hz; HG) are rapidly evolving, spatially localized neurophysiological signals that are believed to be the best representative signature of engaged neural populations. The HG band has been best characterized from invasive electrophysiological approaches such as electrocorticography because of the increased signal-to-noise ratio that results when by-passing the scalp and skull. Despite the recent observation that HG activity can be detected non-invasively by electroencephalography (EEG), it is unclear to what extent EEG can accurately resolve the spatial distribution of HG signals during active task engagement. We have overcome some of the limitations inherent to acquiring HG signals across the scalp by utilizing individual head anatomy in combination with an inverse modeling method. We applied a linearly constrained minimum variance (LCMV) beamformer method on EEG data during a motor imagery paradigm to extract a time-frequency spectrogram at every voxel location on the cortex. To confirm spatially distributed patterns of HG responses, we contrasted overlapping maps of the EEG HG signal with blood oxygen level dependence (BOLD) functional magnetic resonance imaging (fMRI) data acquired from the same set of neurologically normal subjects during a separate session. We show that scalp-based HG band activity detected by EEG during motor imagery spatially co-localizes with BOLD fMRI data. Taken together, these results suggest that EEG can accurately resolve spatially specific estimates of local cortical high frequency signals, potentially opening an avenue for non-invasive measurement of HG potentials from diverse sets of neurologically impaired populations for diagnostic and therapeutic purposes.

Using Empirical Mode Decomposition with Spatio-Temporal dynamics to classify single-trial Motor Imagery in BCI.

This paper introduces a new signal processing method called Spatio-Temporal Multivariate Empirical Mode Decomposition (ST-MEMD). It is a new variation of Empirical Mode Decomposition (EMD) that takes spatial and temporal information into account simultaneously rather than processing each signal source in isolation. The original and new methods were tested on single-trial electroencephalogram data with a two-class problem, in this case data using the Motor Imagery paradigm in brain-computer interfacing. However, whilst ST-MEMD retained the increase in sensitivity and specificity from adding spatial data, the new temporal data made no meaningful difference in terms of performance.

A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery

BackgroundFor a self-paced motor imagery based brain–computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. New methodIn this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system. ResultsAn online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36s. Comparison with existing method(s)As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system. ConclusionsFrom our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI.

Improving the sensitivity of neurovascular coupling assessment using motor‐cognitive paradigms post‐stroke (1068.4)

Neurovascular coupling (NVC) has been used as an index of synaptic activity in health and disease. Cerebral blood flow velocity (CBFv) responses to neural stimulation can be misleading due to influences from peripheral co‐variates, leading to inaccurate NVC assessment. A dynamic multivariate autoregressive‐moving average (ARMA) model was used to calculate the contributions of blood pressure (BP), PaCO2 and the stimulus itself to changes in CBFv during active (AP), passive (PP) and motor imagery (MIP) paradigms of elbow flexion/ extension. Continuous recordings of CBFv, BP, heart rate and end‐tidal CO2 were performed in 20 stroke patients and 17 control subjects during the three paradigms. Though the paradigms led to a significant bilateral CBFv increase in both groups, differences between stroke and controls were found only during the PP (p=0.02). ARMA analysis revealed a marked BP contribution to the initial peaked CBFv response in both groups during all paradigms, as well as a decrease in the amplitude of the stimulation component after stroke (p&lt;0.001). PaCO2 did not interfere with the CBFv responses during either the MIP in controls or any of the paradigms in the stroke group. However, the responses to AP and MIP in controls were influenced by a significant reduction in PaCO2 (AP reduction of 2%; MIP reduction of 2.5%). By removing the influences of BP and PaCO2, it was possible to detect differences in the effects of stimulation between controls and strokes. We concluded that, independent of the paradigm adopted for NVC assessment, factoring out the influences of BP and PaCO2 can lead to improvements in sensitivity.Grant Funding Source: CAPES ‐ Ministry of Education of Brazil

Using a motor imagery questionnaire to estimate the performance of a Brain–Computer Interface based on object oriented motor imagery

ObjectivesThe primary objective was to test whether motor imagery (MI) questionnaires can be used to detect BCI ‘illiterate’. The second objective was to test how different MI paradigms, with and without the physical presence of the goal of an action, influence a BCI classifier. MethodsKinaesthetic (KI) and visual (VI) motor imagery questionnaires were administered to 30 healthy volunteers. Their EEG was recorded during a cue-based, simple imagery (SI) and goal oriented imagery (GOI). ResultsThe strongest correlation (Pearson r2=0.53, p=1.6e-5) was found between KI and SI, followed by a moderate correlation between KI and GOI (r2=0.33, p=0.001) and a weak correlation between VI and SI (r2=0.21, p=0.022) and VI and GOI (r2=0.17, p=0.05). Classification accuracy was similar for SI (71.1±7.8%) and GOI (70.5±5.9%) though corresponding classification features differed in 70% participants. Compared to SI, GOI improved the classification accuracy in ‘poor’ imagers while reducing the classification accuracy in ‘very good’ imagers. ConclusionThe KI score could potentially be a useful tool to predict the performance of a MI based BCI. The physical presence of the object of an action facilitates motor imagination in ‘poor’ able-bodied imagers. SignificanceAlthough this study shows results on able-bodied people, its general conclusions should be transferable to BCI based on MI for assisted rehabilitation of the upper extremities in patients.

Active, passive, and motor imagery paradigms: component analysis to assess neurovascular coupling

The association between neural activity and cerebral blood flow (CBF) has been used to assess neurovascular coupling (NVC) in health and diseases states, but little attention has been given to the contribution of simultaneous changes in peripheral covariates. We used an innovative approach to assess the contributions of arterial blood pressure (BP), PaCO2, and the stimulus itself to changes in CBF velocities (CBFv) during active (MA), passive (MP), and motor imagery (MI) paradigms. Continuous recordings of CBFv, beat-to-beat BP, heart rate, and breath-by-breath end-tidal CO2 (EtCO2) were performed in 17 right-handed subjects before, during, and after motor-cognitive paradigms performed with the right arm. A multivariate autoregressive-moving average model was used to calculate the separate contributions of BP, EtCO2, and the neural activation stimulus (represented by a metronome on-off signal) to the CBFv response during paradigms. Differences were found in the bilateral CBFv responses to MI compared with MA and MP, due to the contributions of stimulation (P < 0.05). BP was the dominant contributor to the initial peaked CBFv response in all paradigms with no significant differences between paradigms, while the contribution of the stimulus explained the plateau phase and extended duration of the CBFv responses. Separating the neural activation contribution from the influences of other covariates, it was possible to detect differences between three paradigms often used to assess disease-related NVC. Apparently similar CBFv responses to different motor-cognitive paradigms can be misleading due to the contributions from peripheral covariates and could lead to inaccurate assessment of NVC, particularly during MI.

Using a Hybrid Brain Computer Interface and Virtual Reality System to Monitor and Promote Cortical Reorganization through Motor Activity and Motor Imagery Training

Stroke is one of the leading causes of adult disability with high economical and societal costs. In recent years, novel rehabilitation paradigms have been proposed to address the life-long plasticity of the brain to regain motor function. We propose a hybrid brain-computer interface (BCI)-virtual reality (VR) system that combines a personalized motor training in a VR environment, exploiting brain mechanisms for action execution and observation, and a neuro-feedback paradigm using mental imagery as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracts. Furthermore, we present the development and validation experiments of the proposed system. More specifically, EEG data on nine naïve healthy subjects show that a simultaneous motor activity and motor imagery paradigm is more effective at engaging cortical motor areas and related networks to a larger extent. Additionally, we propose a motor imagery driven BCI-VR version of our system that was evaluated with nine different healthy subjects. Data show that users are capable of controlling a virtual avatar in a motor imagery training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system.