Imagined Movements Research Articles

The 2017 and 2018 Iranian Brain-Computer Interface Competitions.

This article summarizes the first and second Iranian brain–computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms and discuss the organizational strategies for future campaigns.

Random matrix theory for analysing the brain functional network in lower limb motor imagery.

We use random matrix theory (RMT) to investigate the statistical properties of brain functional networks in lower limb motor imagery. Functional connectivity was calculated by Pearson correlation coefficient (PCC), mutual information (MTI) and phase locking value (PLV) extracted from EEG signals. We found that when the measured subjects imagined the movements of their lower limbs the spectral density as well as the level spacings displayed deviations from the random matrix prediction. In particular, a significant difference between the left and right foot imaginary movements was observed in the maximum eigenvalue from the PCC, which can provide a theoretical basis for further study on the classification of unilateral movement of lower limbs.

Observing Actions Through Immersive Virtual Reality Enhances Motor Imagery Training.

Visual information plays an essential role in enhancing neural activity during mental practices. Previous research has shown that using different visual scenarios during mental practices that involve imagining the movement of a specific body part may result in differences in performance. Many of these scenarios utilize the concept of embodiment, or one's observation of another entity to be a part of oneself, to improve practice quality of the imagined body movement. We therefore hypothesized that applying immersive virtual reality headsets, with their ability to provide rich immersion and illusion by presenting egocentrically simulated virtual scenarios, and action observation to motor imagery practice will result in significant improvement. To explore the possible synergy between immersive systems and motor imagery, we analyzed the electroencephalogram signals of our participants as they were presented the same virtual hand movement scenario with two different mediums: an immersive virtual reality headset and a monitor display. Our experimental results provide evidence that the immersive virtual reality headsets induced improved rhythmic patterns with better discriminating spatial features from the brain compared to the monitor display. These findings suggest that the use of immersive virtual reality headsets, with the illusion and embodiment they provide, can effectively improve motor imagery training.

The Uncanny Afterlife of Dolls: Reconfiguring Personhood through Object Vivification in Gothic Film

This article proposes an investigation of the ways in which the figure of the non person in/animate body operates in Gothic cinema. It will focus on human-like objects, specifically dolls, in order to investigate the key narrative and aesthetic discourses they facilitate regarding hollowness and life. These entities establish a frightening dynamic between stasis and real or imagined (yet always unwanted) movement. In the process, they become haunting symbols of liminality that articulate particular ideas about identity and personhood, while also stressing the permeable boundaries between self and other. Gothic things undermine the normal subject-object relation and thus continually destabilize the demarcations between life and death or sanity and insanity. In so doing, they furthermore expose an irrational attitude towards existence and consciousness. Using an object-oriented approach that draws on Elaine Freedgood’s and Bill Brown’s thing theory, I explore the disruptive tendencies that in/animate agents foster in such films as Maria Lease’s Dolly Dearest (1991), Otto Preminger’s Bunny Lake is Missing (1965), and Robert Aldrich’s What Ever Happened to Baby Jane? (1962). I will focus on these case studies to examine the manner in which Gothic dolls activate uncanniness to represent subjectivity in crisis. In analyzing the figure of the doll to investigate the uneasy relationship between human beings and human-like things, this paper contributes to the growing interest surrounding the role of objects in Gothic cinema.

Electromyography features during physical and imagined standing up in healthy young adults, Phitsanulok, Thailand

PurposeTo compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.Design/methodology/approachTwenty-two participants (ages ranged from 20–29 years old) were recruited to participate in this study. Electrodes were attached to the rectus femoris, biceps femoris, tibialis anterior and the medial gastrocnemius muscles of both sides to monitor the EMG features during physical and imagined standing up. The %maximal voluntary contraction (%MVC), onset and duration were calculated.FindingsThe onset and duration of each muscle of both sides had no statistically significant differences between physical and imagined standing up (p > 0.05). The %MVC of all four muscles during physical standing up was statistically significantly higher than during imagined standing up (p < 0.05) on both sides. Moreover, the tibialis anterior muscle of both sides showed a statistically significant contraction before the other muscles (p < 0.05) during physical and imagined standing up.Originality/valueMuscles can be activated during imagined movement, and the patterns of muscle activity during physical and imagined standing up were similar. Imagined movement may be used in rehabilitation as an alternative or additional technique combined with other techniques to enhance the STS skill.

Distinct Neural Correlates Underlie Inhibitory Mechanisms of Motor Inhibition and Motor Imagery Restraint.

There is evidence to suggest that motor execution and motor imagery both involve planning and execution of the same motor plan, however, in the latter the output is inhibited. Currently, little is known about the underlying neural mechanisms of motor output inhibition during motor imagery. Uncovering the distinctive characteristics of motor imagery may help us better understand how we abstract complex thoughts and acquire new motor skills. The current study aimed to dissociate the cognitive processes involved in two distinct inhibitory mechanisms of motor inhibition and motor imagery restraint. Eleven healthy participants engaged in an imagined GO/NO-GO task during a 7 Tesla fMRI experiment. Participants planned a specific type of motor imagery, then, imagined the movements during the GO condition and restrained from making a response during the NO-GO condition. The results revealed that specific sub-regions of the supplementary motor cortex (SMC) and the primary motor cortex (M1) were recruited during the imagination of specific movements and information flowed from the SMC to the M1. Such condition-specific recruitment was not observed when motor imagery was restrained. Instead, general recruitment of the posterior parietal cortex (PPC) was observed, while the BOLD activity in the SMC and the M1 decreased below the baseline at the same time. Information flowed from the PPC to the SMC, and recurrently between the M1 and the SMC, and the M1 and the PPC. These results suggest that motor imagery involves task-specific motor output inhibition partly imposed by the SMC to the M1, while the PPC globally inhibits motor plans before they are passed on for execution during the restraint of responses.

Use of Imaginary Lower Limb Movements to Control Brain–Computer Interface Systems

This review addresses the challenge of using brain–computer interface (BCI) systems controlled by imagining lower limb movements and their use in clinical practice. There are significantly fewer studies in this area than of BCI controlled by imaginary arm movements, partly because of methodological difficulties. This review describes various modifications to BCI, most studies being directed to restoring walking function and fewer to restoring movements at the ankle joint. Restoration of locomotor function by controlling a BCI by imaginary walking to move an avatar in a virtual space is quite often used. In a number of studies, imaginary movements are accompanied by functional electrical stimulation activating the muscles carrying out the imaginary movements or triggering movements of mechanical devices (orthoses, exoskeletons). Finally, fundamentally new integrative approaches are described, such as BCI controlled by imaginary arm movements generating signals triggering movements of the avatar’s leg or an orthosis, with tactile stimulation of the forearm with the stepping rhythm. The literature contains no studies on the challenge of restoring lower limb functions in Russia.

Motor imagery recognition in electroencephalograms using convolutional neural networks

Electroencephalography is a widespread method to record brain signals with the use of electrodes located on the surface of the head. This method of recording the brain activity has become popular because it is relatively cheap, compact, and does not require implanting the electrodes directly into the brain. The article is devoted to a problem of recognition of motor imagery by electroencephalogram signals. The nature of such signals is complex. Characteristics of electroencephalograms are individual for every person, also depending on their age and mental state, as well as the presence of noise and interference. The multitude of these parameters should be taken into account when analyzing encephalograms. Artificial neural networks are a good tool for solving this class of problems. Their application allows combining the tasks of extracting, selecting and classifying features in one signal processing unit. Electroencephalograms are time signals and we note that Gramian Angular Fields and Markov Transition Field transforms are used to represent time series in the form of images. The article shows the possibility of using the Gramian Angular Fields and Markov Transition Field transformations of the electroencephalogram (EEG) signal for motor imagery recognition using examples of imaginary movements with the right and left hand, also studying the effect of the resolution of Gramian Angular Fields and Markov Transition Field images on the classification accuracy. The best classification accuracy of the EEG signal into the motion and state-of-rest classes is about 99%. In future, the research results can be applied in constructing the brain-computer interface.

Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination

Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding: these techniques, by automatically learning relevant features for class discrimination, improve EEG decoding performances without relying on handcrafted features. Nevertheless, the learned features are difficult to interpret and most of the existing CNNs introduce many trainable parameters. Here, we propose a lightweight and interpretable shallow CNN (Sinc-ShallowNet), by stacking a temporal sinc-convolutional layer (designed to learn band-pass filters, each having only the two cut-off frequencies as trainable parameters), a spatial depthwise convolutional layer (reducing channel connectivity and learning spatial filters tied to each band-pass filter), and a fully-connected layer finalizing the classification. This convolutional module limits the number of trainable parameters and allows direct interpretation of the learned spectral–spatial​ features via simple kernel visualizations. Furthermore, we designed a post-hoc gradient-based technique to enhance interpretation by identifying the more relevant and more class-specific features. Sinc-ShallowNet was evaluated on benchmark motor-execution and motor-imagery datasets and against different design choices and training strategies. Results show that (i) Sinc-ShallowNet outperformed a traditional machine learning algorithm and other CNNs for EEG decoding; (ii) The learned spectral–spatial features matched well-known EEG motor-related activity; (iii) The proposed architecture performed better with a larger number of temporal kernels still maintaining a good compromise between accuracy and parsimony, and with a trialwise rather than a cropped training strategy. In perspective, the proposed approach, with its interpretative capacity, can be exploited to investigate cognitive/motor aspects whose EEG correlates are yet scarcely known, potentially characterizing their relevant features.

AUDIOTACTILE MAPS AS A FACILITATING ELEMENT IN SPATIAL ORIENTATION AND MOBILITY

Tactile graphics represent an important means to overcome or alleviate the information deficit resulting from the loss of visual perception. The research framework of the paper is focused on a primary probe into the field of application of tactile graphics in the spatial orientation of people with visual impairment in the sense of development of spatial imagination and improvement of independent movement through the use of audiotactile maps. The chosen topic is viewed from the perspective of professionals working with people with visual impairment as well as the clients with visual impairment themselves. Partial results of the study are presented, focusing on the overall attitude of people with visual impairment to tactile graphics and their application potential, which adds to the overall picture of contemporary reality in the field of tactile graphics, with an emphasis on usability in spatial orientation and independent movement. The research has shown the considerable potential of tactile graphics in the development of spatial imagination. In connection with these conclusions, modern 3D maps with auditory elements were designed and will be also presented in the paper.

Motor system activation during motor imagery is positively related to the magnitude of cortical plastic changes following motor imagery training

Motor imagery (MI) is a cognitive motor process wherein a person consciously imagines themselves performing a movement. Previous transcranial magnetic stimulation (TMS) studies have demonstrated that physical and observational training can elicit neuroplastic adaptations in the cortical representation of movement. It has been shown that these cortical adaptations can also occur following MI training. These changes are thought to occur because of a training-dependent potentiation (i.e. increased excitability) of the trained movement representation. To test this hypothesis, the current experiment assessed the relationship between motor cortex excitability during MI and the magnitude of motor cortical adaptations following MI training. Prior to training, single-pulse TMS was used to determine the dominant direction of TMS-evoked thumb movements. The pre/post-training change in the direction of TMS-evoked thumb movements as well as the change in the first peak velocity of these thumb movements was used as an indication of the magnitude of adaptation following MI training. During the training session, participants imagined themselves moving their thumb in the opposite direction of the pre-determined dominant direction. Single-pulse TMS was also used to determine the amplitude of motor evoked potentials (MEPs) during imagined thumb movements. A strong positive relationship was found between MEP amplitude during MI of thumb movements and both measures of motor cortical adaptation following MI training. These results support the hypothesis that activation of the corticospinal motor system during MI of movements is related to the magnitude of motor cortical adaptations following MI training.

Induction of plasticity in the human motor system by motor imagery and transcranial magnetic stimulation.

Delivering transcranial magnetic brain stimulation over the motor cortex during motor imagination leads to enhanced motor output, which is selective for the muscles primarily involved in the imagined movement. This novel protocol may be useful to enhance function after damage to the motor system, such as after stroke. Several paired stimulation paradigms are known to induce plasticity in the motor cortex, reflected by changes in the motor evoked potential (MEP) following the paired stimulation. Motor imagery (MI) is capable of activating the motor system and affecting cortical excitability. We hypothesized that it might be possible to use MI in conjunction with transcranial magnetic stimulation (TMS) to induce plasticity in the human motor system. TMS was delivered to the motor cortex of healthy human subjects, and baseline MEPs recorded from forearm flexor, forearm extensor and intrinsic hand muscles. Subjects were then asked to imagine either wrist flexion or extension movements during TMS delivery (n=90 trials). Immediately after this intervention, MEP measurement was repeated. Control protocols tested the impact of imagination or TMS alone. Flexion imagination with TMS increased MEPs in flexors and an intrinsic hand muscle. Extensor imagination with TMS increased MEPs in extensor muscles only. The control paradigms did not produce significant changes. We conclude that delivering TMS during MI is capable of inducing plastic changes in the motor system. This new protocol may find utility to enhance functional rehabilitation after brain injury.

Long-term limb immobilization modulates inhibition-related electrophysiological brain activity

The effect of long-term immobilization on the motor system has been described during motor preparation, imagination or execution, when the movement has to be performed. But, what happens when the movement has to be suppressed? Does long-term limb immobilization modulate physiological responses underlying motor inhibition? Event-related potentials (ERPs) were recorded in healthy participants performing a Go/Nogo task, either with both hands free to respond (T1/T4: before/after the immobilization) or when left-hand movements were prevented by a cast (T2: as soon as the cast was positioned; T3: after one week of immobilization). In the right (control) side, N140, N2, and P3 components showed the expected greater amplitude in Nogo than in Go trials, irrespective of the timepoint. On the contrary, in the left (manipulated) side, each component of the ERP responses to Nogo trials showed specific differences across timepoints, suggesting that the inhibition-related EEG activity is significantly reduced by the presence of the cast and the duration of the immobilization. Furthermore, inhibition-related theta band activity to Nogo stimuli decreased at post-immobilization blocked session (T3-blocked). Altogether these findings can be interpreted as a consequence of the plastic changes induced by the immobilization, as also demonstrated by the cast-related corticospinal excitability modulation (investigated by using TMS) and by the decreased beta band in response to Go and Nogo trials. Thus, only if we are free to move, then inhibitory responses are fully implemented. After one week of immobilization, the amount of inhibition necessary to block the movement is lower and, consequently, inhibitory-related responses are reduced.

Rehabilitation of the Arm Motor Function in Poststroke Patients with an Exoskeleton-Controlling Brain–Computer Interface: Effect of Repeated Hospitalizations

A brain–computer interface (BCI) used to control a hand exoskeleton provides a tool for rehabilitation of the arm motor function (MF) after stroke and has proven efficacy and a potential to stimulate brain neuroplasticity. A study was made to analyze the effect of repeated rehabilitation courses with a BCI + exoskeleton (2 to 9 months after the first course) on the MF restoration in the late recovery period. MF recovery was assessed using a biomechanical analysis of the patient’s movements and clinical scales: the Fugl-Meyer Assessment (FMA) scale, the Action Research Arm Test (ARAT), and the Medical Research Council Weakness Scale sum score (MRC-SS). A positive effect of repeated rehabilitation courses with a BCI + exoskeleton on the MF recovery was observed in both patients with moderate paresis in the late recovery period and patients with severe paresis. The data may be useful for developing an appropriate protocol for the rehabilitation procedures that employ the exoskeleton controlled via a BCI with kinesthetic imagination of movements.

Recognition of three different imagined movement of the right foot based on functional near-infrared spectroscopy

Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.

Intra- and Inter-Rater Reliability of Manual Feature Extraction Methods in Movement Related Cortical Potential Analysis.

Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and inter-rater reliability were evaluated in five electroencephalography (EEG) experts who labelled the peak negativity of averaged movement related cortical potentials (MRCPs) derived from thirty datasets. Each dataset contained 50 MRCP epochs from healthy people performing cued voluntary or imagined movement, or people with stroke performing cued voluntary movement. Reliability was assessed using the intraclass correlation coefficient and standard error of measurement. Excellent intra- and inter-rater reliability was demonstrated in the voluntary movement conditions in healthy people and people with stroke. In comparison reliability in the imagined condition was low to moderate. Post-hoc secondary epoch analysis revealed that the morphology of the signal contributed to the consistency of epoch inclusion; potentially explaining the differences in reliability seen across conditions. Findings from this study may inform future research focused on developing automated labelling methods for ERP feature extraction and call to the wider community of researchers interested in utilizing ERPs as a measure of neurophysiological change or in the delivery of EEG-driven interventions.

Application of Brain-computer Interfaces in Assistive Technologies

In the paper issues of brain-computer interface applications in assistive technologies are considered in particular for robotic devices control. Noninvasive brain-computer interfaces are built based on the classification of electroencephalographic signals, which show bioelectrical activity in different zones of the brain. Such brain-computer interfaces after training are able to decode electroencephalographic patterns corresponding to different imaginary movements and patterns corresponding to different audio-visual stimulus. The requirements which must be met by brain-computer interfaces operating in real time, so that biological feedback is effective and the user's brain can correctly associate responses with events are formulated. The process of electroencephalographic signal processing in noninvasive brain-computer interface is examined including spatial and temporal filtering, artifact removal, feature selection, and classification. Descriptions and comparison of classifiers based on support vector machines, artificial neural networks, and Riemann geometry are presented. It was shown that such classifiers can provide accuracy at the level of 60-80% for recognition of imaginary movements from two to four classes. Examples of application of the classifiers to control robotic devices were presented. The approach is intended both to help healthy users to perform daily functions better and to increase the quality of life of people with movement disabilities. Tasks to increase the efficiency of technology application are formulated.

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level.

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

Alpha Frequency Intervention by Electrical Stimulation to Improve Performance in Mu-Based BCI.

The accuracy of brain-computer interfaces (BCIs) is important for effective communication and control. The mu-based BCI is one of the most widely used systems, of which the related methods to improve users' accuracy are still poorly studied, especially for the BCI illiteracy. Here, we examined a way to enhance mu-based BCI performance by electrically stimulating the ulnar nerve of the contralateral wrist at the alpha frequency (10 Hz) during left- and right-hand motor imagination in two BCI groups (literate and illiterate). We demonstrate that this alpha frequency intervention enhances the classification accuracy between left- and right-hand motor imagery from 66.41% to 81.57% immediately after intervention and to 75.28% two days after intervention in the BCI illiteracy group, while classification accuracy improves from 82.12% to 91.84% immediately after intervention and to 89.03% two days after intervention in the BCI literacy group. However, the classification accuracy did not change before and after the sham intervention (no electrical stimulation). Furthermore, the ERD on the primary sensorimotor cortex during left- or right-hand motor imagery tasks was more visible at the mu-rhythm (8-13 Hz) after alpha frequency intervention. Alpha frequency intervention increases the mu-rhythm power difference between left- and right-hand motor imagery tasks. These results provide evidence that alpha frequency intervention is an effective way to improve BCI performance by regulating the mu-rhythm which might provide a way to reduce BCI illiteracy.

EEG-based Classification of Lower Limb Motor Imagery with Brain Network Analysis

This study aims to investigate the difference in cortical signal characteristics between the left and right foot imaginary movements and to improve the classification accuracy of the experimental tasks. Raw signals were gathered from 64-channel scalp electroencephalograms of 11 healthy participants. Firstly, the cortical source model was defined with 62 regions of interest over the sensorimotor cortex (nine Brodmann areas). Secondly, functional connectivity was calculated by phase lock value for α and β rhythm networks. Thirdly, network-based statistics were applied to identify whether there existed stable and significant subnetworks that formed between the two types of motor imagery tasks. Meanwhile, ten graph theory indices were investigated for each network by t-test to determine statistical significance between tasks. Finally, sparse multinomial logistic regression (SMLR)-support vector machine (SVM), as a feature selection and classification model, was used to analyze the graph theory features. The specific time–frequency (α event-related desynchronization and β event-related synchronization) difference network between the two tasks was congregated at the midline and demonstrated significant connections in the premotor areas and primary somatosensory cortex. A few of statistically significant differences in the network properties were observed between tasks in the α and β rhythm. The SMLR-SVM classification model achieved fair discrimination accuracy between imaginary movements of the two feet (maximum 75% accuracy rate in single-trial analyses). This study reveals the network mechanism of the discrimination of the left and right foot motor imagery, which can provide a novel avenue for the BCI system by unilateral lower limb motor imagery.