Imagined Movements Research Articles

EEG Sensorimotor Rhythms Dynamics in Children with Cerebral Palsy during the Course of Neurorehabilitation, Depending in the Success of Their Imagination of Movements

EEG Sensorimotor Rhythms Dynamics in Children with Cerebral Palsy during the Course of Neurorehabilitation, Depending in the Success of Their Imagination of Movements

Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system.

This study explores the efficacy of our novel and personalized brain-computer interface (BCI) therapy, in enhancing hand movement recovery among stroke survivors. Stroke often results in impaired motor function, posing significant challenges in daily activities and leading to considerable societal and economic burdens. Traditional physical and occupational therapies have shown limitations in facilitating satisfactory recovery for many patients. In response, our study investigates the potential of motor imagery-based BCIs (MI-BCIs) as an alternative intervention. In this study, MI-BCIs translate imagined hand movements into actions using a combination of scalp-recorded electrical brain activity and signal processing algorithms. Our prior research on MI-BCIs, which emphasizes the benefits of proprioceptive feedback over traditional visual feedback and the importance of customizing the delay between brain activation and passive hand movement, led to the development of RehabSwift therapy. In this study, we recruited 12 chronic-stage stroke survivors to assess the effectiveness of our solution. The primary outcome measure was the Fugl-Meyer upper extremity (FMA-UE) assessment, complemented by secondary measures including the action research arm test, reaction time, unilateral neglect, spasticity, grip and pinch strength, goal attainment scale, and FMA-UE sensation. Our findings indicate a remarkable improvement in hand movement and a clinically significant reduction in poststroke arm and hand impairment following 18 sessions of neurofeedback training. The effects persisted for at least 4 weeks posttreatment. These results underscore the potential of MI-BCIs, particularly our solution, as a prospective tool in stroke rehabilitation, offering a personalized and adaptable approach to neurofeedback training.

Conflict, Radical Imagination, and Strategic Coalition Paradigms of Consumer Movements and Activism

Conflict, Radical Imagination, and Strategic Coalition Paradigms of Consumer Movements and Activism

After the Greenfire Revolution: Reimagining Collective Identities of the Future Wildland Fire Workforce in a Paradigm Shift for Ecological Fire Management

This concept paper explores possible collective identities for a future wildland fire workforce. Taking inspiration from the work of futurists who foresee an end to the dominant fire exclusion/suppression paradigm, and assuming that an emerging fire restoration/resilience paradigm shift replaces it, this paper engages in speculative explorations of the process and product of this paradigm shift with respect to the future collective identities of a workforce conducting ecological fire management. Social constructionist assumptions from symbolic interactionist sociological theory, Gramscian political theory’s concept of hegemony, and new social movement theory’s concept of collective identity all provide the intellectual foundations for the discussion. This concept paper argues that in order to actualize a paradigm shift, more than advances in scientific research or reforms of government policies will be required—the wildland fire community will need to become (or join) a social movement engaged in collective actions. An imaginary social movement, the “Greenfire revolution,” is invented to help illustrate how the selected theories and concepts might apply in the social construction of ecological fire management and the collective identities of its future workforce.

An EEG & eye-tracking dataset of ALS patients & healthy people during eye-tracking-based spelling system usage

This research presents a dataset consisting of electroencephalogram and eye tracking recordings obtained from six patients with amyotrophic lateral sclerosis (ALS) in a locked-in state and one hundred seventy healthy individuals. The ALS patients exhibited varying degrees of disease progression, ranging from partial mobility and weakened speech to complete paralysis and loss of speech. Despite these physical impairments, the ALS patients retained good eye function, which allowed them to use a virtual keyboard for communication. Data from ALS patients was recorded multiple times at their homes, while data from healthy individuals was recorded once in a laboratory setting. For each data recording, the experimental design involved nine recording sessions per participant, each corresponding to a common human action or demand. This dataset can serve as a valuable benchmark for several applications, such as improving spelling systems with brain-computer interfaces, investigating motor imagination, exploring motor cortex function, monitoring motor impairment progress in patients undergoing rehabilitation, and studying the effects of ALS on cognitive and motor processes.

Feasibility of Stereo EEG Based Brain Computer Interfacing in An Adult and Pediatric Cohort.

Stereoelectroencephalography (sEEG) is a mesoscale intracranial monitoring method which records from the brain volumetrically with depth electrodes. Implementation of sEEG in BCI has not been well-described across a diverse patient cohort. Across eighteen subjects, channels with high frequency broadband (HFB, 65-115Hz) power increases during hand, tongue, or foot movements during a motor screening task were provided real-time feedback based on these HFB power changes to control a cursor on a screen. Seventeen subjects established successful control of the overt motor BCI, but only nine were able to control imagery BCI with ≥ 80% accuracy. In successful imagery BCI, HFB power in the two target conditions separated into distinct subpopulations, which appear to engage unique subnetworks of the motor cortex compared to cued movement or imagery alone. sEEG-based motor BCI utilizing overt movement and kinesthetic imagery is robust across patient ages and cortical regions with substantial differences in learning proficiency between real or imagined movement.

Classification of Spatial Temporal Patterns Based on Neuromorphic Networks

This work is devoted to the problems of developing neuromorphic classifiers of spatiotemporal patterns, as well as their application in neurointerfaces. Classifiers of spatiotemporal patterns based on neural networks, support vector machines, deep neural networks, and Riemannian geometry are considered. A comparative study of these classifiers is carried out in the plane of the accuracy of multiclass recognition of electroencephalographic signals showing time-dependent bioelectrical activity in different areas of the brain during the imagination of different movements. It is shown that such classifiers can provide an accuracy of 60-80% when recognizing from two to four classes of imaginary movements. A new type of classifier based on a neuromorphic network, based on the biosimilar neurons built on the Izhikevich model, is proposed. The network processes input spike sequences and generates pulse streams of different frequencies at the outputs. The network is trained using the Supervised STDP algorithm based on labeled information containing examples of the correct recognition of the required pattern classes. The recognized pattern class is determined by the maximum frequency of the output sequence. The neuromorphic classifier showed an average classification accuracy of 90% for 4 classes of imaginary commands and a maximum of 95%. By modeling the robot control task in the virtual environment it is shown that such accuracy is sufficient for the effective use of the classifier as part of a non-invasive brain-computer interface for non-contact control of robotic devices.

Robust discrimination of multiple naturalistic same-hand movements from MEG signals with convolutional neural networks

Abstract Discriminating patterns of brain activity corresponding to multiple hand movements are a challenging problem at the limit of the spatial resolution of magnetoencephalography (MEG). Here, we use the combination of MEG, a novel experimental paradigm, and a recently developed convolutional-neural-network-based classifier to demonstrate that four goal-directed real and imaginary movements—all performed by the same hand—can be detected from the MEG signal with high accuracy: >70% for real movements and >60% for imaginary movements. Additional experiments were used to control for possible confounds and to establish the empirical chance level. Investigation of the patterns informing the classification indicated the primary contribution of signals in the alpha (8–12 Hz) and beta (13–30 Hz) frequency range in the contralateral motor areas for the real movements, and more posterior parieto–occipital sources for the imagined movements. The obtained high accuracy can be exploited in practical applications, for example, in brain–computer interface-based motor rehabilitation.

Terapi Bermain Melipat dan Menempel Kertas Origami Pada Anak Usia Dini

Introduction: The benefits of origami for young children are numerous. First, origami can help develop children's fine motor skills. The process of folding and gluing paper requires good hand-eye coordination, as well as fine motor skills. Second, origami can also help develop children's creativity and imagination. Children can create various shapes and objects just by folding paper. Method: This play therapy activity was carried out on Friday 17 November 2023 at the Al-Hidayah Ciomas Islamic Education Foundation, Panjalu sub-district. Participants in this activity were 24 preschool children and RA Al-Hidayah. The method used in this activity is divided into three stages, namely the preparation, implementation and final evaluation stages. In the preparation stage, we prepare tools, make time contracts, convey goals and topics, practice together, and prepare prizes for participants who have good work. Folded paper and hvs are used as media used by children to express their creativity. The instructions given to children are not limited, in the sense that children can express any ideas on hvs paper with origami tools as the main material. The goal is for students to have high thinking and creativity in creating creations using origami paper. Finally, in the evaluation stage, the staff reviews and assesses the children's work on how they draw using origami paper as the main basic material. Result: Playing activities for children were carried out on Friday 17 November 2023 at the Al-Hidayah Ciomas Islamic Education Foundation, Panjalu sub-district, this activity was attended by 29 children as participants. It is known that the implementation of play activities for children runs smoothly. Which was opened by the event section and continued by other implementing sections until the stage of implementing therapy for participants. And continued with evaluation, closing and documentation.Conclusion: Play therapy through origami, the Japanese art of paper folding, is an effective method for early childhood development. It helps in developing fine motor skills, creativity, imagination, and focus.

Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification

BackgroundIn recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges. New methodThis paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise. ResultsThe suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively. Comparison with existing methodsIn terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods. ConclusionThe results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.

Improving Mental Skills in Precision Sports by Using Neurofeedback Training: A Narrative Review.

Primary objectives of neurofeedback training (NFT) are to improve concentration, stress and anxiety management, and performance optimisation. This narrative review examines the role of NFT as a tool to improve mental and cognitive skills of target shooting and archery athletes. Current research discusses how neurofeedback training can act on brain waves by influencing specific EEG frequency bands in order to improve cognitive flexibility. This contribution reports studies that have applied neurofeedback protocols in precision disciplines such as archery and shooting. The results of the studies considered showed that neurofeedback can lead to faster reaction times, more sustained attention, and better emotion management, contributing significantly to athletes' performance. Furthermore, it is emphasised that neurofeedback could be combined with other techniques such as motor imagination to maximise effectiveness in precision sports training. This review emphasises the importance of future studies that focus on the integration of neurofeedback with biofeedback in neuromodulation protocols. Current perspectives and limitations of research in this area are also indicated. Neuromodulation by means of neurofeedback represents a promising strategy to improve the overall mental and cognitive abilities of target shooting and archery athletes with an interesting potential for high-level performance. Future research should focus on integrated approaches and customised protocols to optimise the use of neurofeedback in a precision sports context.

Electroencephalography-based biological and functional characteristics of spinal cord injury patients with neuropathic pain and numbness.

To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness. The EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4-8 Hz), α (8-12 Hz), and β (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation. The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p < 0.05), but higher β band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p < 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (β band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group. The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness.

Significance of EEG-electrode combinations while calculating filters with common spatial patterns.

Common spatial pattern (CSP) is a common filter technique used for pre-processing of electroencephalography (EEG) signals for imaginary movement classification tasks. It is crucial to reduce the amount of features especially in cases where few data is available. Therefore, different approaches to reduce the amount of electrodes used for CSP calculation are tried in this research. Freely available EEG datasets are used for the evaluation. To evaluate the approaches a simple classification pipeline consisting mainly of the CSP calculation and linear discriminant analysis for classification is used. A baseline over all electrodes is calculated and compared against the results of the approaches. The most promising approach is to use the ability of CSP to provide information about the origin of the created filter. An algorithm that extracts the important electrodes from the CSP utilizing these information is proposed.The results show that using subject specific electrode positions has a positive impact on accuracy for the classification task. Further, it is shown that good performing electrode combinations in one session are not necessarily good performing electrodes in another session of the same subject. In addition to the combinations calculated using the developed algorithm, 26 additional electrode combinations are proposed. These can be taken into account when selecting well-performing electrode combinations. In this research we could achieve an accuracy improvement of over 10%. Carefully selecting the correct electrode combination can improve accuracy for classifying an imaginary movement task.

From the Margins to the Centre: The Construction of the Indian Ocean Zone of Peace and the Non-Aligned Movement's Imagination of a New World Order (1964-1980)

From the Margins to the Centre: The Construction of the Indian Ocean Zone of Peace and the Non-Aligned Movement's Imagination of a New World Order (1964-1980)

Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals.

The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.

Possibilities of mirror therapy in cognitive rehabilitation after stroke

The review provides a brief overview of the history of the development of mirror therapy. Current data on the putative mechanisms of mirror therapy as well as the theory of mirror neurons are presented. The authors describe the implementation of the effects of mirror therapy in motor rehabilitation after stroke, including motor imagination or mental simulation of actions, strengthening of spatial attention and self-perception, activation of the ipsilateral corticospinal tract, reorganization of neuronal networks that influence the state of structurally intact but functionally inactive neurons. The authors reflected the prerequisites for the use of mirror therapy in the rehabilitation of cognitive impairment in poststroke patients. The results of current clinical studies and case reports of the use of mirror therapy for the rehabilitation of speech and non-speech cognitive disorders, and neglect syndrome after stroke are presented.

Resultados da imaginação motora no fortalecimento muscular em membro inferior de uma pessoa com sequela pós acidente vascular cerebral: estudo de caso

Motor Imagination (MI) is the mental representation of a motor act without the actual execution of the movement. It activates the same brain areas as real movement, even in the presence of paralysis, missing limb or vision, and can be used in the process of conserving and stimulating brain engrams in the process of motor recovery of a paralyzed limb. Method: We report a 34-year-old patient with right hemiplegia due to ischemic stroke. He performed exercises with a Physical Educator professional, twice a week, 50 minutes/session, for 19 weeks, in addition to the conventional multidisciplinary rehabilitation program. The intervention was based on MI for flexion and extension of the knee on the paralyzed side, followed by the attempt of the same active movement. Results: Active range of motion (ROM_A) of the right knee flexors started at 217° with the minimum equipment load (11 lbs). Then, the professional asked the patient to imagine that he was performing the movement and then try to perform it. After 19 weeks, ROM_A was 112° Conclusion: The ROM_A gain of 8.48° for knee flexion of the paralyzed hemibody represents a clinically important minimal difference in post-stroke patients. MI increases the cognitive demand on the brain's motor networks, increasing plasticity, resulting in motor gains that impact the prognosis of capacity and functionality, justifying its use as a training method in post-stroke recovery. MI associated with strength training in rehabilitation contributes to the recovery of post stroke sequelae.

Effects of transcranial magnetic stimulation on cortical structures during motor imagination performance in the brain-computer interface

The aftermath of a stroke can frequently result in impaired motor functions causing problems performing habitual limb movements. These movement disorders stem from damage to the cerebral cortex and disruptions to neuronal connections in the central pyramidal pathways [1]. Restoring motor skills following a stroke is a time-consuming and challenging process, requiring resources from both external medical sources and the patient. Despite these challenges, it is still feasible for patients to regain control over their limb movements. The common approach for stroke neurorehabilitation consists of therapeutic physical activities and kinesiotherapy. These methods rely on afferent information during motor tasks to repair connections between intact brain areas [2]. Through exercising the impacted limbs, synaptic rearrangement happens in the cortex, awakening dormant neurons and increasing cortical areas adjacent to inactive regions. Although these techniques can partially restore movement, a significant number of stroke patients still face impairments. It is crucial to acknowledge the limitations of the current interventions and explore new approaches for better outcomes. Traditional rehabilitation methods often do not fully restore movement control, prompting researchers to explore alternative approaches. Motor-imagery-based brain-computer interfaces (BCIs) have gained attention recently [3, 4]. They allow for integrating various feedback mechanisms, which can be combined with upper and lower limb exoskeletons. The level of control a subject has over a BCI system directly affects the recovery process [5]. Introducing transcranial magnetic stimulation (TMS) into motor-imagery BCIs shows promise for developing a unified and highly efficient approach to post-stroke rehabilitation. Twenty-nine healthy adult participants (21 females) with a mean age of 20.93±2.14 years were recruited for this experiment. All participants were right-handed and had no prior experience with brain-computer interfaces. Ethical approval was obtained from the local ethics committee of Lobachevsky State University of Nizhny Novgorod (ethical approval No. 2, dated 03/19/2021), and written informed consent was obtained from all participants. Subjects were randomly assigned to receive either a sham intervention (n=15) or active TMS (n=14). The experiment displayed tasks on a 24-inch LCD screen placed 2 meters away. Participants sat in a reclining chair with hands on adjustable armrests for EEG signal recording. Motor-imagery-based BCI training took place over two days with four daily tasks: motor performance, quasi-motor, and two motor imaginations of the dominant hand. EEG activity was recorded prior to and after completing the tasks. Each task comprised of 20 trials, each lasting 10 seconds. TMS was administered in between two motor imagination tasks, and a rest period of 2 minutes followed the stimulation. The researcher objectively monitored electromyography (EMG) in real-time during motor tasks. Certified NVX 52 amplifiers with 32 Cl/Ag electrodes positioned according to the international 10-10 system recorded electroencephalography (EEG) signals. The EEG was digitized at a sampling rate of 1000 Hz and filtered with a 50 Hz Notch filter. Disposable electrodes were used to record EMG data from musculus flexor digitorum superficialis on the right hand. EMG signals were digitized at 1000 Hz and filtered using a 50 Hz Notch filter. TMS was applied to the dorsolateral prefrontal cortex (dlPFC) using a figure-8 coil connected to a Neuro MS/D magnetic stimulator. Sham stimulation was performed with the same parameters, except the coil was tilted 90 degrees to simulate the sound of real stimulation. A nonparametric permutation test was conducted to assess the statistically significant dissimilarities between rest periods and periods subsequent to conducting motor imagery after TMS. Significant clusters of the highest event-related desynchronization (ERD) were observed. The analysis demonstrated a significant negative cluster in the theta rhythm (0–6 Hz) that was distinct from the magnetic stimulation site. Similar significant ERD clusters were identified during the first and second series of imaginary movements. The study examined alterations in motor cortex function following targeted rTMS. The results demonstrated that TMS using particular parameters resulted in the pre-activation of brain regions comparable to those stimulated during motor imagery. The implementation of this stimulation protocol increased activity in cortical regions related to motor imagery. The outcomes propose the probable effectiveness of TMS in elevating motor cortex activation during the rehabilitation process.

Change in Frequency Modulation of Electroencephalographic Activity in Imaginary and Real Limb Movement

INTRODUCTION: Investigation of electroencephalographic activity as a marker of cognitive processes in the brain traditionally focuses on the analysis in the frequency domain considering rhythms of encephalogram (EEG) as potential carriers of information needed for research. At the same time, analysis of the EEG frequency modulation effects requires improvement of approaches in the field of digital signal processing. Taking into account the fact that frequency modulation of EEG, as well as amplitude modulation, can be a marker of a number of states, it seems promising to develop a method for detecting this phenomenon and using it to evaluate a number of parameters of the brain dynamics associated with biological feedback systems. AIM: To evaluate the phenomena of frequency modulation when the user performs tasks associated with the control of external devices based on the brain–computer interface, implemented in the phenomena of electrical activity in the motor cortex area.&#x0D; MATERIALS AND METHODS: To obtain the data, a group of thirty volunteers of both genders aged 17 to 23 years was formed. The participants of the experiment had to execute four commands and repeat them in an unknown order set by the program. The experiment was conducted in two ways: physically and mentally. That is, in the first method, each command corresponded to a certain movement of a person, in the second — the same commands were executed in imagination, the movement was imagined mentally. The command was considered successfully executed if the volunteer managed to repeat and hold the position set by the program for two seconds.&#x0D; RESULTS: Based on the developed method for evaluating the frequency modulation of the EEG, the dynamics of the electrical activity of the brain was studied in the range of 9 Hz to 12 Hz when a user was performing real and imaginary movements. A comparative analysis showed that the differences were mostly recorded in the condition when the subject did not achieve the goal. At the same time, the differences to a greater extent were observed in the experiments where the subject had to make real, rather than imaginary movements. The significant differences between low- and high-frequency modulations were associated with the inability for the user to generate the requested command, which he could see by the biofeedback mechanism. It has been established that the greater the number of high-frequency restructures observed on the EEG, the smaller number of low-frequency restructures occur at the same epoch of analysis.&#x0D; CONCLUSION: The results obtained considerably expand the understanding of the mechanisms of frequency modulation of the EEG. In general, the methods and algorithms underlying the analysis that permitted their identification can be used to solve a wide range of tasks related to processing of EEG signals, including improvement of methods for detecting user errors by EEG when controlling brain-computer interface devices.

Motor cortex activation during visuomotor transformations: evoked potentials during overt and imagined movements.

Despite the prevalence of visuomotor transformations in our motor skills, their mechanisms remain incompletely understood, especially when imagery actions are considered such as mentally picking up a cup or pressing a button. Here, we used a stimulus-response task to directly compare the visuomotor transformation underlying overt and imagined button presses. Electroencephalographic activity was recorded while participants responded to highlights of the target button while ignoring the second, non-target button. Movement-related potentials (MRPs) and event-related desynchronization occurred for both overt movements and motor imagery (MI), with responses present even for non-target stimuli. Consistent with the activity accumulation model where visual stimuli are evaluated and transformed into the eventual motor response, the timing of MRPs matched the response time on individual trials. Activity-accumulation patterns were observed for MI, as well. Yet, unlike overt movements, MI-related MRPs were not lateralized, which appears to be a neural marker for the distinction between generating a mental image and transforming it into an overt action. Top-down response strategies governing this hemispheric specificity should be accounted for in future research on MI, including basic studies and medical practice.