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The MIMED dataset is a dataset that provides raw electroencephalogram signal data for activities: raising the right-hand, lowering the right-hand, raising the left-hand, lowering the left-hand, standing, and sitting. In addition to raw data, this dataset provides feature data that undergoes a baseline reduction process. The baseline reduction process is a process to increase the value of EEG signal features. The feature values ​​of the enhanced EEG signal can be easily recognized in the classification process. The device used is Emotiv Epoc X, which consists of 14 channels. Participants involved in this experiment were 30 students from the Bali region in Indonesia. Four recording scenarios were carried out on the first day and four further scenarios on the second day. Two datasets were obtained based on the recording scenario: the motor movement and image datasets. The duration of motor execution is 40 minutes, while motor imagery is 8 minutes for each scenario.

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BACKGROUND: In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated. METHODS: Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson's correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification. RESULTS: Parietal-frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients. CONCLUSIONS: In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.

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A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execution of movements. Although the PhysioNet EEG Motor Movement/Imagery Dataset is currently the largest EEG dataset in the literature, relatively few studies have used it to decode MI trials. In the present study, we curated and cleaned this dataset to store it in an accessible format that is convenient for quick exploitation, decoding, and classification using recent integrated development environments. We dropped six subjects owing to anomalies in EEG recordings and pre-possessed the rest, resulting in 103 subjects spanning four MI and four motor execution tasks. The annotations were coded to correspond to different tasks using numerical values. The resulting dataset is stored in both MATLAB structure and CSV files to ensure ease of access and organization. We believe that improving the accessibility of this dataset will help EEG-based MI-BCI decoding and classification, enabling more reliable real-life applications. The convenience and ease of access of this dataset may therefore lead to improvements in cross-subject classification and transfer learning.

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Objective: The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets. Methods: Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared. Results: PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area. Conclusion: Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

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Objective:The electroencephalogram(EEG)signals were collected for analysis to define the differences in dy-namic functional connectivity of the brain network of related nodes in the primary motor area(M1)and pre-motor area(PMA)during motor imagination and motor execution.The relationship between muscle synergy and isolated movement was also explored. Method:Ten stroke patients with right hemiplegia and nineteen healthy adults participated in this study.All participants performed motor imagination(MI)and motor execution(ME)tasks according to visual instruc-tions.We recorded and analyzed the EEG signals at 12 sites located in Ml and PMA areas.The chosen EEG signals were filtered and analyzed based on the modified S-transform(MST).All data were normalized to avoid individual differences.Then we analyzed the data with Pearson correlation to identify the dynamic func-tional connectivity(FC)and the differences with Fisher's exact test for node degrees. Result:All the distribution trend of correlation degree of chosen node about left or right MI and ME of stroke patients was similar to that of healthy participants.Compared with the motion execution,the function connection strength and density of each node were elevated at MI,which was also consistent with healthy par-ticipants.When healthy adults underwent left hand MI,the degree of the C4 node in the Ml area was signifi-cantly higher than that of C3 on the opposite side(P＜0.05),while at right hand MI,the sum of the node de-grees of FC3 and FC1 in the left PMA area was significantly higher than that of the lateral symmetric chan-nel FC4 and FC2(P＜0.05).When the right upper limb isolated movement was performed,the node degree of C3 decreased significantly(P＜0.05). Conclusion:The major region of function connectivity of the right hand MI was in the left PMA area,and the node degree at MI was higher than ME.The functional connectivity of each node at the left hand MI was dispersed.The main channels activated by the muscle synergy are different from the isolated movement.

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Age-associated cognitive and motor decline is related to central nervous system injury in older adults. Motor imagery training (MIT), as an emerging rehabilitative intervention, can activate neural basis similar to that in actual exercise, so as to promote motor function in older adults. The complex motor skills rely on the functional integration of the cerebral cortex. Understanding the neural mechanisms underlying motor imagery in older adults would support its application in motor rehabilitation and slowing cognitive decline. Based on this, the present study used functional near infrared spectroscopy (fNIRS) to record the changes in oxygen saturation in older adults (20 participants; mean age, 64.8 ± 4.5 years) during Baduanjin motor execution (ME) and motor imagery (MI). ME significantly activated the left postcentral gyrus, while the oxy-hemoglobin concentration in the right middle temporal gyrus increased significantly during motor imagery. These results indicate that advanced ME activates brain regions related to sensorimotor function, and MI increases the activation of the frontal-parietal cortex related to vision. In older adults, MI overactivated the temporo-parietal region associated with vision, and tend to be activated in the right brain.

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INTRODUCTION: The motor-related bioelectric brain activity of healthy young and old subjects was studied to understand the effect of aging on motor execution. A visually cued finger tapping movement paradigm and high-density EEG were used to examine the time and frequency characteristics. METHODS: Twenty-two young and 22 healthy elderly adults participated in the study. Repeated trials of left and right index finger movements were recorded with a 128-channel EEG. Event-Related Spectral Perturbation (ERSP), Inter Trial Coherence (ITC), and Functional Connectivity were computed and compared between the age groups. RESULTS: An age-dependent theta and alpha band ERSP decrease was observed over the frontal-midline area. Decrease of beta band ERSP was found over the ipsilateral central-parietal regions. Significant ITC differences were found in the delta and theta bands between old and young subjects over the contralateral parietal-occipital areas. The spatial extent of increased ITC values was larger in old subjects. The movement execution of older subjects showed higher global efficiency in the delta and theta bands, and higher local efficiency and node strengths in the delta, theta, alpha, and beta bands. CONCLUSION: As functional compensation of aging, elderly motor networks involve more nonmotor, parietal-occipital, and frontal areas, with higher global and local efficiency, node strength. ERSP and ITC changes seem to be sensitive and complementary biomarkers of age-related motor execution.

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Introduction: Motor imagery (MI) is a method of imagining movement without actual movement, and its use in combination with motor execution (ME) enhances the effects of motor learning. Neurofeedback (NFB) is another method that promotes the effects of MI. This study aimed to investigate the effects of NFB on combined MI and ME (MIME) training in a standing postural control task. Methods: Sixteen participants were randomly divided into MIME and MIME + NFB groups and performed 10 trials of a postural control task on an unstable board, with nine trials of MI in between. Electroencephalogram was assessed during MI, and the MIME + NFB group received neurofeedback on the degree of MI via auditory stimulation. A postural control task using an unstable board was performed before and after the MIME task, during which postural instability was evaluated. Results: Postural instability was reduced after the MIME task in both groups. In addition, the root mean square, which indicates the sway of the unstable board, was significantly reduced in the MIME + NFB group compared to that in the MIME group. Conclusion: Our results indicate that MIME training is effective for motor learning of standing postural control. Furthermore, when MI and ME are combined, the feedback on the degree of MI enhances the learning effect.

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Brain activation during left- and right-hand motor imagery is a popular feature used for brain-computer interfaces. However, most studies so far have only considered right-handed participants in their experiments. This study aimed to investigate how handedness influences brain activation during the processes of imagining and executing simple hand movements. EEG signals were recorded using 32 channels while participants repeatedly squeezed or imagined squeezing a ball using their left, right, or both hands. The data of 14 left-handed and 14 right-handed persons were analyzed with a focus on event-related desynchronization/synchronization patterns (ERD/S). Both handedness groups showed activation over sensorimotor areas; however, the right-handed group tended to display more bilateral patterns than the left-handed group, which is in contrast to earlier research results. Furthermore, a stronger activation during motor imagery than during motor execution could be found in both groups.

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The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain-computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.

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[This corrects the article DOI: 10.3389/fnhum.2023.1134869.].

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While motor imagery (MI) is thought to be 'functionally equivalent' with motor execution (ME), the equivalence of feedforward and feedback mechanisms between the two modalities is unexplored. Here, we tested the equivalence of these mechanisms between MI and ME via two experiments designed to probe the role of somatosensory processing (Exp 1), and cognitive processing (Exp 2). All participants were engaged in a previously established force-matching task adapted for MI. A reference force was applied (on scale of 1-10, with higher numbers indicative of greater force) to one index finger while participants matched the force with their opposite index finger via ME or MI (control conditions). Participants then rated the force on the same scale of 1-10. Exp 1: Participants (N = 27) performed the task with tactile stimulation (ME+TAC, MI+TAC) in addition to control conditions. Exp 2: Participants (N = 26) performed the task in dual-task conditions (ME+COG, MI+COG) in addition to control conditions. Results indicate that (Exp 1) tactile stimulation impaired performance in ME but not MI. Dual-task conditions (Exp 2) were not shown to impair performance in either practice modality. Findings suggest that while somatosensory processing is critical for ME, it is not for MI. Overall we indicate a functional equivalence between feedforward/back mechanisms in MI and ME may not exist.

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When we interact with the environment around us, we are sometimes active participants, making directed physical motor movements and other times only mentally engaging with our environment, taking in sensory information and internally planning our next move without directed physical movement. Traditionally, cortical motor regions and key subcortical structures such as the cerebellum have been tightly linked to motor initiation, coordination, and directed motor behavior. However, recent neuroimaging studies have noted the activation of the cerebellum and wider cortical networks specifically during various forms of motor processing, including the observations of actions and mental rehearsal of movements through motor imagery. This phenomenon of cognitive engagement of traditional motor networks raises the question of how these brain regions are involved in the initiation of movement without physical motor output. Here, we will review evidence for distributed brain network activation during motor execution, observation, and imagery in human neuroimaging studies as well as the potential for cerebellar involvement specifically in motor-related cognition. Converging evidence suggests that a common global brain network is involved in both movement execution and motor observation or imagery, with specific task-dependent shifts in these global activation patterns. We will further discuss underlying cross-species anatomical support for these cognitive motor-related functions as well as the role of cerebrocerebellar communication during action observation and motor imagery.

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The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.

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BACKGROUND: This paper presents a study investigating the recognizability of multiple unilateral upper limb movements in stroke rehabilitation. METHODS: A functional magnetic experiment is employed to study motor execution (ME) and motor imagery (MI) of four movements for the unilateral upper limb: hand-grasping, hand-handling, arm-reaching, and wrist-twisting. The functional magnetic resonance imaging (fMRI) images of ME and MI tasks are statistically analyzed to delineate the region of interest (ROI). Then parameter estimation associated with ROIs for each ME and MI task are evaluated, where differences in ROIs for different movements are compared using analysis of covariance (ANCOVA). RESULTS: All movements of ME and MI tasks activate motor areas of the brain, and there are significant differences (p < 0.05) in ROIs evoked by different movements. The activation area is larger when executing the hand-grasping task instead of the others. CONCLUSION: The four movements we propose can be adopted as MI tasks, especially for stroke rehabilitation, since they are highly recognizable and capable of activating more brain areas during MI and ME.

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It has been confirmed that motor imagery (MI) and motor execution (ME) share a subset of mechanisms underlying motor cognition. In contrast to the well-studied laterality of upper limb movement, the laterality hypothesis of lower limb movement also exists, but it needs to be characterized by further investigation. This study used electroencephalographic (EEG) recordings of 27 subjects to compare the effects of bilateral lower limb movement in the MI and ME paradigms. Event-related potential (ERP) recorded was decomposed into meaningful and useful representatives of the electrophysiological components, such as N100 and P300. Principal components analysis (PCA) was used to trace the characteristics of ERP components temporally and spatially, respectively. The hypothesis of this study is that the functional opposition of unilateral lower limbs of MI and ME should be reflected in the different alterations of the spatial distribution of lateralized activity. Meanwhile, the significant ERP-PCA components of the EEG signals as identifiable feature sets were applied with support vector machine to identify left and right lower limb movement tasks. The average classification accuracy over all subjects is up to 61.85% for MI and 62.94% for ME. The proportion of subjects with significant results are 51.85% for MI and 59.26% for ME, respectively. Therefore, a potential new classification model for lower limb movement can be applied on brain computer interface (BCI) systems in the future.

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Motor simulation theory proposes a functional equivalence between motor execution (ME) and its simulation, suggesting that motor imagery (MI) is the self-intentioned simulation of one's actions. This study used functional magnetic resonance imaging (fMRI) with multivoxel pattern analysis to test whether the direction of hand movement is represented with a similar neural code between ME and MI. In our study, participants used their right hand to move an on-screen cursor in the left-right direction with a joystick or imagined the same movement without execution. The results indicated that the left-right direction as well as their modality (ME or MI) could be decoded significantly above the chance level in the presupplementary motor area (pre-SMA) and primary visual cortex (V1). Next, we used activation patterns of ME as inputs to the decoder to predict hand move directions in MI sessions and found a significantly higher-than-chance accuracy only in V1, not in pre-SMA. Moreover, the representational similarity analysis showed similar activation patterns for the same directions between ME and MI in V1 but not in pre-SMA. This study's finding indicates distinct spatial activation patterns for movement directions between ME and MI in pre-SMA.

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Motor control requires the coordination of spatiotemporally precise neural oscillations in the beta and gamma range within the primary motor cortex (M1). Recent studies have shown that motor performance can be differentially modulated based on the spectral target of noninvasive transcranial alternating current stimulation (tACS), with gamma-frequency tACS improving motor performance. However, the spectral specificity for eliciting such improvements remains unknown. Herein, we derived the peak movement-related gamma frequency in 25 healthy adults using magnetoencephalography and a motor control paradigm. These individualized peak gamma frequencies were then used for personalized sessions of tACS. All participants completed 4 sessions of high-definition (HD)-tACS (sham, low-, peak-, and high-gamma frequency) over M1 for 20 min during the performance of sequential movements of varying complexity (e.g. tapping adjacent fingers or nonadjacent fingers). Our primary findings demonstrated that individualized tACS dosing over M1 leads to enhanced motor performance/learning (i.e. greatest reduction in time to complete motor sequences) compared to nonspecific gamma-tACS in humans, which suggests that personalized neuromodulation may be advantageous to optimize behavioral outcomes.

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Channel selection is a critical part of the classification procedure for multichannel electroencephalogram (EEG)-based brain-computer interfaces (BCI). An optimized subset of electrodes reduces computational complexity and optimizes accuracy. Different tasks activate different sources in the brain and are characterized by distinctive channels. The goal of the current review is to define a subset of electrodes for each of four popular BCI paradigms: motor imagery, motor execution, steady-state visual evoked potentials and P300. Twenty-one studies have been reviewed to identify the most significant activations of cortical sources. The relevant EEG sensors are determined from the reported 3D Talairach coordinates. They are scored by their weighted mean Cohen's d and its confidence interval, providing the magnitude of the corresponding effect size and its statistical significance. Our goal is to create a knowledge-based channel selection framework with a sufficient statistical power. The core channel selection (CCS) could be used as a reference by EEG researchers and would have the advantages of practicality and rapidity, allowing for an easy implementation of semiparametric algorithms.

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