Neural correlates of motor imagery and execution in real-world dynamic behavior: evidence for similarities and differences.

A large body of evidence shows that motor imagery and action execution behaviors result from overlapping neural substrates, even in the absence of overt movement during motor imagery. To date it is unclear how neural activations in motor imagery and execution compare for naturalistic whole-body movements, such as walking. Neuroimaging studies have not directly compared imagery and execution during dynamic walking movements. Here we recorded brain activation with mobile EEG during walking compared to during imagery of walking, with mental counting as a control condition. We asked 24 healthy participants to either walk six steps on a path, imagine taking six steps, or mentally count from one to six. We found beta and alpha power modulation during motor imagery resembling action execution patterns; a correspondence not found performing the control task of mental counting. Neural overlap occurred early in the execution and imagery walking actions, suggesting activation of shared action representations. Remarkably, a distinctive walking-related beta rebound occurred both during action execution and imagery at the end of the action suggesting that, like actual walking, motor imagery involves resetting or inhibition of motor processes. However, we also found that motor imagery elicits a distinct pattern of more distributed beta activity, especially at the beginning of the task. These results indicate that motor imagery and execution of naturalistic walking involve shared motor-cognitive activations, but that motor imagery requires additional cortical resources.

Safety and efficacy of motor imagery-based physical activity in high-risk pregnancy: A randomized controlled study.

OBJECTIVE: This study aimed to investigate the acute effects of motor imagery-based physical activity on maternal well-being, maternal blood pressure, heart rate, oxygen saturation, fetal heart rate, and uterine contractions in women with high-risk pregnancies. METHODS: This randomized controlled trial was conducted in Izmir Tepecik Education and Research Hospital from August 2023 to January 2024. Seventy-six women with high-risk pregnancies were randomized into two groups: a motor imagery group (n = 38, diaphragmatic-breathing exercise and motor imagery-based physical activity) and a control group (n = 38, diaphragmatic-breathing exercise). Maternal well-being was determined using the Numerical Rating Scale-11. Digital sphygmomanometry was used to measure maternal heart rate and blood pressure, pulse oximetry for oxygen saturation, and cardiotocography for fetal heart rate and uterine contractions. Assessments were performed pre-intervention, mid-intervention, and post-intervention. RESULTS: There were no significant differences in baseline characteristics between groups (P > 0.05). There was a significant main effect of time in terms of maternal well-being and maternal heart rate (P = 0.001 and P = 0.015). In addition, there was a significant main effect of the group on oxygen saturation (P = 0.025). The overall group-by-time interaction was significant for maternal well-beingm with an effect size of 0.05 (P = 0.041). CONCLUSION: The combination of diaphragmatic-breathing exercises and a motor imagery-based physical activity program in women with high-risk pregnancies was determined to have no adverse effects on the fetus, did not induce uterine contractions, and resulted in a significant improvement in maternal well-being and oxygen saturation. Thus, imagery-based physical activity can be used in high-risk pregnancies where physical activity and exercise are not recommended.

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.

A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces.

OBJECTIVE: Brain switches provide a tangible solution to asynchronized brain-computer interface (aBCI), which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography (EEG) signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch. APPROACH: Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model. MAIN RESULTS: Our motor imagery(MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/hour was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6s. SIGNIFICANCE: This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/hour, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time (RT) was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.

Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.

OBJECTIVE: Motor Imagery (MI) represents one major paradigm of Brain-Computer Interfaces (BCIs) in which users rely on their Electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technology. APPROACH: This study focuses on enhancing cross-subject MI EEG classification using EEG spectrum images. The proposed calibration-free approach employs deep learning techniques for MI classification and Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed WGAN generates synthetic spectrum images from the recorded MI-EEG to expand the training dataset; aiming to enhance the classifier's performance. The proposed approach eliminates the need for any calibration data from the target subject, making it more suitable for real-world applications. MAIN RESULTS: To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2A, and IV-1 benchmark datasets, employing leave one-subject out validation. Our results demonstrate that using the proposed modified VGG-CNN classifier in addition to WGAN-generated data for augmentation leads to an enhancement in cross-subject accuracy outperforming state-of-the-art methods. SIGNIFICANCE: This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications.

The effect of action observation and motor imagery on jumping and perceived performance.

Introduction: Action observation (AO) and motor imagery (MI) are cognitive processes that involve mentally rehearsing and simulating movements without physically performing them. However, the need for the evidence to support influence of imagery on performance is increasing. This study aims to investigate the impact of combining motor imagery with action observation on athletes' performance and performance perception. Method: Using a pre-test post-test design with a factorial setup, participants were randomly assigned to experimental and control groups. A pre-research power analysis determined the sample size, resulting in 21 voluntary participants (10 male). Opto Jump device recorded drop jump performance measurements, while participants predicted their performance post-motor imagery and action observation practices. The experimental group underwent an 8-week AOMI intervention program, involving 24-minute motor imagery sessions during video observation thrice weekly. Post-test measurements were taken after the intervention. Results: Results indicated no significant performance increase in the experimental group post-intervention, yet the group showed enhanced performance estimation following the video observation, but not in motor imagery condition. Conversely, this improvement was absent in the control group. Discussion: Although AOMI intervention didn't enhance physical performance, it has positively affected athletes' perception toward their performance. The findings are discussed in relation to existing literature.

Intentional binding for noninvasive BCI control.

Objective. Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation.Approach. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm.Main results. Showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.Significance. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.

Characterization and classification of kinesthetic motor imagery levels.

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Assessment of body Schema distortions in early-onset schizophrenia.

Distorted body representations play a major role in the onset and maintenance of Schizophrenia. However, these distortions are difficult to assess because explicit assessments can provoke intense fears about the body and require a good insight. We proposed an implicit motor imagery task to a 14-year-old girl with Early-Onset Schizophrenia. The test consisted of presenting different openings varying in width. For each aperture, the young girl has to say if she could pass through without turning her shoulders. A critical aperture is determined as the first aperture for which she considered she could no longer pass, compared to her shoulders' width. The girl perceived herself as 51 % wider than she was, indicating a significant oversized body schema. The implicit assessments of body schema generate less anxiety and does not require a great level of insight; moreover, those are promising tools for early detection of disease in prodromal phases of Schizophrenia and assistance with differential diagnosis.

[A study on the effects of transcranial direct current stimulation combined with motor imagery on brain function based on electroencephalogram and near infrared spectrum].

Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.

Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.

A motor imagery brain-computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.

Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end-to-end capabilities within BCIs based on motor imagery (MI), this paper introduces STaRNet, a novel model that integrates multi-scale spatio-temporal convolutional neural networks (CNNs) with Riemannian geometry. Initially, STaRNet integrates a multi-scale spatio-temporal feature extraction module that captures both global and local features, facilitating the construction of Riemannian manifolds from these comprehensive spatio-temporal features. Subsequently, a matrix logarithm operation transforms the manifold-based features into the tangent space, followed by a dense layer for classification. Without preprocessing, STaRNet surpasses state-of-the-art (SOTA) models by achieving an average decoding accuracy of 83.29% and a kappa value of 0.777 on the BCI Competition IV 2a dataset, and 95.45% accuracy with a kappa value of 0.939 on the High Gamma Dataset. Additionally, a comparative analysis between STaRNet and several SOTA models, focusing on the most challenging subjects from both datasets, highlights exceptional robustness of STaRNet. Finally, the visualizations of learned frequency bands demonstrate that temporal convolutions have learned MI-related frequency bands, and the t-SNE analyses of features across multiple layers of STaRNet exhibit strong feature extraction capabilities. We believe that the accurate, robust, and end-to-end capabilities of the STaRNet will facilitate the advancement of BCIs.

Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.

Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.

Actual and Imagined Music-Cued Gait Training in People with Multiple Sclerosis: A Double-Blind Randomized Parallel Multicenter Trial.

BACKGROUND: Actual and imagined cued gait trainings have not been compared in people with multiple sclerosis (MS). OBJECTIVE: To analyze the effects of cued motor imagery (CMI), cued gait training (CGT), and combined CMI and cued gait training (CMI-CGT) on motor, cognitive, and emotional functioning, and health-related quality of life in people with MS. METHODS: In this double-blind randomized parallel-group multicenter trial, people with MS were randomized (1:1:1) to CMI, CMI-CGT, or CGT for 30 minutes, 4×/week for 4 weeks. Patients practiced at home, using recorded instructions, and supported by ≥6 phone calls. Data were collected at weeks 0, 4, and 13. Co-primary outcomes were walking speed and distance, analyzed by intention-to-treat. Secondary outcomes were global cognitive impairment, anxiety, depression, suicidality, fatigue, HRQoL, motor imagery ability, music-induced motivation, pleasure and arousal, self-efficacy, and cognitive function. Adverse events and falls were continuously monitored. RESULTS: Of 1559 screened patients, 132 were randomized: 44 to CMI, 44 to CMI-CGT, and 44 to CGT. None of the interventions demonstrated superiority in influencing walking speed or distance, with negligible effects on walking speed (η2 = 0.019) and distance (η2 = 0.005) observed in the between-group comparison. Improvements in walking speed and walking distance over time corresponded to large effects for CMI, CMI-CGT, and CGT (η2 = 0.348 and η2 = 0.454 respectively). No severe study-related adverse events were reported. CONCLUSIONS: CMI-GT did not lead to improved walking speed and distance compared with CMI and CGT alone in people with MS. Lack of a true control group represents a study limitation. TRIAL REGISTRATION: German Clinical Trials Register, DRKS00023978.

Protocol for a home-based self-delivered prehabilitation intervention to proactively reduce fall risk in older adults: a pilot randomized controlled trial of transcranial direct current stimulation and motor imagery.

BACKGROUND: Several changes occur in the central nervous system with increasing age that contribute toward declines in mobility. Neurorehabilitation has proven effective in improving motor function though achieving sustained behavioral and neuroplastic adaptations is more challenging. While effective, rehabilitation usually follows adverse health outcomes, such as injurious falls. This reactive intervention approach may be less beneficial than prevention interventions. Therefore, we propose the development of a prehabilitation intervention approach to address mobility problems before they lead to adverse health outcomes. This protocol article describes a pilot study to examine the feasibility and acceptability of a home-based, self-delivered prehabilitation intervention that combines motor imagery (mentally rehearsing motor actions without physical movement) and neuromodulation (transcranial direct current stimulation, tDCS; to the frontal lobes). A secondary objective is to examine preliminary evidence of improved mobility following the intervention. METHODS: This pilot study has a double-blind randomized controlled design. Thirty-four participants aged 70-95 who self-report having experienced a fall within the prior 12 months or have a fear of falling will be recruited. Participants will be randomly assigned to either an active or sham tDCS group for the combined tDCS and motor imagery intervention. The intervention will include six 40-min sessions delivered every other day. Participants will simultaneously practice the motor imagery tasks while receiving tDCS. Those individuals assigned to the active group will receive 20 min of 2.0-mA direct current to frontal lobes, while those in the sham group will receive 30 s of stimulation to the frontal lobes. The motor imagery practice includes six instructional videos presenting different mobility tasks related to activities of daily living. Prior to and following the intervention, participants will undergo laboratory-based mobility and cognitive assessments, questionnaires, and free-living activity monitoring. DISCUSSION: Previous studies report that home-based, self-delivered tDCS is safe and feasible for various populations, including neurotypical older adults. Additionally, research indicates that motor imagery practice can augment motor learning and performance. By assessing the feasibility (specifically, screening rate (per month), recruitment rate (per month), randomization (screen eligible who enroll), retention rate, and compliance (percent of completed intervention sessions)) and acceptability of the home-based motor imagery and tDCS intervention, this study aims to provide preliminary data for planning larger studies. TRIAL REGISTRATION: This study is registered on ClinicalTrials.gov (NCT05583578). Registered October 13, 2022. https://www. CLINICALTRIALS: gov/study/NCT05583578.

Effects of motor imagery using virtual reality on pain sensitivity and affect in healthy individuals: A prospective randomized crossover study.

OBJECTIVE: Exercise induces a hypoalgesic response and improves affect. However, some individuals are unable to exercise for various reasons. Motor imagery, involving kinesthetic and visual imagery without physical movement, activates brain regions associated with these benefits and could be an alternative for those unable to exercise. Virtual reality also enhances motor imagery performance because of its illusion and embodiment. Therefore, we examined the effects of motor imagery combined with virtual reality on pain sensitivity and affect in healthy individuals. DESIGN: Randomized crossover study. SETTING: Laboratory. SUBJECTS: Thirty-six participants (women: 18) were included. METHODS: Each participant completed three 10-min experimental sessions, comprising actual exercise, motor imagery only, and motor imagery combined with virtual reality. Hypoalgesic responses and affective improvement were assessed using the pressure-pain threshold and the Positive and Negative Affect Schedule, respectively. RESULTS: All interventions significantly increased the pressure-pain threshold at the thigh (P<0.001). Motor imagery combined with virtual reality increased the pressure-pain threshold more than motor imagery alone, but the threshold was similar to that of actual exercise (both P≥0.05). All interventions significantly decreased the negative affect of the Positive and Negative Affect Schedule (all P<0.05). CONCLUSIONS: Motor imagery combined with virtual reality exerted hypoalgesic and affective-improvement effects similar to those of actual exercise.

Combining motor imagery practice with physical practice optimizes the improvement in peak force control during thoracic spinal manipulation.

Spinal manipulation learning requires intensive practice, which can cause injuries in students. Motor imagery (MI) paired with physical practice (PP) appears to be a suitable means to reduce the number of physical repetitions without decreasing skill outcomes. This study examines whether a session of MI paired with PP leads to a similar improvement in the ability to precisely produce peak forces during a thoracic manipulation as PP alone. Chiropractic students participated in a thoracic manipulation training program for five weeks. They were randomised in two groups: the MI+PP group performed sessions combining physical and mental repetitions with 1/3 fewer PP sessions, while the PP group performed only PP. Thoracic manipulation performance was assessed in pre and post-tests, consisting of thoracic manipulations at three different strength targets. Absolute error (AE), corresponding to the difference between the force required and the force applied by the student, was recorded for each trial. The main result revealed that AE was significantly lower in post-test than in pre-test for both groups. Despite fewer physical repetitions, the MI+PP participants showed as much improvement as the PP participants. This result supports the use of MI combined with PP to optimise the benefits of physical repetitions on thoracic manipulation learning.

La combinaison de la pratique de l'imagerie motrice avec la pratique physique optimise l'amélioration du contrôle de la force maximale pendant la manipulation vertébrale thoracique.L'apprentissage de la manipulation vertébrale nécessite une pratique intensive qui peut entraîner des blessures chez les étudiants. L'imagerie motrice (IM) associée à la pratique physique (PP) semble être un moyen approprié pour réduire le nombre de répétitions physiques sans diminuer les acquis de compétences. Cette étude examine de quelle manière une séance d'IM combinée à la pratique physique entraîne une amélioration similaire pour doser avec précision leur force lors d'une manipulation thoracique par rapport à la pratique physique seule. Des étudiants en chiropratique ont participé à un programme de formation à la manipulation thoracique pendant cinq semaines. Ils ont été répartis au hasard en deux groupes: le groupe IM + PP a effectué des séances combinant des répétitions physiques et mentales avec 1/3 de séances PP en moins, tandis que le groupe PP n'a effectué que des séances PP. Les résultats des manipulations thoraciques ont été évalués lors de prétests et de post-tests, consistant en des manipulations thoraciques à trois niveaux de force différents. L'erreur absolue (EA), correspondant à la différence entre la force requise et la force appliquée par l'étudiant, a été enregistrée pour chaque essai. Le résultat principal a révélé que l'EA était significativement plus faible dans le post-test que dans le pré-test pour les deux groupes. Malgré un nombre inférieur de répétitions physiques, les participants IM+PP ont montré autant d'amélioration que les participants PP. Ce résultat soutient l'utilisation de l'IM combinée à la PP pour optimiser les avantages des répétitions physiques sur l'apprentissage de la manipulation thoracique.

Riemannian transfer learning based on log-Euclidean metric for EEG classification.

Introduction: Brain computer interfaces (BCI), which establish a direct interaction between the brain and the external device bypassing peripheral nerves, is one of the hot research areas. How to effectively convert brain intentions into instructions for controlling external devices in real-time remains a key issue that needs to be addressed in brain computer interfaces. The Riemannian geometry-based methods have achieved competitive results in decoding EEG signals. However, current Riemannian classifiers tend to overlook changes in data distribution, resulting in degenerated classification performance in cross-session and/or cross subject scenarios. Methods: This paper proposes a brain signal decoding method based on Riemannian transfer learning, fully considering the drift of the data distribution. Two Riemannian transfer learning methods based log-Euclidean metric are developed, such that historical data (source domain) can be used to aid the training of the Riemannian decoder for the current task, or data from other subjects can be used to boost the training of the decoder for the target subject. Results: The proposed methods were verified on BCI competition III, IIIa, and IV 2a datasets. Compared with the baseline that without transfer learning, the proposed algorithm demonstrates superior classification performance. In contrast to the Riemann transfer learning method based on the affine invariant Riemannian metric, the proposed method obtained comparable classification performance, but is much more computationally efficient. Discussion: With the help of proposed transfer learning method, the Riemannian classifier obtained competitive performance to existing methods in the literature. More importantly, the transfer learning process is unsupervised and time-efficient, possessing potential for online learning scenarios.

An optimized EEGNet decoder for decoding motor image of four class fingers flexion.

As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.