Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation.

Background: The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods: We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results: Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion: The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.

A delayed matching task-based study on action sequence of motor imagery.

The way people imagine greatly affects performance of brain-computer interface (BCI) based on motion imagery (MI). Action sequence is a basic unit of imitation, learning, and memory for motor behavior. Whether it influences the MI-BCI is unknown, and how to manifest this influence is difficult since the MI is a spontaneous brain activity. To investigate the influence of the action sequence, this study proposes a novel paradigm named action sequences observing and delayed matching task to use images and videos to guide people to observe, match and reinforce the memory of sequence. Seven subjects' ERPs and MI performance are analyzed under four different levels of complexities or orders of the sequence. Results demonstrated that the action sequence in terms of complexity and sequence order significantly affects the MI. The complex action in positive order obtains stronger ERD/ERS and more pronounced MI feature distributions, and yields an MI classification accuracy that is 12.3% higher than complex action in negative order (p < 0.05). In addition, the ERP amplitudes derived from the supplementary motor area show a positive correlation to the MI. This study demonstrates a new perspective of improving imagery in the MI-BCI by considering the complexity and order of the action sequences, and provides a novel index for manifesting the MI performance by ERP.

Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification.

Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.

Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.

The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.

Harnessing Mirror Neurons: A New Frontier in Parkinson's Disease Rehabilitation-A Scoping Review of the Literature.

Background: Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. Rehabilitation utilizing mirror neurons leverages the brain's capacity for action observation (AO) and motor imagery (MI) to enhance motor function. This approach involves patients imitating movements observed in therapists or videos, aiming to improve gait, coordination, and overall quality of life. Mirror neuron activation facilitates motor learning and may decelerate disease progression, thus enhancing patient mobility and independence. Methods: This scoping review aimed to map current evidence on PD therapies employing mirror neuron-based rehabilitation. Databases searched included PubMed, PEDro, and Cochrane. The review included randomized controlled trials (RCTs) and systematic reviews that examined the effects of AO and MI in PD rehabilitation. Results: Five studies met the inclusion criteria, encompassing various rehabilitation techniques focusing on AO and MI. These studies consistently demonstrated positive outcomes, such as reduced disease severity and improved quality of life, gait, and balance in PD patients. The activation of mirror neurons through AO and MI was shown to facilitate motor learning and contribute to improved functional mobility. Conclusions: Although the included studies support the beneficial impact of AO and MI techniques in PD rehabilitation, numerous questions remain unresolved. Further research is necessary to evaluate the potential integration of these techniques into standard physiotherapy routines for PD patients. This review highlights the promise of AO and MI in enhancing motor rehabilitation for PD, suggesting the need for more comprehensive studies to validate and refine these therapeutic approaches.

Using mechanistic knowledge to appraise contemporary approaches to the rehabilitation of upper limb function following stroke.

It is a paradox of neurological rehabilitation that, in an era in which preclinical models have produced significant advances in our mechanistic understanding of neural plasticity, there is inadequate support for many therapies recommended for use in clinical practice. When the goal is to estimate the probability that a specific form of therapy will have a positive clinical effect, the integration of mechanistic knowledge (concerning 'the structure or way of working of the parts in a natural system') may improve the quality of inference. This is illustrated by analysis of three contemporary approaches to the rehabilitation of lateralized dysfunction affecting people living with stroke: constraint-induced movement therapy; mental practice; and mirror therapy. Damage to 'cross-road' regions of the structural (white matter) brain connectome generates deficits that span multiple domains (motor, language, attention and verbal/spatial memory). The structural integrity of these regions determines not only the initial functional status, but also the response to therapy. As structural disconnection constrains the recovery of functional capability, 'disconnectome' modelling provides a basis for personalized prognosis and precision rehabilitation. It is now feasible to refer a lesion delineated using a standard clinical scan to a (dis)connectivity atlas derived from the brains of other stroke survivors. As the individual disconnection pattern thus obtained suggests the functional domains most likely be compromised, a therapeutic regimen can be tailored accordingly. Stroke is a complex disorder that burdens individuals with distinct constellations of brain damage. Mechanistic knowledge is indispensable when seeking to ameliorate the behavioural impairments to which such damage gives rise.

EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification.

Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human-computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.

Effects of Motor Learning on Corticospinal Tract Excitability During Motor Imagery.

We aimed to examine the effects of motor performance improvements produced by practice on corticospinal tract excitability during motor imagery (MI) of identical movements. Participants performed a motor task with no guidelines displayed on the monitor (performance test); the participants only imagined performing the task without performing the movement (MI test), and the participants performed the power output and then adjusted it (exercise). The output force conditions were 20, 40, and 60% of the maximum voluntary contraction, and the objective was for 21 participants to learn each output force condition. The outcome of the performance test was calculated as the difference between the actual motor output and the target. During the MI test, we applied a single transcranial magnetic stimulation during imagery, assessed the corticospinal tract excitability of the right first dorsal interosseous by motor-evoked potential (MEP) amplitude, and recorded the vividness of the MI in each trial. We evaluated performance and MI before practice (Pre-test), after 150 practice sessions (Post-test 1), and after another 150 practice sessions (Post-test 2). The MEP amplitude was significantly reduced at Post-test 2 compared to Pre-test. The vividness of the MI improved with practice. Corticospinal tract excitability during MI decreased as motor performance improved. Thus, actual motor practice was also reflected in the MI of the exercise. Performance improvement was accompanied by a decrease in redundant activity, enhancing the efficiency and appropriateness of the exercise.

Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance.

BACKGROUND: Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component. NEW METHODS: In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning. COMPARISON WITH EXISTING METHODS: Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets. RESULTS: To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7 %. CONCLUSION: This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.

Characteristic brain functional activation and connectivity during actual and imaginary right-handed grasp.

We used 34-channel functional near infrared spectroscopy to investigate and compare changes in oxyhemoglobin concentration of brain networks in bilateral prefrontal cortex, sensorimotor cortex, and occipital lobe of 22 right-handed healthy adults during executive right-handed grasp (motor execution task) and imagined right-handed grasp (motor imagery task). Then calculated lateral index and functional contribution degree, and measured functional connectivity strength between the regions of interest. In the motor executive block task, there was a significant increase in oxyhemoglobin concentration in regions of interest except for right occipital lobe (P<0.05), while in the motor imagery task, all left regions of interest's oxyhemoglobin concentration increased significantly (P<0.05). Except the prefrontal cortex in motor executive task, the left side of the brain was dominant. Left sensorimotor cortex played a major role in these two tasks, followed by right sensorimotor cortex. Among all functional contribution degree, left sensorimotor cortex, right sensorimotor cortex and left occipital lobe ranked top three during these tasks. In continuous acquisition tasks, functional connectivity on during motor imagery task was stronger than that during motor executive task. Brain functions during two tasks of right-hand grasping movement were partially consistent. However, the excitability of brain during motor imagery was lower, and it was more dependent on the participation of left prefrontal cortex, and its synchronous activity of the whole brain was stronger. The trend of functional contribution degree was basically consistent with oxyhemoglobin concentration and lateral index, and can be used as a novel index to evaluate brain function. [ChiCTR2200063792 (2022-09-16)].

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 (CS) 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-2 A, 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 CS 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.

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.

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 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.

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.

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.

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.

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.

The Effects of Motor Imagery on Static and Dynamic Balance and on the Fear of Re-Injury in Professional Football Players with Grade II Ankle Sprains.

Lateral ankle sprains are one of the most frequent athletic injuries in football, causing deficits in balance. Motor Imagery (MI) has been successively included in sports rehabilitation as a complementary therapeutic intervention. The aim of the present study was to explore the effects of MI on static and dynamic balance and on the fear of re-injury in professional football players with Grade II ankle sprains. Fifty-eight participants were randomly allocated into two groups: First-MI group (n = 29) and second-Placebo group (n = 29), and they each received six intervention sessions. The first MI group received MI guidance in addition to the balance training program, while the second Placebo group received only relaxation guidance. One-way ANOVA showed statistically significant results for all variables, both before and 4 weeks after the interventions for both groups. The t-test showed statistically significant differences between the two groups for static balance for the right lower extremity (t = 3.25, S (two-tailed) = 0.002, p < 0.05) and also for heart rate (final value) in all time phases. Further research is needed in order to establish MI interventions in sports trauma recovery using stronger MI treatments in combination with psychophysiological factors associated with sports rehabilitation.