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 neighbours (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.

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.

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.

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.

Tactile versus motor imagery: differences in corticospinal excitability assessed with single-pulse TMS.

Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain-computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry.

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.

[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.

White matter organisation of sensorimotor tracts is associated with motor imagery in childhood.

Despite the important role of motor imagery (MI) in motor development, our understanding of the contribution of white matter fibre properties to MI performance in childhood remains limited. To provide novel insight into the white matter correlates of MI performance, this study examined the association between white matter fibre properties and motor imagery performance in a sample of typically developing children. High angular diffusion weighted imaging data were collected from 22 typically developing children aged 6-14 years (12 female, MAge= 10.56). Implicit motor imagery performance was assessed using a mental hand rotation paradigm. The cerebellar peduncles and the superior longitudinal fasciculus were reconstructed using TractSeg, a semi-automated method. For each tract, white matter microstructure (fibre density, FD) and morphology (fibre bundle cross-section, FC) were estimated using Fixel-Based Analysis. Permutation-based inference testing and partial correlation analyses demonstrated that higher FC in the middle cerebellar peduncles was associated with better MI performance. Tract-based region of interest analyses showed that higher FC in the middle and superior cerebellar peduncles were associated with better MI performance. Results suggest that white matter connectivity along the cerebellar peduncles may facilitate MI performance in childhood. These findings advance our understanding of the neurobiological systems that underlie MI performance in childhood and provide early evidence for the relevance of white matter sensorimotor pathways to internal action representations.

Motor imagery ability of children with duchenne muscular dystrophy: Reliability and validity of kinesthetic and Visual Imagery Questionnaire-10, and its association with cognitive status.

PURPOSE: To investigate validity and reliability of the Kinesthetic and Visual Imagery Questionnaire-10 (KVIQ-10) in children with Duchenne Muscular Dystrophy (DMD), to compare the motor imagery (MI) ability with age-matched controls, and to examine the relationship between MI ability and cognitive status. METHODS: The research involved 38 children who were diagnosed with DMD, as well as 20 healthy controls aged between 7 and 18 years. The KVIQ-10 was assessed for its test-retest reliability, internal consistency, construct and concurrent validity. The Motor Imagery Questionnaire for Children (MIQ-C) was selected as the gold standard test for concurrent validity. Cognitive function was assessed using the Modified Mini Mental Test (MMMT) and Montreal Cognitive Assessment (MoCA). RESULTS: KVIQ-10 showed excellent test-retest reliability (ICC>0.90) and high internal consistency (Cronbach's alpha>0.70). A moderate-to-strong association was found between KVIQ-10 and MIQ-C subscales (p < 0.001). KVIQ-10 and MIQ-C subscores were statistically lower in the DMD group (p ≤ 0.05). A correlation was found between MoCA and KVIQ-10 in children with DMD (p ≤ 0.05). CONCLUSIONS: The KVIQ-10 is a reliable and valid measure to assess the MI ability of children with DMD whose imagery ability was determined to be impaired. CLINICAL TRIAL REGISTRATION NUMBER AND URL: NCT05559710 (https://classic. CLINICALTRIALS: gov/ct2/show/NCT05559710?term=NCT05559710&draw=2&rank=1).

Changes in corticospinal excitability during motor imagery by physical practice of a force production task: Effect of the rate of force development during practice.

Transcranial magnetic stimulation studies have indicated that the physical practice of a force production task increases corticospinal excitability during motor imagery (MI) of that task. However, it is unclear whether this practice-induced facilitation of corticospinal excitability during MI depends on a repeatedly practiced rate of force development (RFD). We aimed to investigate whether corticospinal excitability during MI of an isometric force production task is facilitated only when imagining the motor task with the same RFD as the physically practiced RFD. Furthermore, we aimed to examine whether corticospinal excitability during MI only occurs immediately after physical practice or is maintained. Twenty-eight right-handed young adults practiced isometric ramp force production using right index finger abduction. Half of the participants (high group) practiced the force production with high RFD, and the other half (low group) practiced the force production with low RFD. Questionnaire scores indicating MI ability were similar in the two groups. We examined the force error relative to the target force during the force production task without visual feedback, and motor evoked potential (MEP) amplitudes of the first dorsal interosseous (FDI) and abductor pollicis brevis (APB) muscles during the MI of the force production task under practiced and unpracticed RFD conditions before, immediately after, and 20 min after physical practice. Our results demonstrated that the force error in both RFD conditions significantly decreased immediately after physical practice, irrespective of the RFD condition practiced. In the high group, the MEP amplitude of the FDI muscle during MI in the high RFD condition significantly increased immediately after practice compared to that before, whereas the MEP amplitude 20 min after practice was not significantly different from that before practice. Conversely, the MEP amplitude during MI in the high RFD condition did not change significantly in the low group, and neither group had significant changes in MEP amplitude during MI in the low RFD condition. The facilitatory effect of corticospinal excitability during MI with high RFD observed only immediately after physical practice in the high RFD condition may reflect short-term functional changes in the primary motor cortex induced by physical practice.

An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features.

Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extracted from EEG signals. Currently, feature extraction heavily relies on prior knowledge to engineer features (for example from specific frequency bands); therefore, better extraction of EEG features is an important research direction. In this work, we propose an end-to-end deep neural network that automatically finds and combines features for motor imagery (MI) based EEG BCI with 4 or more imagery classes (multi-task). First, spectral domain features of EEG signals are learned by compact convolutional neural network (CCNN) layers. Then, gated recurrent unit (GRU) neural network layers automatically learn temporal patterns. Lastly, an attention mechanism dynamically combines (across EEG channels) the extracted spectral-temporal features, reducing redundancy. We test our method using BCI Competition IV-2a and a data set we collected. The average classification accuracy on 4-class BCI Competition IV-2a was 85.1 % ± 6.19 %, comparable to recent work in the field and showing low variability among participants; average classification accuracy on our 6-class data was 64.4 % ± 8.35 %. Our dynamic fusion of spectral-temporal features is end-to-end and has relatively few network parameters, and the experimental results show its effectiveness and potential.

Combined non-invasive neuromodulation using transcranial direct current stimulation, motor imagery and action observation for motor, cognitive and functional recovery in cortico-basal degeneration: a single case study.

This case report presents a comprehensive assessment and therapeutic intervention using non-invasive motor cortex neuromodulation for a 70-year-old female patient diagnosed with corticobasal degeneration (CBD). The study followed the CARE guidelines. The patient meets the criteria for probable CBD, with neuroimaging evidence of exclusively cortical impairment. The patient underwent a non-invasive neuromodulation protocol involving transcranial direct current stimulation (tDCS) and action observation plus motor imagery (AO+MI). The neuromodulation protocol comprised 20 sessions involving tDCS over the primary motor cortex and combined AO+MI. Anodal tDCS was delivered a 2 mA excitatory current for 20 minutes. AO+MI focused on lower limb movements, progressing over four weeks with video observation and gradual execution, both weekly and monthly. The neuromodulation techniques were delivered online (i.e. applied simultaneously in each session). Outcome measures were obtained at baseline, post-intervention and follow-up (1 month later), and included motor (lower limb), cognitive/neuropsychological and functional assessments. Walking speed improvements were not observed, but balance (Berg Balance Scale) and functional strength (Five Times Sit-to-Stand Test) improved post-treatment. Long-term enhancements in attentional set-shifting, inhibitory control, verbal attentional span, and working memory were found. There was neurophysiological evidence of diminished intracortical inhibition. Functional changes included worsening in Cortico Basal Ganglia Functional Scale score. Emotional well-being and general health (SF-36) increased immediately after treatment but were not sustained, while Falls Efficacy Scale International showed only long-term improvement. The findings suggest potential benefits of the presented neuromodulation protocol for CBD patients, highlighting multifaceted outcomes in motor, cognitive, and functional domains.

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.

Motor imagery of finger movements: Effects on cortical and muscle activities.

PURPOSE: The purpose of the current study was to explore the immediate effect of motor imagery (MI) involving finger movement of a given limb on cortical response and muscle activity in healthy subjects. METHODS: Twenty healthy right-handed adults (7 females and 13 males) with a mean + SD age of 22.05 + 6.08 years participated in the study. The beta-band event-related desynchronization (ERD) at the sensorimotor cortex and muscle activity during finger movement tasks using either the index, middle, or thumb digits on the non-dominant left hand were compared before and after an MI training session. Subjects underwent a pre-MI, MI training, and finally a post-MI session where they either performed or imagined performing a button-pushing action 50 times per session with each of the three digits. RESULTS: The ERD power in the beta frequency band was lower in pre-MI compared to post-MI and was significantly different between the pre- and post-MI sessions for both the index and middle fingers, but not the thumb. A significant decrease was seen in the mean muscle activity during post-MI compared to pre-MI for all the digits except the thumb. CONCLUSIONS: The results from the current study suggest that complex MI can result in motor learning and improvement in motor performance, thereby requiring less effort during motion.

Inequivalent and uncorrelated response priming in motor imagery and execution.

Introduction: Theoretical considerations on motor imagery and motor execution have long been dominated by the functional equivalence view. Previous empirical works comparing these two modes of actions, however, have largely relied on subjective judgments on the imagery process, which may be exposed to various biases. The current study aims to re-examine the commonality and distinguishable aspects of motor imagery and execution via a response repetition paradigm. This framework aims to offer an alternative approach devoid of self-reporting, opening the opportunity for less subjective evaluation of the disparities and correlations between motor imagery and motor execution. Methods: Participants performed manual speeded-choice on prime-probe pairs in each trial under three conditions distinguished by the modes of response on the prime: mere observation (Perceptual), imagining response (Imagery), and actual responses (Execution). Responses to the following probe were all actual execution of button press. While Experiment 1 compared the basic repetition effects in the three prime conditions, Experiment 2 extended the prime duration to enhance the quality of MI and monitored electromyography (EMG) for excluding prime imagery with muscle activities to enhance specificity of the underlying mechanism. Results: In Experiment 1, there was no significant repetition effect after mere observation. However, significant repetition effects were observed in both imagery and execution conditions, respectively, which were also significantly correlated. In Experiment 2, trials with excessive EMG activities were excluded before further statistical analysis. A consistent repetition effect pattern in both Imagery and Execution but not the Perception condition. Now the correlation between Imagery and Execution conditions were not significant. Conclusion: Findings from the current study provide a novel application of a classical paradigm, aiming to minimize the subjectivity inherent in imagery assessments while examining the relationship between motor imagery and motor execution. By highlighting differences and the absence of correlation in repetition effects, the study challenges the functional equivalence hypothesis of imagery and execution. Motor representations of imagery and execution, when measured without subjective judgments, appear to be more distinguishable than traditionally thought. Future studies may examine the neural underpinnings of the response repetition paradigm to further elucidating the common and separable aspects of these two modes of action.

Lumbar extension peak-torque, muscle endurance and motor imagery in junior-elite basketballers with and without a history of low back pain: a pilot study.

Objectives: Low back pain (LBP) is common in elite athletes. Several peripheral and central factors have been identified to be altered in non-athletic LBP populations, however whether these alterations also exist in elite athletes with LBP is unknown. The aim of this study was to determine whether elite basketballers with a history of persistent LBP perform worse than those without LBP at a lumbar muscle endurance task, a lumbar extension peak-torque task, and a lumbar motor imagery task. Method: An observational pilot study. Twenty junior elite-level male basketballers with (n = 11) and without (n = 9) a history of persistent LBP were recruited. Athletes completed a lumbar extensor muscle endurance (Biering-Sorensen) task, two lumbar extensor peak-torque (modified Biering-Sorensen) tasks and two motor imagery (left/right lumbar and hand judgement) tasks across two sessions (48 hours apart). Performance in these tasks were compared between the groups with and without a history of LBP. Results: Young athletes with a history of LBP had reduced lumbar extensor muscle endurance (p < 0.001), reduced lumbar extension peak-torque (p < 0.001), and were less accurate at the left/right lumbar judgement task (p = 0.02) but no less accurate at a left/right hand judgement task (p = 0.59), than athletes without a history of LBP. Response times for both left/right judgement tasks did not differ between groups (lumbar p = 0.24; hand p = 0.58). Conclusions: Junior elite male basketballers with a history of LBP demonstrate reduced lumbar extensor muscle endurance and lumbar extension peak-torque and are less accurate at a left/right lumbar rotation judgement task, than those without LBP.

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.

An EEG channel selection method for motor imagery based on Fisher score and local optimization.

Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.

The concept of the brain-computer interface (BCI) has become one of the popular research topics of recent times because it allows people to express their thoughts and control different applications and devices without actual movement. The communication between the brain and the computer or a machine is generally provided through Electroencephalogram (EEG) signals because they are cost-effective and easy to implement in normal life, not just in healthcare facilities. On the other hand, they are hard to process efficiently due to their nonlinearity and noisy nature. Thus, the field of BCI and EEG needs constant work and improvement. This paper focuses on generalizing the most efficient EEG channels and the most significant features of motor imagery (MI) signals by analyzing the recordings of one participant obtained over 20 different days. Because the classification performance usually decreases with an increasing number of class labels, we have realized the study by analyzing the signals through a new paradigm consisting of multi-class directional labels: right, left, forward, and backward. Afterward, the results are tested on EEG data obtained from 5 participants to see if the results are consistent with each other. The average accuracy of binary and multi-class classification using the Ensemble Subspace Discriminant classifier was found as 87.39 and 61.44%, respectively, with the most efficient 3-channel combination for daily BCI evaluation of one participant. On the other hand, the average accuracy of binary and multi-class classification was found as 71.84 and 50.42%, respectively, for 5 participants, with the most efficient channel combination of 4, where the first three are the same as the daily performance of one participant. During signal processing, the outliers of the signals were discarded by considering the channels separately. An algorithm was developed to dismiss the inconsistent samples within the classes. A novel adaptive filtering approach, correlation-based adaptive variational mode decomposition (CBAVMD), was proposed. The feature selection was realized based on the standard deviation values of the features between the classes. The paradigm based on the direction movements was found to be most effective, especially for binary classification of right and left directions. The generalization of effective channels and features was found to be generally successful.