Auditory imagery ability influences accuracy when singing with altered auditory feedback.

In this preliminary study, we explored the relationship between auditory imagery ability and the maintenance of tonal and temporal accuracy when singing and audiating with altered auditory feedback (AAF). Actively performing participants sang and audiated (sang mentally but not aloud) a self-selected piece in AAF conditions, including upward pitch-shifts and delayed auditory feedback (DAF), and with speech distraction. Participants with higher self-reported scores on the Bucknell Auditory Imagery Scale (BAIS) produced a tonal reference that was less disrupted by pitch shifts and speech distraction than musicians with lower scores. However, there was no observed effect of BAIS score on temporal deviation when singing with DAF. Auditory imagery ability was not related to the experience of having studied music theory formally, but was significantly related to the experience of performing. The significant effect of auditory imagery ability on tonal reference deviation remained even after partialling out the effect of experience of performing. The results indicate that auditory imagery ability plays a key role in maintaining an internal tonal center during singing but has at most a weak effect on temporal consistency. In this article, we outline future directions in understanding the multifaceted role of auditory imagery ability in singers' accuracy and expression.

Pain neuroscience education and motor imagery-based exercise protocol for patients with fibromyalgia: A randomized controlled trial.

BACKGROUND: This study is a randomized controlled, biopsychosocial study investigating the effectiveness of pain neuroscience education (PNE) and motor imagery-based exercise protocol (MIEP) on fibromyalgia pain. METHODS: Our study has four groups (MIEP n = 12, PNE n = 12, MIEP + PNE n = 14, Control n = 12) and all participants (n = 50) consist of patients diagnosed with fibromyalgia with chronic back pain. The primary outcome measure was pain intensity, and secondary outcome measures were beliefs, kinesiophobia, anxiety-depression, cognitive-mood, self-esteem, and body awareness. RESULTS: A statistically significant decrease in pain intensity was observed in all experimental groups, without any group being superior (Visual Analog Scale [VAS]: MIEP + PNE p = .003, 95% confidence interval [CI], -4.7078 to -0.9922; MIEP p = .003, 95% CI, -5.4806 to -1.0194; PNE p = .002, 95% CI, -3.6139 to -1.5461). There was a significant improvement in organic beliefs in both groups where PNE was applied (MIEP + PNE: p = .017, 95% CI, -7.8211 to -0.3189; PNE: p = .003, 95% CI, -9.7999 to -0.0401). A significant superiority in organic pain beliefs was detected in the MIEP + PNE group compared to the control group (p = .008, 95% CI, 1.7241-9.4959). CONCLUSIONS: According to this study, in which MIEP and PNE were combined, there was a decrease in pain intensity when both applications were applied together and when they were applied one by one. MIEP has improved her motor imagery ability, improved pain and increased body awareness. PNE has improved people's organic pain beliefs; removed people from fears, catastrophizing, and negative thoughts about pain; improved easier management of psychological processes and cognitive-emotion regulation ability.

Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.

For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trial-feedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks.

Acquisition and processing of Motor Imagery and Motor Execution Dataset (MIMED) for six movement activities.

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

Childhood-related PTSD: the role of cognitions in EMDR and imagery rescripting.

Background: The relationship between trauma-related negative cognitions and post-traumatic stress disorder (PTSD) symptoms has been studied frequently. Several studies found a mediating effect of trauma-related negative cognitions on symptom reduction in studies on different psychotherapeutic treatments, however, this relationship has never been studied in imagery rescripting (ImRs) or eye movement desensitization and reprocessing (EMDR).Objective: To analyse the role of trauma-related negative cognitions in the treatment of PTSD due to childhood trauma with EMDR and ImRs.Method: N = 155 patients with PTSD due to childhood trauma aged between 18 and 65 (M = 38.54) participated in a randomized clinical trial and were treated with either EMDR or ImRs in Australia, Germany, and the Netherlands between October 2014 and June 2019. We analysed the relationship between PTSD symptoms (Clinician-administered PTSD Scale for DSM-5, CAPS-5 and Impact of Event Scale revised; IES-R, completed twice for index trauma and for all other traumas) and trauma-related negative cognitions (Post-Traumatic Cognitions Inventory, PTCI) using Granger Causality analyses with linear mixed models on person-centered variables. Assessments were conducted pre-treatment, post-treatment (12 sessions in 6 weeks), eight weeks post-treatment, and one year after the pre-treatment assessment.Results: Changes in negative cognitions (PTCI) preceded changes in PTSD symptoms (unidirectional) as measured by the CAPS and the IES-R for index trauma. For the IES-R related to all other traumas, a unidirectional relationship was found in which changes in PTSD symptoms preceded changes in negative cognitions. No moderating effect of treatment was found. On the level of PTCI subscales only changes in cognitions about oneself preceeded changes in PTSD symptoms.Conclusions: The results support the idea of a general role of trauma-related negative cognitions in the treatment of PTSD. The analyses should be replicated with a higher frequency of assessments.

We studied the role of trauma-related negative cognitions in the treatment of post-traumatic stress disorder (PTSD) with either EMDR or ImRs.Within-person changes in trauma-related negative cognitions preceded changes in PTSD symptoms, except for self-reported PTSD symptoms of all other trauma's than the index trauma, where the opposite relationship was found.We found no moderation by treatment condition, this supports the idea of a general role of trauma-related negative cognitions in the treatment of post-traumatic stress disorder.

The Efficacy of Motor Imagery Additional to Task-Oriented Training for Children with Developmental Coordination Disorder: A Study Protocol for a Randomized Open-Label Controlled Trial.

BACKGROUND: Developmental Coordination Disorder (DCD) affects school-age children and interferes with the practice of their daily activities. Task-oriented work and motor imagery have shown great efficacy in addressing this problem. OBJECTIVE: This study presents a protocol that seeks to understand the effectiveness of a combined program of both modalities on the motor competence of children susceptible to the presence of DCD. DESING: Randomised controlled trial. METHODS: The participants are children susceptible to the presence of DCD (from 6 to 12 years old) distributed into an intervention group that received a total of 20 protocol sessions and a control group that continued with their usual school routine.

Motor imagery with cues in virtual reality, audio and screen.

OBJECTIVE: Training plays a significant role in motor imagery (MI), particularly in applications such as Motor Imagery-based Brain-Computer Interface (MIBCI) systems and rehabilitation systems. Previous studies have investigated the intricate relationship between cues and MI signals. However, the medium of presentation still remains an emerging area to be explored, as possible factors to enhance Motor Imagery signals.. Approach: We hypothesise that the medium used for cue presentation can significantly influence both performance and training outcomes in MI tasks. To test this hypothesis, we designed and executed an experiment implementing no- feedback MI. Our investigation focused on three distinct cue presentation mediums -audio, screen, and virtual reality(VR) headsets-all of which have potential implications for BCI use in the Activities of Daily Lives. Main Results: The results of our study uncovered notable variations in MI signals depending on the medium of cue presentation, where the analysis is based on 3 EEG channels. To substantiate our findings, we employed a comprehensive approach, utilizing various evaluation metrics including Event- Related Synchronisation(ERS)/Desynchronisation(ERD), Feature Extraction (using Recursive Feature Elimination (RFE)), Machine Learning methodologies (using Ensemble Learning), and participant Questionnaires. All the approaches signify that Motor Imagery signals are enhanced when presented in VR, followed by audio, and lastly screen. Applying a Machine Learning approach across all subjects, the mean cross-validation accuracy (Mean ± Std. Error) was 69.24 ± 3.12, 68.69 ± 3.3 and 66.1±2.59 when for the VR, audio-based, and screen-based instructions respectively. Significance: This multi-faceted exploration provides evidence to inform MI- based BCI design and advocates the incorporation of different mediums into the design of MIBCI systems, experimental setups, and user studies. The influence of the medium used for cue presentation may be applied to develop more effective and inclusive MI applications in the realm of human-computer interaction and rehabilitation.

How pain and body representations transform each other: A narrative review.

Pain, as a multidimensional and subjective experience, intertwines with various aspects of body representation, involving sensory, affective and motivational components. This review explores the bidirectional relationship between pain and body representations, emphasizing the impact of the sense of ownership on pain perception, the transformative impact of pain on motor imagery, the effects associated with vicarious pain perception on body representations and the role of pain in the maintenance of body representations in specific clinical conditions. Literature indicates complex interactions between pain and body representations, with the sense of ownership inducing analgesic effects in some cases and hyperalgesia in others, contingent upon factors such as the appearance of the affected limb. Pain sensations inform the body on which actions might be executed without harm, and which are potentially dangerous. This information impacts on motor imagery too, showing reduced motor imagery and increased reaction times in tasks where motor imagery involves the painful body parts. Finally, contrary to the conventional view, according to which pain impairs body representation, evidence suggests that pain can serve as an informative somatosensory index, preserving or even enhancing the representation of the absent or affected body parts. This bidirectional relationship highlights the dynamic and multifaceted nature of the interplay between pain and body representations, offering insights into the adaptive nature of the central nervous system in response to perceived bodily states.

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

BACKGROUND: Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. OBJECTIVE: This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. METHODS: Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones-ResNet50, ResNet101, and MobileNet-were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. RESULTS: The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921. CONCLUSIONS: The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

Acute Stress Does Not Affect Motor Imagery Ability in Young, Healthy Participants: A Randomized Trial.

Motor imagery (MI) is the mental representation of a movement without its execution. It activates internal representations of the movement without external stimulus through different memory-related processes. Although acute stress is frequent in the population and affects supraspinal structures essential for memory functionality, it is still unknown how that stress affects MI capacity and temporal congruence (TC) between execution and movement imagination. This study aimed to discover how acute stress may influence MI capacity and TC in the subscales of internal and external visual imagery and kinesthetic imagery. A double-blind, randomized trial was conducted. Sixty-two young, healthy subjects (mean age = 20.65 [2.54]; 39 females and 23 males) unfamiliar with the assessment and uses of MI were recruited. Participants were assigned by stratified randomization to the stress group or the control group. Stress was induced by the Maastricht Acute Stress Test (MAST), while the control group performed the MAST control protocol. MI capacity and TC were assessed before (t1) and after (t2) MAST stress or control using the Movement Imagery Questionnaire-3 (MIQ-3). Electrodermal activity and heart rate variability were further recorded as control variables to assess stress induction. Thirty subjects in the stress group and 26 subjects in the control group were analyzed. No significant group differences were observed when comparing MI capacity or TC in any subscales. These findings suggest that acute stress does not significantly affect MI capacity or TC in young, healthy, non-experienced MI subjects. MI could thus be a relevant helpful technique in stressful situations.

Dynamic weighted residual ensemble learning for hyperspectral image classification driven by features and samples.

Dynamic ensemble selection has emerged as a promising approach for hyperspectral image classification. However, selecting relevant features and informative samples remains a pressing challenge. To address this issue, we introduce two novel dynamic residual ensemble learning methods. The first proposed method is called multi-features driven dynamic weighted residuals ensemble learning (MF-DWRL). This method leverages various combinations of features to construct classifier pools that incorporate feature differences. The K-Nearest Neighbors algorithm is employed to establish the region of competence (RoC) in the dynamic ensemble selection process. By assessing the performance of the RoC, the feature sets that yield the highest classification accuracy are identified as the optimal feature combinations. Additionally, the classification accuracy is utilized as prior information to guide the residual adjustments of each classifier. The second method, known as features and samples double-driven dynamic weighted residual ensemble learning (FS-DWRL), further enhances the performance of the ensemble. This approach not only considers the selection of feature combinations but also takes into account the informative samples. By jointly optimizing the feature and sample selection processes, FS-DWRL achieves superior classification accuracy compared to existing state-of-the-art methods. To evaluate the effectiveness of the proposed methods, three hyperspectral datasets from China-WHU-Hi-HanChuan, WHU-Hi-LongKou, and WHU-Hi-HongHu-are used for classification experiments. For these datasets, the proposed methods achieve the highest classification accuracies of 90.57 %, 98.77 %, and 91.08 %, respectively. The MF-DWRL and FS-DWRL methods exhibit significant improvements in classification accuracy.

Fine classification of rice fields in high-resolution remote sensing images.

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%.

Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.

In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs. This leads to the data scarcity problem, which causes overfitting issues to training samples, resulting in reducing generalization performance. To solve the EEG data scarcity problem and improve the performance of the EEG classifiers, we propose a novel EEG data augmentation (DA) framework using conditional generative adversarial networks (cGANs). An experimental study is implemented with two public EEG datasets, including motor imagery (MI) tasks (BCI competition IV IIa and III IVa), to validate the effectiveness of the proposed EEG DA method for the EEG classifiers. To evaluate the proposed cGAN-based DA method, we tested eight EEG classifiers for the experiment, including traditional MLs and state-of-the-art DLs with three existing EEG DA methods. Experimental results showed that most DA methods with proper DA proportion in the training dataset had higher classification performances than without DA. Moreover, applying the proposed DA method showed superior classification performance improvement than the other DA methods. This shows that the proposed method is a promising EEG DA method for enhancing the performances of the EEG classifiers in MI-based BCIs.

Unlike overt movement, motor imagery cannot update internal models.

In overt movement, internal models make predictions about the sensory consequences of a desired movement, generating the appropriate motor commands to achieve that movement. Using available sensory feedback, internal models are updated to allow for movement adaptation and in-turn better performance. Whether internal models are updated during motor imagery, the mental rehearsal of movement, is not well established. To investigate internal modelling during motor imagery, 66 participants were exposed to a leftwards prism shift while performing actual pointing movements (physical practice; PP), imagined pointing movements (motor imagery; MI), or no pointing movements (control). If motor imagery updates internal models, we hypothesized that aftereffects (pointing in the direction opposite the prism shift) would be observed in MI, like that of PP, and unlike that of control. After prism exposure, the magnitude of aftereffects was significant in PP (4.73° ± 1.56°), but not in MI (0.34° ± 0.96°) and control (0.34° ± 1.04°). Accordingly, PP differed significantly from MI and control. Our results show that motor imagery does not update internal models, suggesting that it is not a direct simulation of overt movement. Furthering our understanding of the mechanisms that underlie learning through motor imagery will lead to more effective applications of motor imagery.

Aphantasia and autism: An investigation of mental imagery vividness.

OBJECTIVE: The present study investigated whether autistic adults report different levels of mental imagery vividness than non-autistic adults, and, moreover, if autism is associated with aphantasia which is defined as a condition of reduced or absent voluntary imagery. DESIGN AND METHODS: Clinically diagnosed and self-identifying autistic participants were compared with non-autistic participants in their mental imagery vividness (vision, sound, smell, taste, touch, bodily sensation and emotional feeling) and autistic traits using an online survey (N = 121). RESULTS: The autistic group scored significantly lower than the non-autistic group on imagery vividness (d = -0.44), in addition to having a higher proportion of participants scoring at cut-off for aphantasia. Moreover, a similar difference was observed for the emotional feel (η2 = 0.11). CONCLUSION: The vividness of visual and emotional mental imagery was on average lower for autistic individuals, with a higher proportion presenting at cut-off to be considered an aphantasic.

Assessing Human Influence and Vegetative Dune Dynamics on Barrier Islands via Satellite Raster Classification.

Barrier islands support ecological diversity and offshore ecosystems and provide critical protection to coastal communities. Climate change has intensified the frequency and severity of hurricanes affecting these islands, leading to ongoing erosion. The primary goal of this study was to explore the relationship between human intervention such as development and construction and the vegetative dune systems on Gulf Coast barrier islands in Alabama and Mississippi, USA. This research employed two decades of satellite images of three neighboring barrier islands and employed GIS raster classification to track changes in the vegetative dune system in terms of: (1) dune coverage (surface area of the vegetation), (2) vegetative maturity (vegetation type), and (3) stability (fluctuations in the vegetative coverage over time). Time series and trend analyses were used to compare the results for three neighboring islands. The findings show that Dauphin Island, which features both commercial facilities and vacation homes, exhibited a decrease in total area over time, and had the lowest percentage of vegetative dune coverage and highest level of vegetative fluctuation. In contrast, Petit Bois and Horn Islands, which remain untouched by human activity, displayed significantly higher levels of vegetative maturity and coverage and comparatively less fluctuation. This research provides a foundation for those advocating for dune restoration strategies, development limitations, and conservation regulations as nature-based infrastructure solutions to combat erosion on barrier islands and serves as a point of entry for future inquiries in the field of environmental management.

Vibration-Induced Illusory Movement Task Can Induce Functional Recovery in Patients With Subacute Stroke.

In recent years, mental practice (MP), which involves repetitive motor imagery (MI), has been applied in rehabilitation to actively enhance exercise performance. MP is a method that involves repetitive MI, consciously evoking the intentions and content of the exercise without actual exercise. Combining actual exercise with MP promotes the development of exercise skills. However, it is possible that the MI recall ability differs greatly between individuals, affecting the therapeutic effect. In contrast, the vibration-induced illusory movement (VIM) task acts as a method to induce a motor illusion by somatosensory stimuli without actual motor. VIM, actual movement, and MI are thought to share a common neural basis in the brain. Therefore, it was hypothesized that the VIM task would complement the differences in MI recall in individual patients with hemiplegic stroke and may be a new treatment to enhance MI recall. Accordingly, in this study, we investigated the therapeutic effects of the VIM task in patients with hemiplegic stroke. In Study I, the therapeutic effect of the VIM task in 14 patients with post-stroke hemiplegia was evaluated by motor function assessment. In Study II, treatment effects were investigated by examining the ability of the same group of patients to recall MI and by neurophysiological examination of the electroencephalogram (EEG) during MI recall in four patients who consented to the study. Motor function and MI were assessed four times: before the intervention, after occupational therapy, after the VIM task (which used the motor illusion induced by tendon vibration), and one month after acceptance of therapy. Compared with occupational therapy, the VIM task showed a statistically significant improvement in upper limb function and MI ability. In addition, we found an increase in event-related desynchronization intensity during MI in the affected hemisphere only after the VIM task. It is possible that the VIM task facilitates motor function and MI. VIM task implementation of MI recall variability between individuals, which is a problem in mental practice, possible to increase the effectiveness of the brain-machine interface.

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.

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.

Spinal maps of motoneuron activity during human locomotion: neuromechanical considerations.

The spatial segmental location of motoneurons in the human spinal cord is influenced by both evolutionary and functional principles tending to optimize motor control, reflex integration, and adaptation to the demands of movement. Bearing in mind the biomechanics of limb muscles, it is logical to examine how motoneuron activity clusters functionally during typical daily activities like walking. This article provides a summary of advancements in the study of spinal maps of motoneuron activation during human locomotion by reviewing data gathered over ∼20 years. The effects of child development, aging, and neurological disorders show the salient characteristics of spinal segmental activity during different human locomotor tasks and conditions. By exploiting the neuromechanics of the spinal motor circuits, that is, the link between motoneuron activity and gait mechanics, neuroprosthetics and other focused treatments may better help individuals with locomotor impairments.