Comparisons of Visual Versus Kinesthetic Mental Imagery in Soccer Players: An EEG Study

Mental imagery has been shown to effectively increase sport performances. However, limited studies have examined the underlying neurological influence of mental practice, especially with team sports. The current study investigated whether electroencephalogram (EEG) patterns differ based on an athlete\u27s ability to use mental imagery and if differences exist between the two types of mental imagery, visual versus kinesthetic, when mentally rehearsing specific soccer scenarios. Ten college elite soccer athletes and seven novices participated in this study. EEG data and self-rating were collected during mental rehearsal of three simple movements and three soccer scenarios applying either visual or kinesthetic mental imagery. Although visual mental imagery was predominantly preferred for both groups, the alpha amplitude of EEG significantly decreased during kinesthetic mental imagery of the soccer scenarios for the elite group, suggesting a deeper brain involvement than the novice group

Is heart rate variability affected by distinct motor imagery strategies

Although some studies have reported significant changes in autonomic responses according to the perspective-taking during motor imagery [first person perspective (1P) and third person perspective (3P)], investigations on how the strategies adopted to mentally simulate a given movement affect the heart rate variability (HRV) seem so far unexplored. Twenty healthy subjects mentally simulated the movement of middle-finger extension in 1P and 3P, while electrocardiogram was recorded. After each task, the level of easiness was self-reported. Motor imagery ability was also assessed through the revised version of Movement Imagery Questionnaire (MIQ-R) and a mental chronometry index. The traditional measures of HRV in the time- and frequency-domain were compared between 1P and 3P tasks by using Student's t-test for dependent samples. The MIQ-R results showed that subjects had the same facility to imagine movements in 1P or 3P. The mental chronometry index revealed a similar temporal course only between 1P and execution, while the 3P strategy had a shorter duration. Additionally, the subjective report was similar between the experimental tasks. Regarding the HRV measures, the low frequency component, in log-transformed unit, was significantly higher (p=0.017) in 1P than 3P, suggesting a higher activity of the sympathetic system during 1P. This log-transformed HRV parameter seems to be more sensitive than normalized values for the assessment of the motor imagery ability, together with questionnaires, scales and mental chronometry

The challenge of measuring physiological parameters during motor imagery engagement in patients after a stroke

Introduction: It is suggested that eye movement recordings could be used as an objective evaluation method of motor imagery (MI) engagement. Our investigation aimed to evaluate MI engagement in patients after stroke (PaS) compared with physical execution (PE) of a clinically relevant unilateral upper limb movement task of the patients' affected body side. Methods: In total, 21 PaS fulfilled the MI ability evaluation [Kinaesthetic and Visual Imagery Questionnaire (KVIQ-10), body rotation task (BRT), and mental chronometry task (MC)]. During the experiment, PaS moved a cup to distinct fields while wearing smart eyeglasses (SE) with electrooculography electrodes integrated into the nose pads and electrodes for conventional electrooculography (EOG). To verify MI engagement, heart rate (HR) and oxygen saturation (SpO2) were recorded, simultaneously with electroencephalography (EEG). Eye movements were recorded during MI, PE, and rest in two measurement sessions to compare the SE performance between conditions and SE's psychometric properties. Results: MI and PE correlation of SE signals varied between r = 0.12 and r = 0.76. Validity (cross-correlation with EOG signals) was calculated for MI (r = 0.53) and PE (r = 0.57). The SE showed moderate test–retest reliability (intraclass correlation coefficient) with r = 0.51 (95% CI 0.26–0.80) for MI and with r = 0.53 (95% CI 0.29 – 0.76) for PE. Event-related desynchronization and event-related synchronization changes of EEG showed a large variability. HR and SpO2 recordings showed similar values during MI and PE. The linear mixed model to examine HR and SpO2 between conditions (MI, PE, rest) revealed a significant difference in HR between rest and MI, and between rest and PE but not for SpO2. A Pearson correlation between MI ability assessments (KVIQ, BRT, MC) and physiological parameters showed no association between MI ability and HR and SpO2. Conclusion: The objective assessment of MI engagement in PaS remains challenging in clinical settings. However, HR was confirmed as a reliable parameter to assess MI engagement in PaS. Eye movements measured with the SE during MI did not resemble those during PE, which is presumably due to the demanding task. A re-evaluation with task adaptation is suggested

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

The role of somatosensory feedback for brain-machine interfaces applications

Brain-machine interfaces (BMI) based on motor imagery (MI) have emerged as a promising approach to enhance motor skills and restore motor functions. However, the efficacy and efficiency of BMI systems remain limited. The current lack of usability can be explained by the fact that significant efforts have been dedicated to improve decoding efficiency and accuracy, but BMI studies have generally ignored the user-training component of BMI operation. It has been suggested that somatosensory feedback would be more suitable than standard visual feedback to train subjects to control a BMI. In this thesis, a novel feedback modality has been explored to improve BMI usability, namely sensory-threshold neuromuscular electrical stimulation (St-NMES). St-NMES delivers transcutaneous electrical stimulation that depolarizes sensory and motor axons without eliciting any muscular contraction. In order to assess the effect of this new feedback modality on BMI skill learning this thesis is composed of four experiments. In a first experiment, the effect of St-NMES on MI performance was investigated. Twelve healthy subjects participated in a cross-over design experiment comparing St-NMES with visual feedback. Offline analyses showed that St-NMES not only enhanced MI brain patterns, but also improved classification accuracy. Importantly, St-NMES alone did not induce detectable artefacts. In a second experiment, physiological impact of online BMI training on corticospinal tract (CST) plasticity was studied according to the feedback modality âeither St-NMES or visual feedback. Ten healthy participants were enrolled in a cross-over design experiment testing both BMI systems. Results showed that BMI based on St-NMES significantly enhanced CST excitability compared to BMI based on visual feedback. Moreover, BMI system based on St-NMES was significantly more robust and accurate over days. A third experiment further explored the parallelism between BMI learning based on St-NMES feedback and natural motor learning, putting particular attention on the underlying physiology of the process. Apart from analyzing the evolution of BMI performance, we also examined changes in CST excitability and modulation of intracortical inhibition in the early learning phase (after one BMI session) as well as later learning stage (after 2 weeks training). Ten healthy participants were trained to control a BMI based on St-NMES feedback. Results showed that subjects improved their BMI control with practice, what might be explained by the adaptation of the central nervous system over time. Finally, the last experiment explored the feasibility of BMI-St-NMES for upper limb rehabilitation after stroke. A chronic stroke patient with a severe motor disability was trained with BMI-St-NMES over 3 weeks. After training, upper-limb motor function improved, reaching clinical relevance. Based on our previous observations, we believe that BMI-St-NMES training enhanced CST projections leading to motor recovery. As a conclusion, this thesis showcases that a contingent activation of central nervous system with somatosensory stimulation through BMI-St-NMES is a promising solution to enhance BMI control and to induce cortico and corticospinal changes. This new BMI modality could become a future opportunity for several fields of research including mental training assistive scenarios as well as motor rehabilitation of patients with lesions within central nervous system

Cortical Activation Patterns in Art Making vs. Fine Motor Movement as Measured by EEG

This quantitative study explores the differences in cortical activation patterns when subjects create art versus when they engage in a rote motor task. It is hypothesized that a statistically significant difference occurs in cortical activity patterns during art making compared with non- creative rote motor behavior and that such differences can be detected and quantified with the electroencephalogram (EEG.) Ten consenting study subjects (one with formal art training, three with some art experience, and six with no art experience) underwent EEG recording at baseline (multiple measures) and with art making, and also with rote motor tasking. Baseline control recordings showed minimal changes in EEG while art making was associated with a persistent change from baseline of significant direction and amplitude involving both hemispheres, a change that was similar to the persistent change in EEG following rote motor tasks. These preliminary findings suggest that EEG may be a meaningful biomarker for cortical activation in the study of creative arts and points to further exploration using Mobile Brain Body Imaging (MoBI) in experimental designs. This system provides a reproducible, measurable, and quantitative methodology for evaluating brain activity and function in the study of the neuroscientific basis of creative arts, neuroaesthetics, and art therapy

Action and familiarity effects on self and other expert musicians’ Laban effort-shape analyses of expressive bodily behaviors in instrumental music performance: a case study approach

Self-reflective performance review and expert evaluation are features of Western music performance practice. While music is usually the focus, visual information provided by performing musicians’ expressive bodily behaviors communicates expressiveness to musically trained and untrained observers. Yet, within a seemingly homogenous group, such as one of musically trained individuals, diversity of experience exists. Individual differences potentially affect perception of the subtleties of expressive performance, and performers’ effective communication of their expressive intentions. This study aimed to compare self- and other expert musicians’ perception of expressive bodily behaviors observed in marimba performance. We hypothesized that analyses of expressive bodily behaviors differ between expert musicians according to their specialist motor expertise and familiarity with the music. Two professional percussionists and experienced marimba players, and one professional classical singer took part in the study. Participants independently conducted Laban effort-shape analysis – proposing that intentions manifest in bodily activity are understood through shared embodied processes – of a marimbists’ expressive bodily behaviors in an audio-visual performance recording. For one percussionist, this was a self-reflective analysis. The work was unfamiliar to the other percussionist and singer. Perception of the performer’s expressive bodily behaviors appeared to differ according to participants’ individual instrumental or vocal motor expertise, and familiarity with the music. Furthermore, individual type of motor experience appeared to direct participants’ attention in approaching the analyses. Findings support forward and inverse perception–action models, and embodied cognitive theory. Implications offer scientific rigor and artistic interest for how performance practitioners can reflectively analyze performance to improve expressive communication

Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

Background: For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks. Methods: 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O2Hb] signal features (Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis). Results: The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O2Hb] signal features comprising Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%. Conclusions: Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation

Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?

International audiencePredicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities