Using motor imagery practice for improving motor performance - A review.

Motor imagery practice is a current trend, but there is a need for a systematic integration of neuroscientific advances in the field. In this review, we describe the technique of motor imagery practice and its neural representation, considering different fields of application. The current practice of individualized motor imagery practice schemes often lacks systematization and is mostly based on experience. We review literature related to motor imagery practice in order to identify relevant modulators of practice effects like previous experience in motor training and motor imagery practice, the type of motor task to be trained, and strategies to increase sensory feedback during physical practice. Relevant discrepancies are identified between neuroscientific findings and practical consideration of these findings. To bridge these gaps, more effort should be directed at analyzing the brain network activities related to practically relevant motor imagery practice interventions.

Motor Imagery: How to Assess, Improve Its Performance, and Apply It for Psychosis Diagnostics.

With this review, we summarize the state-of-the-art of scientific studies in the field of motor imagery (MI) and motor execution (ME). We composed the brain map and description that correlate different brain areas with the type of movements it is responsible for. That gives a more complete and systematic picture of human brain functionality in the case of ME and MI. We systematized the most popular methods for assessing the quality of MI performance and discussed their advantages and disadvantages. We also reviewed the main directions for the use of transcranial magnetic stimulation (TMS) in MI research and considered the principal effects of TMS on MI performance. In addition, we discuss the main applications of MI, emphasizing its use in the diagnostics of various neurodegenerative disorders and psychoses. Finally, we discuss the research gap and possible improvements for further research in the field.

Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury.

Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.

Breathing with the mind: Effects of motor imagery on breath-hold performance.

We aimed at studying the effect of Motor Imagery (MI), i.e., the mental representation of a movement without executing it, on breath-holding performance. Classical guidelines for efficient MI interventions advocate for a congruent MI practice with regards to the requirements of the physical performance, specifically in terms of physiological arousal. We specifically aimed at studying whether an incongruent form of MI practice might enhance the breath-hold performance. In a counterbalanced design including three experimental sessions, participants engaged in maximal breath-hold trials while concomitantly performing i) MI of breathing, ii) MI of breath-hold, and iii) an "ecological" breath-hold trial, i.e., without specific instructions of MI practice. In addition to breath-hold durations, we measured the cardiac activity and blood oxygen saturation. Performance was improved during MI of breathing (73.06 s ±â€¯24.53) compared to both MI of breath-hold (70.57 s ±â€¯18.15) and the control condition (67.67 s ±â€¯19.27) (p < 0.05). The mechanisms underlying breath-hold performance improvements during MI of breathing remain uncertain. MI of breathing might participate to decrease the threat perception associated with breath-holding, presumably due to psychological and physiological effects associated with the internal simulation of a breathing body state.

An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface.

A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects. We trained the motor recognition model for the target subject using both the target's EEG signal and the EEG signals of other subjects. To reduce the individual variation of different datasets, we proposed two data mapping methods. These two methods separately diminished the variation caused by dissimilarities in the brain activation region and the strength of the brain activation in different subjects. After these data mapping stages, we adopted an ensemble method to aggregate the EEG signals from all subjects into a final model. We compared our method with other methods that reduce the calibration time. The results showed that our method achieves a satisfactory recognition accuracy using very few training trials (32 samples). Compared with existing methods using few training trials, our method achieved much greater accuracy. Graphical abstract The framework of the proposed method. The workflow of the framework have three steps: 1, process each subjects EEG signals according to the target subject's EEG signal. 2, generate models from each subjects' processed signals. 3, ensemble these models to a final model, the final model is a model for the target subject.

Imagine There Is No Plegia. Mental Motor Imagery Difficulties in Patients with Traumatic Spinal Cord Injury.

In rehabilitation of patients with spinal cord injury (SCI), imagination of movement is a candidate tool to promote long-term recovery or to control futuristic neuroprostheses. However, little is known about the ability of patients with spinal cord injury to perform this task. It is likely that without the ability to effectively perform the movement, the imagination of movement is also problematic. We therefore examined, whether patients with SCI experience increased difficulties in motor imagery (MI) compared to healthy controls. We examined 7 male patients with traumatic spinal cord injury (aged 23-70 years, median 53) and 20 healthy controls (aged 21-54 years, median 30). All patients had incomplete SCI, with AIS (ASIA Impairment Scale) grades of C or D. All had cervical lesions, except one who had a thoracic injury level. Duration after injury ranged from 3 to 314 months. We performed the Movement Imagery Questionnaire Revised as well as the Beck Depression Inventory in all participants. The self-assessed ability of patients to visually imagine movements ranged from 7 to 36 (Md = 30) and tended to be decreased in comparison to healthy controls (ranged 16-49, Md = 42.5; W = 326.5, p = 0.055). Also, the self-assessed ability of patients to kinesthetically imagine movements (range = 7-35, Md = 31) differed significantly from the control group (range = 23-49, Md = 41; W = 337.5, p = 0.0047). Two patients yielded tendencies for depressive mood and they also reported most problems with movement imagination. Statistical analysis however did not confirm a general relationship between depressive mood and increased difficulty in MI across both groups. Patients with spinal cord injury seem to experience difficulties in imagining movements compared to healthy controls. This result might not only have implications for training and rehabilitation programs, but also for applications like brain-computer interfaces used to control neuroprostheses, which are often based on the brain signals exhibited during the imagination of movements.

Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures.

Stroke frequently results in motor impairment. Motor imagery (MI), the mental practice of movements, has been suggested as a promising complement to other therapeutic approaches facilitating motor rehabilitation. Of particular potential is the combination of MI with neurofeedback (NF). However, MI NF protocols have been largely optimized only in younger healthy adults, although strokes occur more frequently in older adults. The present study examined the influence of age on the neural correlates of MI supported by electroencephalogram (EEG)-based NF and on the neural correlates of motor execution. We adopted a multimodal neuroimaging framework focusing on EEG-derived event-related desynchronization (ERD%) and oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations simultaneously acquired using functional near-infrared spectroscopy (fNIRS). ERD%, HbO concentration and HbR concentration were compared between younger (mean age: 24.4 years) and older healthy adults (mean age: 62.6 years). During MI, ERD% and HbR concentration were less lateralized in older adults than in younger adults. The lateralization-by-age interaction was not significant for movement execution. Moreover, EEG-based NF was related to an increase in task-specific activity when compared to the absence of feedback in both older and younger adults. Finally, significant modulation correlations were found between ERD% and hemodynamic measures despite the absence of significant amplitude correlations. Overall, the findings suggest a complex relationship between age and movement-related activity in electrophysiological and hemodynamic measures. Our results emphasize that the age of the actual end-user should be taken into account when designing neurorehabilitation protocols.

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination.

Flicker-noise spectroscopy (FNS) is a general phenomenological approach to analyzing dynamics of complex nonlinear systems by extracting information contained in chaotic signals. The main idea of FNS is to describe an information hidden in correlation links, which are present in the chaotic component of the signal, by a set of parameters. In the paper, FNS is used for the analysis of electroencephalography signal related to the hand movement imagination. The signal has been parametrized in accordance with the FNS method, and significant changes in the FNS parameters have been observed, at the time when the subject imagines the movement. For the right-hand movement imagination, abrupt changes (visible as a peak) of the parameters, calculated for the data recorded from the left hemisphere, appear at the time corresponding to the initial moment of the imagination. In contrary, for the left-hand movement imagination, the meaningful changes in the parameters are observed for the data recorded from the right hemisphere. As the motor cortex is activated mainly contralaterally to the hand, the analysis of the FNS parameters allows to distinguish between the imagination of the right- and left-hand movement. This opens its potential application in the brain-computer interface.

Comparison of movement related cortical potential in healthy people and amyotrophic lateral sclerosis patients.

OBJECTIVE: To understand the brain motor functions and neurophysiological changes due to motor disorder by comparing electroencephalographic data between healthy people and amyotrophic lateral sclerosis (ALS) patients. METHODS: The movement related cortical potential (MRCP) was recorded from seven healthy subjects and four ALS patients. They were asked to imagine right wrist extension at two speeds (fast and slow). The peak negativity (PN) and rebound rate (RR) were extracted from MRCP for comparison. RESULTS: The statistical analysis has showed that there was no significant difference in PN between the healthy and the ALS subjects. However, the healthy subjects presented faster RR than ALS during both fast and slow movement imagination. CONCLUSIONS: The weaker RR of ALS patients might reflect the impairment of motor output pathways or the degree of motor degeneration. SIGNIFICANCE: The comparison between healthy people and ALS patients provides a way to explain the movement disorder through brain electrical signal. In addition, the characteristics of MRCP could be used to monitor and guide brain plasticity in patients.