Imagery Tasks Research Articles

Early-stage fusion of EEG and fNIRS improves classification of motor imagery.

Many research papers have reported successful implementation of hybrid brain-computer interfaces by complementarily combining EEG and fNIRS, to improve classification performance. However, modality or feature fusion of EEG and fNIRS was usually designed for specific user cases, which were generally customized and hard to be generalized. How to effectively utilize information from the two modalities was still unclear. In this paper, we conducted a study to investigate the stage of bi-modal fusion based on EEG and fNIRS. A Y-shaped neural network was proposed and evaluated on an open dataset, which fuses the bimodal information in different stages. The results suggests that the early-stage fusion of EEG and fNIRS have significantly higher performance compared to middle-stage and late-stage fusion network configuration (N = 57, P < 0.05). With the proposed framework, the average accuracy of 29 participants reaches 76.21% in the left-or-right hand motor imagery task in leave-one-out cross-validation, using bi-modal data as network inputs respectively, which is in the same level as the state-of-the-art hybrid BCI methods based on EEG and fNIRS data.

F-waves induced by motor point stimulation are facilitated during handgrip and motor imagery tasks.

The F-wave is a motor response elicited via the antidromic firings of motor nerves by the electrical stimulation of peripheral nerves, which reflects the motoneuron pool excitability. However, the F-wave generally has low robustness i.e., low persistence and small amplitude. We recently found that motor point stimulation (MPS), which provides the muscle belly with electrical stimulation, shows different neural responses compared to nerve stimulation, e.g., MPS elicits F-waves more robustly than nerve stimulation. Here, we investigated whether F-waves induced by MPS can identify changes in motoneuron pool excitability during handgrip and motor imagery. Twelve participants participated in the present study. We applied MPS on their soleus muscle and recorded F-waves during eyes-open (EO), eyes-closed (EC), handgrip (HG), and motor imagery (MI) conditions. In the EO and EC conditions, participants relaxed with their eyes open and closed, respectively. In the HG, participants matched the handgrip force level to 30% of the maximum voluntary force with visual feedback. In the MI, they performed kinesthetic MI of plantarflexion at the maximal strength with closed eyes. In the HG and MI, the amplitudes of the F-waves induced by MPS were increased compared with those in the EO and EC, respectively. These results indicate that the motoneuron pool excitability was facilitated during the HG and MI conditions, consistent with findings in previous studies. Our findings suggest that F-waves elicited by MPS can be a good tool in human neurophysiology to assess the motoneuron pool excitability during cognitive and motor tasks.

Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network

The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.

My future-self has (not) quit smoking: An experimental study into the effect of a future-self intervention on smoking-related self-identity constructs

ObjectivesEnvisioning one's (non)smoking future may make (un)desired future identities more accessible, salient, and personally relevant and facilitate smoking cessation. The current study assessed whether a future-self intervention can weaken smoker self-identity and expected identity loss when quitting smoking, and strengthen quitter- and nonsmoker self-identity, while accounting for personal factors—socioeconomic position, nicotine dependence, consideration of future consequences, and clarity of the envisioned future-self. Additionally, it examined the association between smoking-related identity and quitting intention and behavior. MethodsThis longitudinal online experimental study randomized 233 adult smokers to an intervention condition (where they completed mental imagery, visual, and verbal tasks about a future (non)smoking self), or to a passive control condition. Smoker-, quitter-, nonsmoker self-identity and identity loss were measured post-intervention and after one- and three-months. Quit intention and attempts were measured at baseline and after one month. ResultsThere was a consistent increase in non-smoker self-identity, and decrease in smoker self-identity and identity loss over a period of six months for all participants, but no significant difference in smoking-related identity between the intervention and control group. While personal factors did not moderate the effect of the intervention, we found that smoking-related identity constructs do vary with nicotine dependence, consideration of future consequences, and clarity of the envisioned future-self. Quitting behavior is primarily associated with non-smoker self-identity. ConclusionsAlthough the future-self intervention did not significantly influence smoking-related identity or behavior, identity—in particular, non-smoker self-identity—is important to consider in smoking cessation interventions. More research is needed to find effective operationalizations for identity-based interventions in the context of smoking.

Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with the device by tackling a sequence of motor imagery tasks. However, the extracting user-specific features and increasing the accuracy of the classifier remain as difficult tasks in MI-based BCI. In this work, we propose a new method using artificial neural network (ANN) enhancing the performance of the motor imagery classification. Feature extraction techniques, like time domain parameters, band power features, signal power features, and wavelet packet decomposition (WPD), are studied and compared. Four classification algorithms are implemented which are Quadratic Discriminant Analysis, k-Nearest Neighbors, Linear Discriminant Analysis, and proposed ANN architecture. We added Batch Normalization layers to the proposed ANN architecture to improve the learning time and accuracy of the neural network. These layers also alleviate the effect of weight initialization and the addition of a regularization effect on the network. Our proposed method using ANN architecture achieves 0.5545 of kappa and 58.42% of accuracy on the BCI Competition IV-2a dataset. Our results show that the modified ANN method, with frequency and spatial features extracted by WPD and Common Spatial Pattern, respectively, offers a better classification compared to other current methods.

Optimized Motor Imagery Paradigm via Multimodal Stimulation and Explainable LSTM Model in fNIRS-based BCI

Motor imagery-based brain–computer interface (MI-BCI) has been applied in the field of motor function rehabilitation for the improvement of patients’ motor function. Functional near-infrared spectroscopy (fNIRS) can be used to study the working mechanism of the brain by monitoring hemodynamic responses related to the activation of cortical neurons. Hence, a novel MI-fNIRS-BCI system with multimodal stimulation is proposed. The multimodal stimulation paradigms include “visual-auditory stimulation,” “electrical stimulation + proprioceptive stimulation,” and “visual-auditory stimulation + electrical stimulation + proprioceptive stimulation.” We explored the optimization of the combination of various stimulations to enhance motor imagery patterns. Furthermore, an explainable long short-term memory (e-LSTM) model was designed to decode cortical activation. The motor imagery task was classified using an LSTM network and the relationship between the classification results and cortical activation was analyzed using an explanation module. Comparative experiments were conducted with eight healthy subjects. The results demonstrated that classification accuracies were significantly improved in the multimodal stimulation paradigms. The proposed MI-fNIRS-BCI system can improve the motor imagery patterns and enhance cortical activation during motor imagery.

PSIs of EEG with Refined Frequency Decomposition Could Prognose Motor Recovery Before Rehabilitation Intervention.

Stroke often leads to permanent impairment in motor function. Accurate and quantitative prognosis of potential motor recovery before rehabilitation intervention can help healthcare centers improve resources organization and enable individualized intervention. The context of this paper investigated the potential of using electroencephalography (EEG) functional connectivity (FC) measures as biomarkers for assessing and prognosing improvement of Fugl-Meyer Assessment in upper extremity motor function (ΔFMU) among participants with chronic stroke. EEG data from resting and motor imagery task were recorded from 13 participants with chronic stroke. Three functional connectivity methods, which were Pearson correlation measure (PCM), weighted Phase Lag Index (wPLI) and phase synchronization index (PSI), were investigated, under three regions of interest (inter-hemispheric, intra-hemispheric, and whole-brain), in two statues (resting and motor imagery), with 15 refined center frequencies. We applied correlation analysis to identify the optimal center frequencies and pairs of synchronized channels that were consistently associated with ΔFMU. Predictive models were generated using regression analysis algorithms based on optimized center frequencies and channel pairs identified from the proposed analysis method, with leave-one-out cross-validation. We found that PSI in the Alpha band (with center frequency of 9Hz) was the most sensitive FC measures for prognosing motor recovery. Strong and significant correlations were identified between the predictions and actual ΔFMU scores both in the resting state (R2=0.79, P<0.001, N=13) and motor imagery (R2=0.65, P<0.001, N=13). Our results suggested that EEG connectivity measured with PSI in resting state could be a promising biomarker for quantifying motor recovery before motor rehabilitation intervention.

Decoding Imagined Musical Pitch From Human Scalp Electroencephalograms.

Brain-computer interfaces (BCIs) can restore impaired cognitive functions in people with neurological disorders such as stroke. Musical ability is a cognitive function that is correlated with non-musical cognitive functions, and restoring it can enhance other cognitive functions. Pitch sense is the most relevant function to musical ability according to previous studies of amusia, and thus decoding pitch information is crucial for BCIs to be able to restore musical ability. This study evaluated the feasibility of decoding pitch imagery information directly from human electroencephalography (EEG). Twenty participants performed a random imagery task with seven musical pitches (C4-B4). We used two approaches to explore EEG features of pitch imagery: multiband spectral power at individual channels (IC) and differences between bilaterally symmetric channels (DC). The selected spectral power features revealed remarkable contrasts between left and right hemispheres, low- (< 13 Hz) and high-frequency ( 13 Hz) bands, and frontal and parietal areas. We classified two EEG feature sets, IC and DC, into seven pitch classes using five types of classifiers. The best classification performance for seven pitches was obtained using IC and multiclass Support Vector Machine with an average accuracy of 35.68±7.47% (max. 50%) and an information transfer rate (ITR) of 0.37±0.22 bits/sec. When grouping the pitches to vary the number of classes (K = 2-6), the ITR was similar across K and feature sets, suggesting the efficiency of DC. This study demonstrates for the first time the feasibility of decoding imagined musical pitch directly from human EEG.

Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities.

A new kind of sequence-to-sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG-based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery-based brain-computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi-head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.

Hybrid EEG-fNIRS Brain Computer Interface Based on Common Spatial Pattern by Using EEG-Informed General Linear Model

Hybrid brain computer interfaces (BCI) utilizing the high temporal resolution of electroencephalography (EEG) and the high spatial resolution of near-infrared spectroscopy (fNIRS) are preferred over single-modal BCIs. However, due to the large dimensionality of the multi-class statistical features commonly used in fNIRS signals, it is easy to cause overfitting of the EEG-fNIRS hybrid BCI classifier. Therefore, a low-dimensional feature extraction method for fNIRS based on the EEG-informed fNIRS general linear model (GLM) analysis is proposed in this paper. First, a regression coefficient matrix is obtained by using the EEG-informed fNIRS GLM with a time window added, and the common spatial pattern (CSP) features of this regression coefficient matrix are extracted as the fNIRS features. Lastly, the fNIRS features were combined with the CSP features extracted from the optimal narrow band of EEG as hybrid features, and the support vector machine (SVM) method is used to classify the samples with hybrid features. The proposed method was tested on a publicly available motor imagery dataset. The classification accuracy using fNIRS signals alone reached 68.79% (oxygenated hemoglobin) and 68.62% (deoxygenated hemoglobin), and the classification accuracy of combining EEG-fNIRS features reached 79.48%, which was higher than other existing methods using the same dataset. By using this fNIRS feature extraction method, the problem of poor performance of CSP on fNIRS signals is solved, which not only enriches the processing methods of fNIRS signals, but also improves the classification accuracy of hybrid EEG-fNIRS BCI in motor imagery tasks.

Affective touch in anorexia nervosa: Exploring the role of social anhedonia and lifespan experiences

BackgroundThe pleasantness of a gentle and slow, namely affective, touch experienced in interpersonal interactions motivates social closeness. In anorexia nervosa (AN), independent evidence suggests lower pleasantness of affective touch, as well as social withdrawal. We aim to probe both the experience of affective touch and its possible association with social anhedonia and lifespan experiences of affective bodily contacts in AN. MethodsThe pleasantness of affective and non-affective touch was compared between fourteen women with AN and fourteen healthy women. Stimuli were traditionally delivered with a brush, with the experimenter's hand, as novelty, and with a stick, as control. The pleasantness of imagined and real touch was probed. Self-report questionnaires assessed social anhedonia and lifespan experiences of affective touch. ResultsA preserved pleasantness of affective touch emerged in AN in both the imagery and real task, despite higher social anhedonia and less lifespan experience of affective touch than healthy women. LimitationsAffective touch involves loved ones; thus, the experimenter's touch may not resemble real-life interactions. Future research may take advantage of imagery procedures to solve this issue. ConclusionsBody-oriented therapy for AN recognizes touch as a therapeutic tool: ascertaining how touch is experienced is crucial to maximize rehabilitative outcomes. Furthermore, clarifying the possible interplay between interpersonal difficulties in AN and affective touch is especially relevant considering the possible role of the attachment style, which is intensively debated in AN, on affective touch.

Repetitive Transcranial Magnetic Stimulation of the Brain Region Activated by Motor Imagery Involving a Paretic Wrist and Hand for Upper-Extremity Motor Improvement in Severe Stroke: A Preliminary Study.

Approximately two-thirds of stroke survivors experience chronic upper-limb paresis; however, treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) can enhance motor function recovery in stroke survivors, but its efficacy is controversial. We compared the efficacy of stimulating different targets in 10 chronic stroke patients with severe upper-limb motor impairment. Motor imagery-based brain-computer interface training augmented with virtual reality was used to induce neural activity in the brain region during an imagery task. Participants were then randomly assigned to two groups: an experimental group (received high-frequency rTMS delivered to the brain region activated earlier) and a comparison group (received low-frequency rTMS delivered to the contralesional primary motor cortex). Behavioural metrics and diffusion tensor imaging were compared pre- and post rTMS. After the intervention, participants in both groups improved somewhat. This preliminary study indicates that in chronic stroke patients with severe upper-limb motor impairment, inducing activation in specific brain regions during motor imagery tasks and selecting these regions as a target is feasible. Further studies are needed to explore the efficacy of this intervention.

Involuntary refreshing of mental representations

Laboratory tasks have revealed that mental representations (e.g., mental imagery) can enter consciousness in a manner that is involuntary, reliable, and insuppressible. These effects illuminate the capacities of involuntary processes as well as the function of voluntary, conscious processing. The Reflexive Imagery Task was developed a decade ago to investigate these involuntary effects systematically. Can refreshing yield such involuntary effects? Refreshing is the reactivating in mind of a mental representation that was activated moments ago. It is associated with mental rehearsal and executive function. We investigated whether a mental representation (subvocalization of an object name) can arise in consciousness involuntarily after a delayed interval, when the relevant stimulus is no longer present, and in response to a cue. In Experiment 1, participants were instructed not to refresh a previously presented (6 s before) stimulus in response to a cue. Involuntary refreshing occurred on a substantive proportion (0.56) of the trials. Experiment 2 replicated and extended this finding (proportion of the trials = 0.53) with a refreshing task that was more challenging than that of Experiment 1. Our findings suggest that mental representations arising from processes such as refreshing can occur involuntarily. We discuss the theoretical implications of this conclusion.

A Method for Using Video Presentation to Increase Cortical Region Activity during Motor Imagery Tasks in Stroke Patients.

Previous studies have reported that stroke patients have difficulty recalling the motor imagery (MI) of a task, also known as MI vividness. Research on combining MI with action observation is gaining importance as a method to improve MI vividness. We enrolled 10 right-handed stroke patients and compared MI vividness and cortical activity under different presentation methods (no inverted image, inverted image of another individual's hand, and an inverted image of the patient's nonparalyzed hand) using near-infrared spectroscopy. Images of the nonparalyzed upper limb were inverted to make the paralyzed upper limb appear as if it were moving. Three tasks (non inverted image, AO + MI (other hand), AO + MI (own hand)) were randomly performed on 10 stroke patients. MI vividness was significantly higher when the inverted image of the nonparalyzed upper limb was presented compared to the other conditions (p &lt; 0.01). The activity of the cortical regions was also significantly enhanced (p &lt; 0.01). Our study highlights the potential application of inverted images of a stroke patient's own nonparalyzed hand in mental practice to promote the motor recovery of stroke patients. This technique achieved higher levels of MI vividness and cortical activity when performing motor tasks.

Analysis of instantaneous brain interactions contribution to a motor imagery classification task.

The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself.

Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks.

Emerging deep learning approaches to decode motor imagery (MI) tasks have significantly boosted the performance of brain-computer interfaces. Although recent studies have produced satisfactory results in decoding MI tasks of different body parts, the classification of such tasks within the same limb remains challenging due to the activation of overlapping brain regions. A single deep learning model may be insufficient to effectively learn discriminative features among tasks. The present study proposes a framework to enhance the decoding of multiple hand-MI tasks from the same limb using a multi-branch convolutional neural network. The CNN framework utilizes feature extractors from established deep learning models, as well as contrastive representation learning, to derive meaningful feature representations for classification. The experimental results suggest that the proposed method outperforms several state-of-the-art methods by obtaining a classification accuracy of 62.98% with six MI classes and 76.15 % with four MI classes on the Tohoku University MI-BCI and BCI Competition IV datasets IIa, respectively. Despite requiring heavy data augmentation and multiple optimization steps, resulting in a relatively long training time, this scheme is still suitable for online use. However, the trade-of between the number of base learners, training time, prediction time, and system performance should be carefully considered.

Mental imagery of nature induces positive psychological effects

Exposure to natural environments promotes positive psychological effects. Experimental studies on this issue typically have not been able to distinguish the contributions of top-down processes from stimulus-driven bottom-up processing. We tested in an online study whether mental imagery (top-down processing) of restorative natural environments would produce positive psychological effects, as compared with restorative built and non-restorative urban environments. The participants (n = 70) from two countries (Finland and Norway) imagined being present in different environments for 30 s, after which they rated their subjective experiences relating to vividness of imagery, relaxation, emotional arousal, valence (positivity vs. negativity) of emotions, and mental effort. In addition, a psychometric scale measuring vividness of imagination, a scale measuring nature connectedness, and a questionnaire measuring preference of the imagined environments were filled-in. Imagery of natural environments elicited stronger positive emotional valence and more relaxation than imagery of built and urban environments. Nature connectedness and preference moderated these effects, but they did not fully explain the affective benefits of nature. Scores in a psychometric imagery scale were associated in consistent way to the subjective ratings in the imagery task, suggesting that the participants performed attentively and honestly in reporting their subjective experiences. We conclude that top-down factors play a key role in the psychological effects of nature. A practical implication of the findings is that inclusion of natural elements in imagery-based interventions may help to increasing positive affective states.

Repetitive Peripheral Magnetic Stimulation Combined with Motor Imagery Changes Resting-State EEG Activity: A Randomized Controlled Trial.

Repetitive peripheral magnetic stimulation is a novel non-invasive technique for applying repetitive magnetic stimulation to the peripheral nerves and muscles. Contrarily, a person imagines that he/she is exercising during motor imagery. Resting-state electroencephalography can evaluate the ability of motor imagery; however, the effects of motor imagery and repetitive peripheral magnetic stimulation on resting-state electroencephalography are unknown. We examined the effects of motor imagery and repetitive peripheral magnetic stimulation on the vividness of motor imagery and resting-state electroencephalography. The participants were divided into a motor imagery group and motor imagery and repetitive peripheral magnetic stimulation group. They performed 60 motor imagery tasks involving wrist dorsiflexion movement. In the motor imagery and repetitive peripheral magnetic stimulation group, we applied repetitive peripheral magnetic stimulation to the extensor carpi radialis longus muscle during motor imagery. We measured the vividness of motor imagery and resting-state electroencephalography before and after the task. Both groups displayed a significant increase in the vividness of motor imagery. The motor imagery and repetitive peripheral magnetic stimulation group exhibited increased β activity in the anterior cingulate cortex by source localization for electroencephalography. Hence, combined motor imagery and repetitive peripheral magnetic stimulation changes the resting-state electroencephalography activity and may promote motor imagery.

Use of functional magnetic resonance imaging to assess cognition and consciousness in severe Guillain-Barré syndrome

ObjectiveFunctional neuroimaging may provide a viable means of assessment and communication in patients with Guillain-Barré Syndrome (GBS) mimicking the complete locked-in state. Functional neuroimaging has been used to assess residual cognitive function and has allowed for binary communication with other behaviourally non-responsive patients, such as those diagnosed with unresponsive wakefulness syndrome. We evaluated the potential application of functional neuroimaging using a clinical-grade scanner to determine if individuals with severe GBS retained auditory function, command following, and communication. MethodsFourteen healthy participants and two GBS patients were asked to perform motor imagery and spatial navigation imagery tasks while being scanned using functional magnetic resonance imaging. The GBS patients were also asked to perform additional functional neuroimaging scans to attempt communication. ResultsThe motor imagery and spatial navigation task elicited significant activation in appropriate regions of interest for both GBS patients, indicating intact command following. Both patients were able to use the imagery technique to communicate in some instances. Patient 1 was able to use one of four communication tasks to answer a question correctly. Patient 2 was able to use three of seven communication tasks. However, two questions were incorrectly answered while a third was non-verifiable. ConclusionsGBS patients can respond using mental imagery and these responses can be detected using functional neuroimaging. Furthermore, these patients may also be able to use mental imagery to provide answers to ‘yes’ or ‘no’ questions in some instances. We argue that the most appropriate use of neuroimaging-based communication in these patients is to allow them to communicate wishes or preferences and assent to previously expressed decisions, rather than to facilitate decision-making.

Interference of unilateral lower limb amputation on motor imagery rhythm and remodeling of sensorimotor areas.

The effect of sensorimotor stripping on neuroplasticity and motor imagery capacity is unknown, and the physiological mechanisms of post-amputation phantom limb pain (PLP) illness remain to be investigated. In this study, an electroencephalogram (EEG)-based event-related (de)synchronization (ERD/ERS) analysis was conducted using a bilateral lower limb motor imagery (MI) paradigm. The differences in the execution of motor imagery tasks between left lower limb amputations and healthy controls were explored, and a correlation analysis was calculated between level of phantom limb pain and ERD/ERS. The multiple frequency bands showed a significant ERD phenomenon when the healthy control group performed the motor imagery task, whereas amputees showed significant ERS phenomena in mu band. Phantom limb pain in amputees was negatively correlated with bilateral sensorimotor areas electrode powers. Sensorimotor abnormalities reduce neural activity in the sensorimotor cortex, while the motor imagination of the intact limb is diminished. In addition, phantom limb pain may lead to over-activation of sensorimotor areas, affecting bilateral sensorimotor area remodeling.