Imagery Tasks Research Articles

Identifying a Suitable Signal Processing Technique for MI EEG Data

Motor imagery (MI) electroencephalography (EEG) technology is acquiring great attention from researchers due to its remarkable real-world applications. EEG signals inherit a high degree of non-stationarity, making their analysis not modest. Hence, choosing an appropriate signal processing approach becomes crucial. This comparative paper aims to identify a suitable signal processing method among famous approaches, namely short-time Fourier transform (STFT), continuous wavelet transform (CWT), and two variations of discrete wavelet transform maximal overlap DWT (MODWT) and MODWT multiresolution analysis (MODWTMRA). Different mother wavelet basis filters experimented with wavelet methods: Morse, Amor, Bump, Symlets, Daubechies, Coiflets, and Fejér-Korovkin. The different methods were tested on the classification of the right-hand and left-hand motor imagery tasks using the brain-computer interface (BCI) competition IV 2b dataset. A shallow convolutional neural network containing a single convolution layer was first trained and then used for classification. The experimental outcomes verified that MI EEG signals can be better analyzed and recognized using the maximal overlap-based signal processing methods. The classification accuracy proved that MODWT and MODWTMRA with the Symlets wavelet outperformed the other methods.

Differences in motor imagery abilities in active and sedentary individuals: new insights from backward-walking imagination

Evidence has shown that imagining a complex action, like backward-walking, helps improve the execution of the gesture. Despite this, studies in sport psychology have provided heterogeneous results on the use of motor imagery (MI) to improve performance. We aimed to fill this gap by analyzing how sport experience influences backward-walking MI processes in a sample of young women (n = 41, mean age = 21 ± 2.2) divided into Active and Sedentary. All participants were allocated to two randomized mental chronometric tasks, in which they had first to imagine and then execute forward-walking (FW) and backward-walking (BW). The Isochrony Efficiency measured the difference between imagination and execution times in both conditions (FW and BW). Moreover, we analyzed the ability to vividly imagine FW and BW within various perspectives in both groups through the Vividness of Movement Imagery Questionnaire (VMIQ-2). Findings showed that active individuals performed better in the BW imagery task when compared to sedentary ones (F1,39 = 4.98; p = 0.03*), while there were no differences between groups in the FW imagery task (F1,39 = .10; p = 0.75). Further, VMIQ-2 had evidenced that the ability to imagine backward is influenced by perspective used. Specifically, the use of internal visual imagery (IVI) led to worse Isochrony Efficiency (t32,25 = 2.16; p = 0.04*), while the use of kinesthetic imagery (KIN) led to better Isochrony Efficiency (t32,25 = − 2.34; p = 0.03*). These results suggest a close relation between motor experience and complex motor imagery processes and open new insights for studying these mental processes.

Brain activation by a VR-based motor imagery and observation task: An fMRI study.

Training motor imagery (MI) and motor observation (MO) tasks is being intensively exploited to promote brain plasticity in the context of post-stroke rehabilitation strategies. This may benefit from the use of closed-loop neurofeedback, embedded in brain-computer interfaces (BCI's) to provide an alternative non-muscular channel, which may be further augmented through embodied feedback delivered through virtual reality (VR). Here, we used functional magnetic resonance imaging (fMRI) in a group of healthy adults to map brain activation elicited by an ecologically-valid task based on a VR-BCI paradigm called NeuRow, whereby participants perform MI of rowing with the left or right arm (i.e., MI), while observing the corresponding movement of the virtual arm of an avatar (i.e., MO), on the same side, in a first-person perspective. We found that this MI-MO task elicited stronger brain activation when compared with a conventional MI-only task based on the Graz BCI paradigm, as well as to an overt motor execution task. It recruited large portions of the parietal and occipital cortices in addition to the somatomotor and premotor cortices, including the mirror neuron system (MNS), associated with action observation, as well as visual areas related with visual attention and motion processing. Overall, our findings suggest that the virtual representation of the arms in an ecologically-valid MI-MO task engage the brain beyond conventional MI tasks, which we propose could be explored for more effective neurorehabilitation protocols.

Optimized Deep Learning Model Using Modified Whale’s Optimization Algorithm for EEG Signal Classification

Brain-Computer Interface (BCI) is a technology in which Electroencephalogram (EEG) signals are utilized to create a link between a person’s mental state and a computer-based signal processing system that decodes the signals without needing muscle movement. The mental process of picturing the movement of a body component without actually moving that body part is known as Motor Imagery (MI). MI BCI is a Motor Imagery-based Brain-Computer Interface that allows patients with motor impairments to interact with their environment by operating robotic prostheses, wheelchairs, and other equipment. Feature extraction and classification are essential parts of the EEG signal processing for MI BCI. In this work, Whales Optimization Algorithm with an Improved Mutualism Phase is proposed to find the optimal Convolutional Neural Network architecture for the classification of motor imagery tasks with high accuracy and less computational complexity. The Neurosky and BCI IV 2a datasets were used to evaluate the proposed methodology. Experiments demonstrate that the suggested technique outperforms other competing methods regarding classification accuracy values at 94.1% and 87.7% for the Neurosky and BCI datasets, respectively.

Autonomic nervous system modulation during self-induced non-ordinary states of consciousness

Self-induced cognitive trance (SICT) is a voluntary non-ordinary state of consciousness characterized by a lucid yet narrowed awareness of the external surroundings. It involves a hyper-focused immersive experience of flow, expanded inner imagery, modified somatosensory processing, and an altered perception of self and time. SICT is gaining attention due to its potential clinical applications. Similar states of non-ordinary state of consciousness, such as meditation, hypnosis, and psychedelic experiences, have been reported to induce changes in the autonomic nervous system. However, the functioning of the autonomic nervous system during SICT remains poorly understood. In this study, we aimed to investigate the impact of SICT on the cardiac and respiratory signals of 25 participants proficient in SICT. To accomplish this, we measured various metrics of heart rate variability (HRV) and respiration rate variability (RRV) in three conditions: resting state, SICT, and a mental imagery task. Subsequently, we employed a machine learning framework utilizing a linear discriminant analysis classifier and a cross-validation scheme to identify the features that exhibited the best discrimination between these three conditions. The results revealed that during SICT, participants experienced an increased heart rate and a decreased level of high-frequency (HF) HRV compared to the control conditions. Additionally, specific increases in respiratory amplitude, phase ratio, and RRV were observed during SICT in comparison to the other conditions. These findings suggest that SICT is associated with a reduction in parasympathetic activity, indicative of a hyperarousal state of the autonomic nervous system during SICT.

Harmony of senses: Exploring the impact of sound aesthetic features' on taste imagery

People reliably associate visual aesthetic features such as curvature and symmetry with tastes. In the present study, considering the transitive hypothesis of crossmodal correspondences, we evaluated whether these findings would extend to the relationship between sound aesthetic features and tastes, and whether feature-based congruency or affective priming would explain the influence of melodies on taste imagery. In Experiment 1, we evaluated how people associate different melody profiles (balanced vs. unbalanced, smooth vs. jagged, symmetrical vs. asymmetrical, simpler vs. more complex) with different tastes (sweet, sour, bitter, salty), as well as the melodies' associated fluency, valence, and arousal. Smooth and complex melodies were perceived as sweeter, jagged and unbalanced melodies as sourer, asymmetrical and jagged melodies as more bitter, and jagged and balanced melodies as saltier. In Experiment 2, we selected the most strongly associated aesthetic sound dimension with tastes, namely contour, and evaluated whether crossmodal congruency, based on crossmodal correspondences, or affective priming would influence people’s sensory and hedonic imagery associated with sweet and sour foods. In the imagery tasks, the participants showed higher sour ratings in the sourness task and higher sweetness ratings in the sweetness task. In the sweet imagery task, smooth melodies led to sweeter and less sour food imagery, whereas jagged melodies in the sourness task led to more sour and less sweet food imagery. These results favour the crossmodal congruency explanation rather than the affective alternative, which we ponder in the general discussion.

Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems

Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.

Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG

Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this paper, the signal of each channel of raw EEG is decomposed into a finite set of narrowband signals using a Fourier-transformation-based bandpass filter. Rhythmic components of EEG are represented by each of the narrowband signals that characterize the brain activities related to MI tasks. The subband signals are arranged to extend the dimension of the EEG trial in the spatial domain. The spatial features are extracted from the set of extended trials using a common spatial pattern (CSP). An optimum number of features are employed to classify the motor imagery tasks using an artificial neural network. An integrated approach with full-band and narrowband signals is implemented to derive discriminative features for MI classification. In addition, the subject-dependent parameter optimization scheme enhances the performance of the proposed method. The performance evaluation of the proposed method is obtained using two publicly available benchmark datasets (Dataset I and Dataset II). The experimental results in terms of classification accuracy (93.88% with Dataset I and 91.55% with Dataset II) show that it performs better than the recently developed algorithms. The enhanced MI classification accuracy is very much applicable in BCI implementation.

Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022: BCI Turing Test

The Turing Test is a method of testing whether a machine has human intelligence. A novel brain–computer interface (BCI) Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022. Contestants developed algorithms that can distinguish if an instruction is issued by a human. Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario. Participants were asked to perform steady-state visual evoked potential (SSVEP) tasks or motor imagery (MI) tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants. Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems. This paper details the algorithms of the top five teams with the best performance in the final, lists the popular classification models and algorithms for MI and SSVEP, and discusses the effectiveness of each approach in improving classification performance and reducing the computation time.

Classification algorithm for EEG-based motor imagery using hybrid neural network with spatio-temporal convolution and multi-head attention mechanism.

Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.

PolyCosGraph: A Privacy-Preserving Cancelable EEG Biometric System

Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacy-preserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based on <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Poly</b> nomial transformation embedding <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cos</b> ine functions with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Graph</b> features of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.

Corticospinal excitability changes during muscle relaxation and contraction in motor imagery.

To enhance smooth muscle contraction and relaxation during rehabilitation and sports activities, a comprehensive understanding of the motor control mechanisms within the central nervous system is necessary. However, current knowledge on these aspects is insufficient. Therefore, this study aimed to deepen our understanding of motor controls, by investigating the alterations in corticospinal excitability within cortical motor areas related to muscle contraction and relaxation using motor imagery with a reaction time task paradigm. Transcranial magnetic stimulation was used to measure the motor-evoked potentials in the first dorsal interosseous muscle of the right hand after the 'go' signal. Static weak muscle contraction (Experiment 1: 18 healthy participants) and resting state (Experiment 2: 16 healthy participants) were applied as background factors, and a trial without motor imagery was performed as a control. Muscle contraction was maintained in the background in the contraction motor imagery. A decrease in excitability in the relaxation motor imagery task occurred compared with the control. When the muscles were at rest, an increase in excitability in the contraction motor imagery and a transient increase in excitability in the relaxation motor imagery occurred compared with the control condition. Hence, the excitability of contraction and relaxation motor imagery is characterized by a continuous increase in excitability, transient increase and subsequent decrease in excitability, respectively. These results suggest that muscle contraction sensory information in the background condition may be necessary for muscle relaxation. Matching the background conditions may be crucial when utilizing motor imagery for rehabilitation or sports training.

The perception of affective touch in women affected by obesity.

Pleasant and comforting bodily contacts characterized intimate and affective interactions. Affective touch informs us about others' emotions and intentions, sustains intimacy and closeness, protecting from loneliness and psychological distress. Previous evidence points to an altered experience of affective touch in clinical populations reporting interpersonal difficulties. However, there is no investigation of affective touch in obesity, which is often associated with negative affective-relational experiences since childhood. This study aimed to provide the first evidence about the experience of affective touch in obesity by comparing 14 women with obesity with 14 women with healthy weight. Participants rated the pleasantness of both imagined and actual tactile stimuli, which consisted of (i) soft-brush strokes, (ii) touches of the experimenter's hand, and (iii) of a plastic stick (as control, non-affective, stimulation). Participants should report the pleasantness of each kind of touch. Moreover, we explored lifespan experiences of affective touch and interpersonal pleasure in social contexts through self-report questionnaires. No differences emerged for the pleasantness of affective touch (in both the real and imagery task) between the two groups. However, participants with obesity reported less frequent and less satisfying early experiences of affective touch when compared with the controls. Our results spoke in favor of a preserved experience of affective touch when experimentally probed in obesity, despite a limited early exposure to bodily affective contacts. We interpreted our results in the light of the social reconnection hypothesis. Nevertheless, we provided crucial methodological considerations for future research, considering that both the experimenter's and the brush touch may not resemble adequately real-life experiences, in which affective touch involves intimate people.

Quantification of event related brain patterns for the motor imagery tasks using inter-trial variance technique

Quantification of event-related (de) synchronization (ERD/ERS) patterns is a challenging task in the field of motor imagery (MI)-based brain–computer interface (BCI). Accurately determining the optimal time and frequency band for localizing the ERD/ERS brain patterns is crucial in designing a robust MI-based BCI. To address above issue, this study proposes an inter-trial variance (IV) technique that focuses on localizing the ERD/ERS brain patterns and classifying the left and right hand imaginations of the subjects. The effectiveness of the proposed technique is validated using the BCI Competition-IV, Dataset-2b, which includes raw EEG signals from nine healthy subjects performing both left and right hand MI movements. In this technique, the sensorimotor frequency band (8–30 Hz) and its ERD/ERS brain patterns related to the left and right hand MI movements are derived from the signals. The obtained brain patterns are fed into various ML models, and the efficiency of the models is assessed using the classification accuracy (%CA) and Cohen’s kappa coefficient (K). The outcomes show that the proposed technique enhances the performance of the BCI system, classifying both left and right hand imaginations of the subjects with higher %CA (86.11%) and K (0.72), and outperforming the state-of-the-art techniques. This concludes that the ERD and ERS brain patterns extracted from the proposed technique are significant features in designing the MI-based BCI system.

Regulation of "Right Ankle Dorsiflexion" Motor Imagery on Brain Function of Spinal Cord Injury: A FOCA-Based Prospective Study.

Motor dysfunction is the main functional disability after spinal cord injury (SCI), seriously affecting the life and work of patients. In addition to spinal cord damage, the brain undergoes structural and functional plastic changes. This study explored brain function remodeling in patients with SCI and the effect of right ankle dorsiflexion motor imagery task on brain function. This prospective study enrolled 11 patients with SCI and dyskinesia of the right lower limb and 12 healthy subjects at the General Hospital of Western Theater Command PLA (January 2015 to December 2016). They underwent functional magnetic resonance imaging (fMRI) in the resting state and the "right ankle dorsiflexion" motor imagery task state. Four-dimensional (spatiotemporal) concordance (FOCA) of local neuronal activity was used for fMRI image analysis. The differences between SCI patients and healthy subjects were compared using the two-sample t-test. In the resting state, compared with healthy subjects, patients with SCI showed decreased FOCA in the left putamen, right caudate nucleus, and right superior occipital gyrus and increased FOCA in the left precentral gyrus. In the right ankle dorsiflexion motor imagery task state, FOCAs in the right inferior temporal gyrus and left inferior parietal lobule were decreased in patients with SCI. After SCI, a series of changes in the structure and function of the brain occur. Research on brain plasticity after SCI might help explore the central mechanisms underlying functional recovery after treatments, providing more therapeutic strategies for SCI.

Priming cross-session motor imagery classification with a universal deep domain adaptation framework

Electroencephalogram (EEG) based motor imagery (MI) brain–computer interfaces (BCI) are widely used in applications related to rehabilitation and external device control. However, due to the non-stationary and low signal-to-noise ratio characteristics of EEG, classifying motor imagery tasks of the same participant from different recording sessions is generally challenging. Whether the classification accuracy of cross-session MI can be improved from the perspective of domain adaptation is a question worth verifying. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The SDDA framework primarily consists of three components: a novel preprocessing method based on domain-invariant features, a maximum mean discrepancy (MMD) loss for aligning source and target domain embedding features, and an improved cosine-based center loss designed to suppress the influence of noise and outliers on the neural network. The SDDA framework has been validated with two classic and popular convolutional neural networks (EEGNet and ConvNet) from BCI research field in two MI EEG public datasets (BCI Competition IV IIA, IIB). Compared with the vanilla EEGNet and ConvNet, the SDDA framework improves the MI classification accuracy by 10.49%, 7.60% respectively in IIA dataset, and 4.59%, 3.35% in IIB dataset. The SDDA not only significantly improves the classification performance of the vanilla networks but also surpasses state-of-the-art transfer learning methods, making it a superior and user-friendly approach for MI classification.

Spontaneous eye blinking during an auditory, an interoceptive and a visual task: The role of the sensory modality and the attentional focus

Previous evidence suggested that spontaneous eye blinking changes as a function of the attentional focus. In particular, eye blink rate (EBR) tends to increase when attention is directed to internal versus environmental processing. Most studies on this issue compared eye blinking during visual and mental imagery tasks, and interpreted the increase in EBR as a mechanism to focus cognitive resources on internal processing by disengaging attention from interfering information. However, since eye blinking also depends on the sensory modality of the task, the findings might be influenced by a modality-specific effect.In the present Registered Report we aim at investigating whether the environmental versus internal attentional focus can affect spontaneous blinking behaviour in non-visual tasks as well, in conditions where visual stimuli are not relevant. In a within-subject design, healthy participants performed an interoceptive task (i.e., heartbeat counting) and an auditory task in which pre-recorded heartbeats were presented aurally; during both tasks irrelevant visual stimuli were also presented. In a further control condition with the same auditory and visual stimuli, the participants were required to focus their attention on visual stimuli. Participants' EBR was recorded during each task by means of an eye-tracking system.We found that, although the interoceptive task was more difficult than the auditory and visual tasks, participants' EBR decreased by a comparable level in all tasks with respect to a rest condition, with no differences between internal versus environmental conditions. The present findings do not support the idea that EBR is modulated by an internal versus external focus of attention, at least in presence of controlled visual stimulation.

Meaning in hand: Investigating shared mechanisms of motor imagery and sensorimotor simulation in language processing

There is substantial evidence to support grounded theories of semantic representation, however the mechanisms of simulation in most theories are underspecified. In the present study, we used an individual differences approach to test whether motor imagery may share some mechanisms with sensorimotor simulations engaged during semantic processing. We quantified individual differences in motor imagery ability via implicit imagery tasks and explicit imagery questionnaires and tested their relationship to sensorimotor effects in syntactic classification tasks. In Experiment 1 (N = 185) we tested relationships between motor imagery and semantic processing of body-object interaction meaning (BOI; the degree to which you can interact with a word's referent) and foot/leg action meaning. We observed two interactions between imagery ability measured on the Florida Praxis Imagery Questionnaire (FPIQ) and BOI effects in semantic processing (response time and accuracy). In both interactions poorer imagery ability was associated with null BOI effects, whereas better imagery was associated with BOI effects. We also observed faster and more accurate responses to verbs associated with more foot/leg action meaning than verbs with less foot/leg action meaning, but this foot/leg action effect did not significantly interact with individual differences in motor imagery. In Experiment 2 (N = 195) we tested whether the interactions observed in Experiment 1 were dependent on the object-directed nature of the actions, or whether similar effects would be observed for hand actions not associated with objects. We also expanded our investigation beyond hand and foot imagery to consider whole body imagery. We observed an interaction between performance on a hand laterality judgement task (HLJT; assessing hand motor imagery) and sensorimotor effects in semantic processing of verbs associated with hand/arm action meaning. Participants with the fastest responses on the most difficult trials of the HLJT showed no significant difference in their response times to words with high and low hand/arm action meaning. We also observed faster and more accurate responses to high relative to low embodiment verbs, but this sensorimotor effect did not interact with individual differences in motor imagery. The results suggest specific (and not general) associations, in that some, but not all forms of hand and object-directed motor imagery are related to sensorimotor effects in language processing of hand/arm action verbs and nouns describing objects that are easy to interact with. As such, hand and object-directed motor imagery may share mechanisms with sensorimotor simulation during semantic processing.

Re-learning mental representation of walking after a brain lesion. Effects of a cognitive-motor training with a robotic orthosis.

Stroke-related deficits often include motor impairments and gait dysfunction, leading to a limitation of social activities and consequently affecting the quality of life of stroke survivors. Neurorehabilitation takes advantage of the contribution of different techniques in order to achieve more benefits for patients. Robotic devices help to improve the outcomes of physical rehabilitation. Moreover, motor imagery seems to play a role in neurological rehabilitation since it leads to the activation of the same brain areas as actual movements. This study investigates the use of a combined physical and cognitive protocol for gait rehabilitation in stroke patients. Specifically, we tested the efficacy of a 5-week training program using a robotic orthosis (P.I.G.R.O.) in conjunction with motor imagery training. Twelve chronic stroke patients participated in the study. We evaluated balance and gait performance before and after the training. Six of them underwent fMRI examination before and after the training to assess the effects of the protocol on brain plasticity mechanisms in motor and imagery tasks. Our results show that the rehabilitation protocol can effectively improve gait performance and balance and reduce the risk of falls in stroke patients. Furthermore, the fMRI results suggest that rehabilitation is associated with cerebral plastic changes in motor networks. The present findings, if confirmed by future research, have the potential to advance the development of new, more effective rehabilitation approaches for stroke patients, improving their quality of life and reducing the burden of stroke-related disability.

Handedness effects on imagery of dominant- versus non-dominant-hand movements: An electroencephalographic investigation.

Mental representations of our bodies are thought to influence how we interact with our surroundings. We can examine these mental representations through motor imagery, the imagination of movement using scalp EEG recordings. The visual modality of motor imagery emphasises 'seeing' the imagined movement and is associated with increased activity in the alpha rhythm (8-14 Hz) measured over the occipital regions. The kinaesthetic modality emphasises 'feeling' the movement and is associated with decreased activity in the mu rhythm (8-14 Hz) measured over the sensorimotor cortices. These two modalities can be engaged in isolation or together. We recorded EEG activity while 37 participants (17 left-hand dominant) completed an objective hand motor imagery task. Left-handers exhibited significant activity differences between occipital and motor regions only during imagery of right-hand (non-dominant-hand) movements. This difference was primarily driven by less oscillatory activity in the mu rhythm, which may reflect a shift in imagery strategy wherein participants placed more effort into generating the kinaesthetic sensations of non-dominant-hand imagery. Spatial features of 8-14 Hz activity generated from principal component analysis (PCA) provide further support for a strategy shift. Right-handers also exhibited significant differences between alpha and mu activity during imagery of non-dominant movements. However, this difference was not primarily driven by either rhythm, and no differences were observed in the group's PCA results. Together, these findings indicate that individuals imagine movement differently when it involves their dominant versus non-dominant hand, and left-handers may be more flexible in their motor imagery strategies.