Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.

Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.

Negative Flashforward Imagery in Adolescent Social Anxiety Disorder: A Pilot Study of Imagery Reports and a Short EMDR Intervention.

Psychological treatments for social anxiety disorder (SAD) in adolescents have shown poorer outcomes than for other anxiety disorders. A relevant factor to consider for improving outcomes may be negative imagery. In this pilot study, we examined negative 'flashforward' imagery of feared catastrophic outcomes in adolescents with SAD and evaluated the feasibility and preliminary outcomes of a short eye movement desensitization and reprocessing (EMDR) intervention targeting this imagery. We used a case series design with a 1-week baseline period. Outcomes included symptoms of social anxiety and avoidance related to selected social situations and features of associated flashforward imagery as the proposed mechanism of change during the intervention. We found that six out of seven assessed adolescents reported to experience flashforwards and rated image distress, vividness and threat appraisal as high. In these six participants (aged 14-17 years old), the short EMDR flashforward intervention appeared feasible and was followed by a decrease in social anxiety and avoidance in five participants, while no notable changes were observed during the baseline period. Furthermore, we observed a decrease in flashforward imagery features in at least five participants. Nonparametric tests of the overall (group-based) changes during the intervention period partially supported these findings. Limitations include the small sample size and the lack of a control group. Results suggest that vivid and distressing flashforward imagery is a common experience and that targeting flashforwards with EMDR may be beneficial in treating social anxiety in youth. Further experimental research on effects and added value to current treatments is necessary. Trial Registration: Dutch Clinical Trial Register (National Trial Register [NTR]): NL8974.

Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network.

Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex neural network. In consideration of the structural and functional similarity of the neurons in a neuroanatomical region, i.e., a region of interest (ROI), we propose that the comprehensive performance of each ROI may be reflected by a specific representative dipole (RD), and the time-frequency spectrums of all RDs are applied simultaneously to Random Forest algorithm to give a quantitative metric of each ROI importance (RI). Then, the more divided sub-band spectral powers are reinforced by RI, and they are interpolated to a 2-dimensional (2D) plane transformed from 3D space of all RDs, yielding an ensemble representation of RD feature image sequences (ERDFIS). Furthermore, a lightweight network, including 2D separable convolution and gated recurrent unit (2DSCG), is developed to extract and classify the frequency-spatial and temporal features from ERDFIS, forming a novel MI decoding method in cortical level (called ERDFIS-2DSCG). Based on two public datasets, the decoding accuracies of ten-fold cross-validation are 89.89% and 94.35%, respectively. The results suggest that RD can embody the overall property of ROI in time-frequency-space domains, and ROI importance is helpful to highlight the subject-based characteristics of MI-EEG. Meanwhile, 2DSCG is matched well with ERDFIS, jointly improving the decoding performance.

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

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

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.

Sensorimotor Synchronization and Neural Entrainment to Imagined Rhythms in Individuals With Proficient Imagery Ability.

Sensorimotor synchronization (SMS) is the temporal coordination of motor movements with external or imagined stimuli. Finger-tapping studies indicate better SMS performance with auditory or tactile stimuli compared to visual. However, SMS with a visual rhythm can be improved by enriching stimulus properties (e.g., spatiotemporal content) or individual differences (e.g., one's vividness of auditory imagery). We previously showed that higher self-reported vividness of auditory imagery led to more consistent synchronization-continuation performance when participants continued without a guiding visual rhythm. Here, we examined the contribution of imagery to the SMS performance of proficient imagers, including an auditory or visual distractor task during the continuation phase. While the visual distractor task had minimal effect, SMS consistency was significantly worse when the auditory distractor task was present. Our electroencephalography analysis revealed beat-related neural entrainment, only when the visual or auditory distractor tasks were present. During continuation with the auditory distractor task, the neural entrainment showed an occipital electrode distribution, suggesting the involvement of visual imagery. Unique to SMS continuation with the auditory distractor task, we found neural and sub-vocal (measured with electromyography) entrainment at the three-beat pattern frequency. In this most difficult condition, proficient imagers employed both beat- and pattern-related imagery strategies. However, this combination was insufficient to restore SMS consistency to that observed with visual or no distractor task. Our results suggest that proficient imagers effectively utilized beat-related imagery in one modality when imagery in another modality was limited.

Neural signatures of motor imagery for a supernumerary thumb in VR: an EEG analysis.

Human movement augmentation is a rising field of research. A promising control strategy for augmented effectors involves utilizing electroencephalography through motor imagery (MI) functions. However, performing MI of a supernumerary effector is challenging, to which MI training is one potential solution. In this study, we investigate the validity of a virtual reality (VR) environment as a medium for eliciting MI neural activations for a supernumerary thumb. Specifically, we assess whether it is possible to induce a distinct neural signature for MI of a supernumerary thumb in VR. Twenty participants underwent a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements. Spectral power and event related desynchronization (ERD) analyses at the group level showed that the MI signature associated with the supernumerary thumb was indeed distinct, significantly different from both the baseline and the MI signature associated with the natural thumb, while single-trial classification showed that it is distinguishable with a 78% and 69% classification accuracy, respectively. Furthermore, spectral power and ERD analyses at the group level showed that the MI signatures associated with directional movement of the supernumerary thumb, flexion and extension, were also significantly different, and single-trial classification demonstrated that these movements could be distinguished with 60% accuracy. Fine-tuning the models further increased the respective classification accuracies, indicating the potential presence of personalized features across subjects.

Episodic recombination and the role of time in mental travel.

Mental time travel is often presented as a singular mechanism, but theoretical and empirical considerations suggest that it is composed of component processes. What are these components? Three hypotheses about the major components of mental time travel are commonly considered: (i) remembering and imagining might, respectively, rely on different processes, (ii) past- and future-directed forms of mental time travel might, respectively, rely on different processes, and (iii) the creation of episodic representations and the determination of their temporal orientation might, respectively, rely on different processes. Here, we flesh out the last of these proposals. First, we argue for 'representational continuism': the view that different forms of mental travel are continuous with regard to their core representational contents. Next, we propose an updated account of episodic recombination (the mechanism generating these episodic contents) and review evidence in its support. On this view, episodic recombination is a natural kind best viewed as a form of compositional computation. Finally, we argue that episodic recombination should be distinguished from mechanisms determining the temporal orientation of episodic representations. Thus, we suggest that mental travel is a singular capacity, while mental time travel has at least two major components: episodic representations and their temporal orientation. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

[The social imaginary of ageing: broadening the horizon of possibilities]. / L'imaginaire social du vieillissement, élargir l'horizon des possibles.

The social imaginary of aging confines the elderly to a narrow horizon. We need to broaden this horizon to open up new possibilities. We can do this by exploring the ways in which older people live their old age, by discovering what is not known. We can also do this by inventing new ways of aging, using the arts.

A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.

Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.

Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge.

Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances in BCI that ML can be credited with, the performance of BCI solutions is still not up to the mark, posing a major barrier to the widespread use of this technology. This paper proposes a novel, automatic feature selection method for BCI able to leverage both data-dependent and expert knowledge to suppress noisy features and highlight the most relevant ones thanks to a fuzzy logic (FL) system. Our approach exploits the capability of FL to increase the reliability of decision-making by fusing heterogeneous information channels while maintaining transparency and simplicity. We show that our method leads to significant improvement in classification accuracy, feature stability and class bias when applied to large motor imagery or attempt datasets including end-users with motor disabilities. We postulate that combining data-driven methods with knowledge derived from neuroscience literature through FL can enhance the performance, explainability, and learnability of BCIs.

Distinct and additive effects of visual and vibratory feedback for motor rehabilitation: an EEG study in healthy subjects.

INTRODUCTION: The use of visual and proprioceptive feedback is a key property of motor rehabilitation techniques. This feedback can be used alone, for example, for vision in mirror or video therapy, for proprioception in focal tendon vibration therapy, or in combination, for example, in robot-assisted training. This Electroencephalographic (EEG) study in healthy subjects explored the distinct neurophysiological impact of adding visual (video therapy), proprioceptive (focal tendinous vibration), or combined feedback (video therapy and focal tendinous vibration) to a motor imagery task. METHODS: Sixteen healthy volunteers performed 20 mental imagery (MI) tasks involving right wrist extension and flexion under four conditions: MI alone (IA), MI + video feedback observation (IO), MI + vibratory feedback (IV), and MI + observation + vibratory feedback (IOV). Brain activity was monitored with EEG, and time-frequency neurophysiological markers of movement were computed. The emotions of the patients were also measured during the task. RESULTS: In the alpha band, we observed bilateral ERD in the visual feedback conditions (IO, IOV). In the beta band, the ERD was bilateral in the IA, IV and IOV but more lateralized in the IV and IOV. After movement, we observed strong ERS in the IO and IOV but not in the IA or IV. Embodiment was stronger in conditions with vibratory feedback (IOV > IV > IA and IO) CONCLUSION: Conditions with visual feedback (IO, IOV) recruit the mirror neurons system (alpha ERD) and provide more accurate feedback of the task than IA and IV, which triggers motor validation pathways (beta rebound analysis). Vibratory feedback enhances the recruitment of the left sensorimotor areas, with a synergistic effect in the IOV (beta ERD analysis), thus maximizing embodiment. Visual and vibratory feedback recruits the sensorimotor cortex during motor imagery in different ways and can be combined to maximize the benefits of both techniques TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT04449328 .

Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals.

The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.

The Effect of Internal and external imagery on learning badminton long serve skill: The role of visual and audiovisual imagery.

This study aimed to examine the impact of internal and external audiovisual imagery on the learning of the badminton long serve skill. A lot of 42 right-handed novice women were selected using availability sampling. Participants were categorized into four groups based on their scores from the visual imagery ability questionnaire and Bucknell auditory questionnaire: Visual-Internal imagery, Visual-External imagery, AudioVisual-Internal imagery and AudioVisual-External imagery groups. To generate an auditory pattern, the shoulder joint's angular velocity of a skilled individual was recorded and translated into sound based on frequency characteristic changes. Subjects underwent four sessions of 40 trials each and subsequently participated in retention and transfer tests. Performance accuracy of the badminton long serve was assessed using the Scott and Fox standard test and repeated measures ANOVA was employed to compare performance across groups during test stages. While no significant differences were noted between groups during the acquisition stages, indicated that subjects in the AudioVisual imagery conditions outperformed those in Visual imagery during the retention test. Additionally, the AudioVisual-Internal Imagery group demonstrated superior performance compared to other groups. Internal imagery groups also exhibited better performance in the later stages of acquisition, retention and transfer tests compared to external imagery groups. These findings suggest that the incorporation of audiovisual imagery utilizing movement sonification, alongside physical practice, improves skill development more effectively than visual imagery alone.

Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.

This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.

Measures of mental imagery in emotional disorders: A COSMIN systematic review of psychometric properties.

BACKGROUND: Dysfunctional imagery processes characterise a range of emotional disorders. Valid, reliable, and responsive mental imagery measures may support the clinical assessment of imagery and advance research to develop theory and imagery-based interventions. We sought to review the psychometric properties of mental imagery measures relevant to emotional disorders. METHODS: A systematic review registered on the Open Science Framework was conducted using COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidance. Five databases were searched. COSMIN tools were used to assess the quality of study methodologies and psychometric properties of measures. RESULTS: Twenty-three articles describing twenty-one self-report measures were included. Measures assessed various imagery processes and were organised into four groups based on related emotional disorders. Study methodological quality varied: measure development and reliability studies were generally poor, while internal consistency and hypothesis testing studies were higher quality. Most measurement properties assessed were of indeterminate quality. CONCLUSION: Imagery measures were heterogenous and primarily disorder specific. Due to a lack of high-quality psychometric assessment, it is unclear whether most included imagery measures are valid, reliable, or responsive. Measures had limited evidence of content validity suggesting further research could engage clinical populations to ensure their relevance and comprehensiveness.

How can imaginal retraining for modifying food craving be improved?

Imaginal retraining (IR) is an emerging intervention technique in which people imagine avoidance behaviors towards imagined foods or other substances, such as throwing them away. Although IR shows promise in reducing initial craving for a range of substances, including alcohol and tobacco, effects appear less robust for craving for energy-dense foods. This raises the question of how IR for food craving can be improved. Here, we address this question informed by emerging findings from IR dismantling studies and the field of regular cognitive bias modification training paradigms. Based on current insights, we suggest the most optimal 'craving-reduction' effects for energy-dense food can likely be expected for IR that includes an overt motor movement. While it is not yet clear what movement works best for food, we suggest a tailored movement or Go/No-Go-based stop movement has the potential to be most effective. The most likely mechanism in reducing craving is cue-devaluation of trained vivid craving images regarding specific energy-dense food products. Future work is needed that investigates and assess the underlying mechanisms (e.g., updating beliefs; cue-devaluation), task characteristics (e.g., IR instructions; specific motor movements) and individual characteristics (e.g., perceived craving; vividness of food imagination) that determine IR effects.

Understanding effects of observing affordance-driven action during motor imagery through EEG analysis.

The aim of this paper is to investigate the impact of observing affordance-driven action during motor imagery. Affordance-driven action refers to actions that are initiated based on the properties of objects and the possibilities they offer for interaction. Action observation (AO) and motor imagery (MI) are two forms of motor simulation that can influence motor responses. We examined combined AO + MI, where participants simultaneously engaged in AO and MI. Two different kinds of combined AO + MI were employed. Participants imagined and observed the same affordance-driven action during congruent AO + MI, whereas in incongruent AO + MI, participants imagined the actual affordance-driven action while observing a distracting affordance involving the same object. EEG data were analyzed for the N2 component of event-related potential (ERP). Our study found that the N2 ERP became more negative during congruent AO + MI, indicating strong affordance-related activity. The maximum source current density (0.00611 µ A/mm 2 ) using Low-Resolution Electromagnetic Tomography (LORETA) was observed during congruent AO + MI in brain areas responsible for planning motoric actions. This is consistent with prefrontal cortex and premotor cortex activity for AO + MI reported in the literature. The stronger neural activity observed during congruent AO + MI suggests that affordance-driven actions hold promise for neurorehabilitation.

Enhancing motor imagery in the third-person perspective by manipulating sense of body ownership with virtual reality.

Virtual reality (VR)-guided motor imagery (MI) is a widely used approach for motor rehabilitation, especially for patients with severe motor impairments. Most approaches provide visual guidance from the first-person perspective (1PP). MI training with visual guidance from the third-person perspective (3PP) remains largely unexplored. We argue that 3PP MI training has its own advantages and can supplement 1PP MI. For some movements beyond the view of 1PP, such as shoulder shrugging and other axial movements, MI are suitable performed under 3PP. However, the efficiency of existing paradigms for 3PP MI is unsatisfactory. We speculate that the absence of sense of body ownership (SOO) from 3PP could be one possible factor and hypothesize that 3PP MI could be enhanced by eliciting SOO over a 3PP avatar. Based on our hypothesis, a novel paradigm was proposed to enhance 3PP MI by inducing full-body illusion (FBI) from 3PP, which is similar to the so-called out-of-body experience (OBE), using synchronous visuo-tactile stimulus with VR. The event-related Electroencephalograph (EEG) desynchronization (ERD) at motor-related regions from 31 healthy participants were calculated and compared with a control paradigm without "OBE" FBI induction. This study attempts to enhance 3PP MI with FBI induction. It offers an opportunity to perform MI guided by action observation from 3PP with elicited SOO to the observed avatar. We believe that 3PP MI could provide more possibilities for effective rehabilitation training, when SOO could be elicited to a virtual avatar and the present work demonstrates its viability and effectiveness.