Previous interoception research has demonstrated that sensory processing is reduced during cardiac systole, an effect associated with diminished cortical excitability, possibly due to heightened baroreceptor activity. This study aims to determine how phases of the cardiac cycle—systole and diastole—modulate neural sensorimotor activity during motor imagery (MI) and motor execution (ME). We hypothesised that MI performance, indexed by enhanced suppression of contralateral sensorimotor alpha (8–13 Hz) and beta (14–30 Hz) activity, would be modulated by the cardiac phases, with improved performance during diastole due to enhanced sensory processing of movement cues. Additionally, we investigated whether movement cues during systole or diastole enhance muscle activity. To test these hypotheses, 29 participants were instructed to perform or imagine thumb abductions, while we recorded their electroencephalography, electrocardiogram, and electromyogram (EMG) activity. We show that imaginary movements instructed during diastole lead to more pronounced suppression of alpha and beta activity in contralateral sensorimotor cortices, with no significant cardiac timing effects observed during ME as confirmed by circular statistics. Additionally, diastole was associated with significantly increased EMG on the side of actual and, to a lesser degree, imagined movements. Our study identifies optimal cardiac phases for MI performance, suggesting potential pathways to enhance MI-based assistive technologies.

Brain–computer interfaces (BCIs) have gained popularity in recent years. Among noninvasive BCIs, EEG-based systems stand out as the primary approach, utilizing the motor imagery (MI) paradigm to discern movement intentions. Initially, BCIs were predominantly focused on nonembedded systems. However, there is now a growing momentum towards shifting computation to the edge, offering advantages such as enhanced privacy, reduced transmission bandwidth, and real-time responsiveness. Despite this trend, achieving the desired target remains a work in progress. To illustrate the feasibility of this shift and quantify the potential benefits, this paper presents a comparison of deploying a CNN for MI classification across different computing platforms, namely, CPU-, embedded GPU-, and FPGA-based. For our case study, we utilized data from 29 participants included in a dataset acquired using an EEG cap for training the models. The FPGA solution emerged as the most efficient in terms of the power consumption–inference time product. Specifically, it delivers an impressive reduction of up to 89% in power consumption compared to the CPU and 71% compared to the GPU and up to a 98% reduction in memory footprint for model inference, albeit at the cost of a 39% increase in inference time compared to the GPU. Both the embedded GPU and FPGA outperform the CPU in terms of inference time.

Fatigue is one of the most disabling symptoms of multiple sclerosis (MS), yet its neurobiology remains unclear, and there are no objective biomarkers. Previous studies using electroencephalography alone have revealed altered movement-related beta Event-Related Desynchronization (ERD) and Synchronization (ERS) dynamics in fatigued patients, but without providing mechanistic insight. In this cross-sectional study, we combined electroencephalography with transcranial magnetic stimulation, structural magnetic resonance imaging with diffusion tensor imaging (DTI), and clinical measures to probe the mechanistic basis of movement-related beta modulation depth (ERS-ERD) and its link with fatigue. Based on the Fatigue Severity Scale score (FSS), we enrolled 41 relapsing-remitting MS patients, 19 with clinically significant fatigue, 22 without (aged 25–55 years, 25 females), alongside 18 age- and sex-matched healthy volunteers. Participants underwent neuropsychological assessment, blood sampling for inflammatory and neurodegeneration-related biomarkers, structural magnetic resonance imaging with DTI to assess grey and white matter integrity, transcranial magnetic stimulation to quantify cortical excitatory and inhibitory balance, and continuous electroencephalography during 300 cued pinch movements to characterize movement-related beta dynamics. Compared with non-fatigued patients and healthy volunteers, fatigued patients exhibited reduced beta peak ERD to ERS modulation (P < 0.001), especially in frontal regions. The modulation depth correlated with fatigue severity (ρ = −0.54, P = 0.0006), intracortical facilitation (ρ = 0.49, P = 0.0009), and caudate nucleus volume (ρ = 0.35, P = 0.010). A nested elastic-net logistic regression integrating demographic, clinical, structural, and functional markers showed robust held-out performance (mean Receiver Operating Characteristic-Area Under the Curve = 0.92, Precision Recall-Area Under the Curve = 0.83, accuracy = 0.89, Brier score = 0.10). Variables with the strongest protective association with fatigue were higher intracortical facilitation, better mental health, larger caudate volume, greater beta modulation over the frontal regions, and higher corticospinal tract and superior longitudinal fasciculus integrity. These findings support frontal beta modulation as a mechanistically grounded, non-invasive biomarker of central fatigue in multiple sclerosis and highlight its potential utility for clinical diagnosis and targeted therapeutic intervention.

Central fatigue affects 80% of patients with multiple sclerosis, with 60% of them claiming it as the most disabling symptom. Current research often independently explores neurophysiological, structural, or functional imaging and biological underpinnings of fatigue, thus lacking a multidimensional perspective. Here, we used a multidimensional approach to investigate the functional, structural and biological underpinnings of fatigue in MS and to assess the relative contribution of each factor. A cross-sectional study was conducted with 41 patients with relapsing–remitting multiple sclerosis and 21 healthy controls (female 14) (HC). MS patients were recruited by including only those with an Expanded Disability Status Scale score < 4, and were categorized as fatigued (MS-F: 19, Female 13, FSS ≥ 4) or non-fatigued (MS-NF: 22, Female 11, FSS < 4). Over five phases, participants underwent Transcranial Magnetic Stimulation, resting-state Electroencephalography, structural and functional Magnetic Resonance, clinical assessments, and blood tests for neurofilament light chain, serum glial fibrillary acidic protein and cytokine levels. Data were analysed using both non-parametric and parametric tests, based on the data distribution. Finally, a decision-tree model was applied to predict patient group assignment. Neurophysiologically, the two patient groups differed in several domains. Those with fatigue had increased θ-band EEG power in frontocentral regions with eyes open. Transcranial Magnetic Stimulation findings indicated significantly lower intracortical facilitation in the MS-F group. Neuroimaging revealed stronger functional connectivity between nodes of the Default Mode Network, between the left temporal node and the right prefrontal node, in the MS-F group. Furthermore, fractional anisotropy via Diffusion Tensor Imaging showed reduced white matter integrity in the corticospinal tracts and corpus callosum in these patients. No significant differences were observed in lesion load, brain volumes, clinical/psychological measures, or blood sample findings linked with neurodegeneration or inflammation; the only psychological variable that differed between the two groups was the depression scale score, with MS-F patients reporting higher scores than MS-NF patients. The decision tree analysis identified both ICF and significantly lower fractional anisotropy values as the most accurate predictors of fatigue, with a classification accuracy of 84.2%. Results highlight the importance of a multidisciplinary approach in defining central fatigue in multiple sclerosis, which would emerge through subtle, subclinical, regional abnormalities of myelin integrity and clearly manifest neurophysiological evidence of impaired glutamatergic activity in motor areas. They also suggest possible biomarkers for the diagnosis of fatigue, possibly useful for eventual targeting novel neuromodulatory treatments.

Voluntary actions are associated with sense of agency and distortions of perceived duration. This study examined how sensorimotor coupling and visual perspective modulate perceived duration and sense of agency during manual actions in four pre-registered experiments using virtual reality. Participants moved their hands while observing a virtual hand moving (a)synchronously, and evaluated movement duration and their sense of agency. We found that sensorimotor coupling provided by synchronous visual feedback is associated with longer perceived duration relative to action-related time compression, compared with delayed feedback or pre-recorded movements of another person (Experiments 1 and 2). Sensorimotor coupling also modulated sense of agency and may serve as a shared basis for perceived time and agency. Comparable modulations across first- and third-person perspectives (Experiments 1 and 2), anatomical configurations (Experiment 3), and attention on external entities (Experiment 4) suggest a shared predictive system for sensory consequences of self-generated actions and others’ reactions. This modulation was observed irrespective of anatomical configuration and when attention was directed toward objects unrelated to one’s own body, suggesting that the underlying mechanism is not confined to representations specific to the self, but may instead reflect domain-general predictive computations. These findings, together with the virtual reality technique developed, offer new insights into how humans experience passage of time and agency during voluntary action.

Introduction The baroreceptor hypothesis posits that baroreceptors, located on the cardiac walls, are most active during systole, translating cardiac contraction information to the brain. Studies within this context have suggested that the systolic phase, characterised by increased noise, may compromise the processing of sensory stimuli. Although the effect of systolic and diastolic cardiac cycle phases on cognition, perception, and action has been widely documented, there remains a gap in applying these interoceptive insights to enhance assistive technologies such as brain-computer interfaces (BCIs). In the context of BCIs, motor imagery (MI) —the mental rehearsal of movement—serves as a widely used control paradigm, yet its modulation through the cardiac cycle has not been empirically tested. Bridging this gap, this study examined how the cardiac cycle phases influence MI by assessing their effect on contralateral suppression of alpha (8-13 Hz) and beta (14-30 Hz) activity in primary sensorimotor cortices. Materials & Methods Twenty-nine participants performed left/right thumb abductions based on the direction of an arrow presented on the screen to get familiarised with kinesthetic sensations. They then completed a MI task of the same movements. We recorded both electroencephalography (EEG) and electrocardiography (ECG), focusing our analysis on data epochs aligned with the experimental cue, based on whether it occurred during the systolic or diastolic phase of the cardiac cycle. Time-frequency analysis of source-reconstructed data assessed cue-induced changes in power spectral density (PSD) within the alpha and beta bands in the postcentral and precentral gyrus. Results We found that alpha and beta suppression in the contralateral primary motor and somatosensory cortex was more pronounced when the cue fell during the diastolic phase of the cardiac cycle than during the systolic phase. Validating the main results, an analysis with circular statistics revealed that trials with particularly pronounced contralateral alpha and beta suppression featured cues with latencies clustering during diastole, the quietest time of the cardiac cycle. Accompanying the EEG effects, EMG activity on the side of the movement was enhanced during diastole. Conclusion These findings provide evidence that MI performance can be enhanced by considering the cardiac cycle phases, offering promising implications for BCI-based applications. Key point 1: The phases of the cardiac cycle influence motor imagery performance. Key point 2: Alpha and beta contralateral suppression over sensorimotor cortices is more pronounced when movement direction is cued during diastole. Key point 3: Contralateral suppression is more likely to cluster during the quietest time of the diastolic phase: between the T-wave and the P-wave.