Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human–robot interaction may benefit from integration with human–human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain–computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning. Event-related desynchronization (ERD) was estimated to measure cortical activation, and interbrain functional connectivity was assessed using multilevel statistical analysis. Furthermore, we compared BCI classification performance under different social interaction conditions. In the experimental group, greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction. Notably, EEG channels with decreased power were mainly distributed around the frontal, central, and occipital regions. A significant increase in interbrain coupling was also found under social interaction conditions. BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition. However, for the strangers’ group, no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions. These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling. This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.

Transcutaneous electrical spinal cord stimulation (tSCS) is a non-invasive neuromodulatory technique that has in recent years been linked to improved volitional limb control in spinal-cord injured individuals. Although the technique is growing in popularity there is still uncertainty regarding the neural mechanisms underpinning sensory and motor recovery. Brain monitoring techniques such as electroencephalography (EEG) may provide further insights to the changes in coritcospinal excitability that have already been demonstrated using other techniques. It is unknown, however, whether intelligible EEG can be extracted while tSCS is being applied, owing to substantial high-amplitude artifacts associated with stimulation-based therapies. Here, for the first time, we characterise the artifacts that manifest in EEG when recorded simultaneously with tSCS. We recorded multi-channel EEG from 21 healthy volunteers as they took part in a resting state and movement task across two sessions: One with tSCS delivered to the cervical region of the neck, and one without tSCS. An offline analysis in the time and frequency domain showed that tSCS manifested as narrow, high-amplitude peaks with a spectral density contained at the stimulation frequency. We quantified the altered signals with descriptive statistics—kurtosis, root-mean-square, complexity, and zero crossings—and applied artifact-suppression techniques—superposition of moving averages, adaptive, median, and notch filtering—to explore whether the effects of tSCS could be suppressed. We found that the superposition of moving averages filter was the most successful technique at returning contaminated EEG to levels statistically similar to that of normal EEG. In the frequency domain, however, notch filtering was more effective at reducing the spectral power contribution of stimulation from frontal and central electrodes. An adaptive filter was more appropriate for channels closer to the stimulation site. Lastly, we found that tSCS posed no detriment the binary classification of upper-limb movements from sensorimotor rhythms, and that adaptive filtering resulted in poorer classification performance. Overall, we showed that, depending on the analysis, EEG monitoring during transcutaneous electrical spinal cord stimulation is feasible. This study supports future investigations using EEG to study the activity of the sensorimotor cortex during tSCS, and potentially paves the way to brain–computer interfaces operating in the presence of spinal stimulation.

Combination therapy with motor imagery (MI)-based brain-computer interface (BCI) and repetitive transcranial magnetic stimulation (rTMS) is a promising therapy for poststroke neurorehabilitation. However, with patients’ individual differences, the clinical effects vary greatly. This study aims to explore the hypothesis that stroke patients show individualized cortical response to rTMS treatments, which determine the effectiveness of rTMS-induced MI decoding enhancement. We applied four kinds of rTMS treatments respectively to four groups of subacute stroke patients, twenty-six patients in total, and observed their EEG dynamics, MI decoding performance, and Fugl-Meyer assessment changes following 2-week neuromodulation. Four treatments consisted of ipsilesional 10 Hz rTMS, contralesional 1 Hz rTMS, ipsilesional 1 Hz rTMS, and sham stimulation. Results showed stroke patients with different neural reorganization patterns responded individually to rTMS therapy. Patients with cortical lesions mostly showed contralesional recruitment and patients without cortical lesions mostly presented ipsilesional focusing. Significant activation increases in the ipsilesional hemisphere (pre: −15.7% ∓ 8.2%, post: −17.3% ∓ 8.1%, p = 0.037) and MI decoding accuracy enhancement (pre: 76.3 ± 13.8%, post: 86.6 ± 8.2%, p = 0.037) were concurrently found in no-cortical-lesion patients with ipsilesional activation treatment. In the group of patients without cortical lesions, recovery rate in those receiving ipsilesional activation therapy (23.5 ± 10.4%) was higher than those receiving ipsilesional suppression therapy (9.9 ± 9.3%) (p = 0.041). This study reveals that tailoring neuromodulation therapy by recognizing cortical activation patterns is promising for improving effectiveness of the combination therapy with BCI and rTMS.

Transcutaneous spinal cord stimulation (tSCS) can improve upper-limb motor function after spinal cord injury. A number of studies have attempted to deduce the corticospinal mechanisms which are modulated following tSCS, with many relying on transcranial magnetic stimulation to provide measures of corticospinal excitability. Other metrics, such as cortical oscillations, may provide an alternative and complementary perspective on the physiological effect of tSCS. Hence, the present study recorded EEG from 30 healthy volunteers to investigate if and how cortical oscillatory dynamics are altered by 10 min of continuous cervical tSCS. Participants performed repetitive upper-limb movements and resting-state tasks while tSCS was delivered to the posterior side of the neck as EEG was recorded simultaneously. The intensity of tSCS was tailored to each participant based on their maximum tolerance (mean: 50 ± 20 mA). A control session was conducted without tSCS. Changes to sensorimotor cortical activity during movement were quantified in terms of event-related (de)synchronisation (ERD/ERS). Our analysis revealed that, on a group level, there was no consistency in terms of the direction of ERD modulation during tSCS, nor was there a dose-effect between tSCS and ERD/ERS. Resting-state oscillatory power was compared before and after tSCS but no statistically significant difference was found in terms of alpha peak frequency or alpha power. However, participants who received the highest stimulation intensities had significantly weakened ERD/ERS (10% ERS) compared to when tSCS was not applied (25% ERD; p = 0.016), suggestive of cortical inhibition. Overall, our results demonstrated that a single 10 min session of tSCS delivered to the cervical region of the spine was not sufficient to induce consistent changes in sensorimotor cortical activity among the entire cohort. However, under high intensities there may be an inhibitory effect at the cortical level. Future work should investigate, with a larger sample size, the effect of session duration and tSCS intensity on cortical oscillations.

Human-machine interfaces (HMIs) have been widely integrated with motor rehabilitation and augmentation systems. Forecasting movement transitions during human-robot interaction is crucial to ensure system safety, intuitiveness, and reactivity, particularly in anticipating human motor intentions under sudden perturbations or emergency scenarios. In this study, we investigated pre-movement neural signatures preceding sudden movement transitions during ongoing bimanual tasks. Informed by these findings, we propose a physiology-informed EEG Transformer (PI-EEGformer) for EEG-based motor intention recognition. An EEG dataset collected from a bimanual movement task, where one hand was required to switch motor states in response to unexpected cues, was used to evaluate the performance of the PI-EEGformer in comparison with seven state-of-the-art models. Results showed that, prior to the movement transition, EEG power spectrum decreased, and movement-related cortical potentials (MRCPs) could be accurately extracted from the contralateral motor cortex. PI-EEGformer reached an average accuracy of 0.912 in inter-subject tests and 0.829 in cross-subject tests in detecting movement transitions using EEG from 500 ms to 100 ms prior to the actual movement. This performance was superior to all the state-of-the-art models tested. These results demonstrate that EEG neural signatures can predict sudden movement transitions during ongoing bimanual tasks. The PI-EEGformer, designed with these physiological signatures, can enable accurate prediction of sudden movement transitions. This study will help improve the response of HMI systems to sudden disturbances, contributing to a more realistic HMI system.

Beta oscillations in the sensorimotor cortex are modulated across various stages of motor control, including movement preparation and cancellation, and are known to project to the motor neuron pool and muscles during Go/No-Go tasks. While these oscillations have been linked to motor inhibition, the specificity of their transmission to end-effector muscles during movement cancellation remains unclear. We investigated the selectivity of beta-band activity using a lateralised Go/No-Go paradigm. Participants maintained steady bimanual wrist extensor contractions and either executed a unilateral ballistic wrist extension (Go) or cancelled the planned movement (No-Go). Concurrent electroencephalographic and electromyographic recordings were measured throughout. Successful movement cancellation was characterised by contralateral cortical beta modulation accompanied by stronger and more clearly lateralised peripheral beta synchronisation in the muscle associated with the suppressed movement. Peripheral beta activity exhibited greater magnitude and clearer separability between left- and right-sided conditions than cortical signals. These findings demonstrate effector-specific brain-to-muscle beta transmission during movement cancellation and highlight the potential of peripheral measures to provide enhanced resolution of motor cortical dynamics.