The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity

Differences and Commonalities of Electrical Stimulation Paradigms After Central Paralysis and Amputation

BACKGROUND

Patients with spinal cord injury (SCI) or with severe brain stroke suffer from life-lasting functional and sensory impairments. Other traumatic injuries such as limb loss after an accident or disease also affect motor function and sensory feedback and impair quality of life in those individuals. Invasive and non-invasive functional electrical stimulation (FES) is a well-established method to partially restore function and sensory feedback of paralyzed and phantom limbs. It is also a supporting technology for the rehabilitation of the neuromuscular system and for complementing assistive devices.

METHODS

This work reviews the current state-of-the-art of FES as a technology for restoring function and supporting rehabilitation therapy and assistive devices.

RESULTS

Electrodes, electrical stimulation, use of brain signals for rehabilitation and control, and sensory feedback are covered as parts of the whole. A perspective is given on how clinical and research protocols developed for patients with SCI and brain injuries can be translated to the treatment of patients with an amputation and vice versa. We further elaborate on how motor learning strategies with quantitative electrophysiological and kinematic measurements may help caregivers in the rehabilitation process. Insights from practitioners (collected during a workshop of the IFESS 2025) have been integrated to summarize common needs, open questions, and challenges.

CONCLUSIONS

The information from the literature and from practitioners was integrated to propose the next steps towards establishing common guidelines and measures of FES in clinical practice towards evidence-driven treatment and objective assessments.

Review on brain-computer interface technologies in healthcare

Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials

To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.

EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning

In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variability and reducing BCI inefficiency, which has been estimated to affect 10-50% of the population. This convolutional neural network includes the use of inception modules, residual connections and a design that introduces the symmetry of the brain through the mid-sagittal plane into the network architecture. It is complemented with a data augmentation technique that improves the generalization of the model and with the use of transfer learning across different datasets. We compare EEGSym's performance on inter-subject MI classification with ShallowConvNet, DeepConvNet, EEGNet and EEG-Inception. This comparison is performed on 5 publicly available datasets that include left or right hand motor imagery of 280 subjects. This population is the largest that has been evaluated in similar studies to date. EEGSym significantly outperforms the baseline models reaching accuracies of 88.6±9.0 on Physionet, 83.3±9.3 on OpenBMI, 85.1±9.5 on Kaya2018, 87.4±8.0 on Meng2019 and 90.2±6.5 on Stieger2021. At the same time, it allows 95.7% of the tested population (268 out of 280 users) to reach BCI control (≥70% accuracy). Furthermore, these results are achieved using only 16 electrodes of the more than 60 available on some datasets. Our implementation of EEGSym, which includes new advances for EEG processing with DL, outperforms previous state-of-the-art approaches on inter-subject MI classification.

Associative cued asynchronous BCI induces cortical plasticity in stroke patients

OBJECTIVE

We propose a novel cue-based asynchronous brain-computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways.

METHODS

The proposed BCI detects the intention to move from single-trial EEG signals in real time, but, contrary to classic asynchronous-BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue-based asynchronous-BCI was compared with two traditional BCI modes (asynchronous-BCI and offline synchronous-BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor-evoked potentials in the tibialis-anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention.

RESULTS

The proposed cue-based asynchronous-BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous-BCI. Linear-mixed-models showed that motor-evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue-based asynchronous-BCI resulted in the largest relative increase in peak-to-peak motor-evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%).

INTERPRETATION

These findings prove the high performance of a newly proposed cue-based asynchronous-BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.

Directional Decoding From EEG in a Center-Out Motor Imagery Task With Visual and Vibrotactile Guidance

Motor imagery is a popular technique employed as a motor rehabilitation tool, or to control assistive devices to substitute lost motor function. In both said areas of application, artificial somatosensory input helps to mirror the sensorimotor loop by providing kinesthetic feedback or guidance in a more intuitive fashion than via visual input. In this work, we study directional and movement-related information in electroencephalographic signals acquired during a visually guided center-out motor imagery task in two conditions, i.e., with and without additional somatosensory input in the form of vibrotactile guidance. Imagined movements to the right and forward could be discriminated in low-frequency electroencephalographic amplitudes with group level peak accuracies of 70% with vibrotactile guidance, and 67% without vibrotactile guidance. The peak accuracies with and without vibrotactile guidance were not significantly different. Furthermore, the motor imagery could be classified against a resting baseline with group level accuracies between 76 and 83%, using either low-frequency amplitude features or μ and β power spectral features. On average, accuracies were higher with vibrotactile guidance, while this difference was only significant in the latter set of features. Our findings suggest that directional information in low-frequency electroencephalographic amplitudes is retained in the presence of vibrotactile guidance. Moreover, they hint at an enhancing effect on motor-related μ and β spectral features when vibrotactile guidance is provided.

A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery

In this study, we address the issue of whether vibrotactile feedback can enhance the motor cortex excitability translated into the plastic changes in local cortical areas during motor imagery (MI) BCI-based training. For this purpose, we focused on two of the most notable neurophysiological effects of MI - the event-related desynchronization (ERD) level and the increase in cortical excitability assessed with navigated transcranial magnetic stimulation (nTMS). For TMS navigation, we used individual high-resolution 3D brain MRIs. Ten BCI-naive and healthy adults participated in this study. The MI (rest or left/right hand imagery using Graz-BCI paradigm) tasks were performed separately in the presence and absence of feedback. To investigate how much the presence/absence of vibrotactile feedback in MI BCI-based training could contribute to the sensorimotor cortical activations, we compared the MEPs amplitude during MI after training with and without feedback. In addition, the ERD levels during MI BCI-based training were investigated. Our findings provide evidence that applying vibrotactile feedback during MI training leads to (i) an enhancement of the desynchronization level of mu-rhythm EEG patterns over the contralateral motor cortex area corresponding to the MI of the non-dominant hand; (ii) an increase in motor cortical excitability in hand muscle representation corresponding to a muscle engaged by the MI.

Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation

Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.

Investigating the Intervention Parameters of Endogenous Paired Associative Stimulation (ePAS)

Advances in our understanding of neural plasticity have prompted the emergence of neuromodulatory interventions, which modulate corticomotor excitability (CME) and hold potential for accelerating stroke recovery. Endogenous paired associative stimulation (ePAS) involves the repeated pairing of a single pulse of peripheral electrical stimulation (PES) with endogenous movement-related cortical potentials (MRCPs), which are derived from electroencephalography. However, little is known about the optimal parameters for its delivery. A factorial design with repeated measures delivered four different versions of ePAS, in which PES intensities and movement type were manipulated. Linear mixed models were employed to assess interaction effects between PES intensity (suprathreshold (Hi) and motor threshold (Lo)) and movement type (Voluntary and Imagined) on CME. ePAS interventions significantly increased CME compared to control interventions, except in the case of Lo-Voluntary ePAS. There was an overall main effect for the Hi-Voluntary ePAS intervention immediately post-intervention (p = 0.002), with a sub-additive interaction effect at 30 min’ post-intervention (p = 0.042). Hi-Imagined and Lo-Imagined ePAS significantly increased CME for 30 min post-intervention (p = 0.038 and p = 0.043 respectively). The effects of the two PES intensities were not significantly different. CME was significantly greater after performing imagined movements, compared to voluntary movements, with motor threshold PES (Lo) 15 min post-intervention (p = 0.012). This study supports previous research investigating Lo-Imagined ePAS and extends those findings by illustrating that ePAS interventions that deliver suprathreshold intensities during voluntary or imagined movements (Hi-Voluntary and Hi-Imagined) also increase CME. Importantly, our findings indicate that stimulation intensity and movement type interact in ePAS interventions. Factorial designs are an efficient way to explore the effects of manipulating the parameters of neuromodulatory interventions. Further research is required to ensure that these parameters are appropriately refined to maximise intervention efficacy for people with stroke and to support translation into clinical practice.

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain-computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

Can Operant Conditioning of EMG-Evoked Responses Help to Target Corticospinal Plasticity for Improving Motor Function in People With Multiple Sclerosis?

Corticospinal pathway and its function are essential in motor control and motor rehabilitation. Multiple sclerosis (MS) causes damage to the brain and descending connections, and often diminishes corticospinal function. In people with MS, neural plasticity is available, although it does not necessarily remain stable over the course of disease progress. Thus, inducing plasticity to the corticospinal pathway so as to improve its function may lead to motor control improvements, which impact one's mobility, health, and wellness. In order to harness plasticity in people with MS, over the past two decades, non-invasive brain stimulation techniques have been examined for addressing common symptoms, such as cognitive deficits, fatigue, and spasticity. While these methods appear promising, when it comes to motor rehabilitation, just inducing plasticity or having a capacity for it does not guarantee generation of better motor functions. Targeting plasticity to a key pathway, such as the corticospinal pathway, could change what limits one's motor control and improve function. One of such neural training methods is operant conditioning of the motor-evoked potential that aims to train the behavior of the corticospinal-motoneuron pathway. Through up-conditioning training, the person learns to produce the rewarded neuronal behavior/state of increased corticospinal excitability, and through iterative training, the rewarded behavior/state becomes one's habitual, daily motor behavior. This minireview introduces operant conditioning approach for people with MS. Guiding beneficial CNS plasticity on top of continuous disease progress may help to prolong the duration of maintained motor function and quality of life in people living with MS.

Intra- and Inter-Rater Reliability of Manual Feature Extraction Methods in Movement Related Cortical Potential Analysis

Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and inter-rater reliability were evaluated in five electroencephalography (EEG) experts who labelled the peak negativity of averaged movement related cortical potentials (MRCPs) derived from thirty datasets. Each dataset contained 50 MRCP epochs from healthy people performing cued voluntary or imagined movement, or people with stroke performing cued voluntary movement. Reliability was assessed using the intraclass correlation coefficient and standard error of measurement. Excellent intra- and inter-rater reliability was demonstrated in the voluntary movement conditions in healthy people and people with stroke. In comparison reliability in the imagined condition was low to moderate. Post-hoc secondary epoch analysis revealed that the morphology of the signal contributed to the consistency of epoch inclusion; potentially explaining the differences in reliability seen across conditions. Findings from this study may inform future research focused on developing automated labelling methods for ERP feature extraction and call to the wider community of researchers interested in utilizing ERPs as a measure of neurophysiological change or in the delivery of EEG-driven interventions.

Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training

Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.

Neuromuscular electrical stimulation-promoted plasticity of the human brain

The application of neuromuscular electrical stimulation (NMES) to paretic limbs has demonstrated utility for motor rehabilitation following brain injury. When NMES is delivered to a mixed peripheral nerve, typically both efferent and afferent fibres are recruited. Muscle contractions brought about by the excitation of motor neurons are often used to compensate for disability by assisting actions such as the formation of hand aperture, or by preventing others including foot drop. In this context, exogenous stimulation provides a direct substitute for endogenous neural drive. The goal of the present narrative review is to describe the means through which NMES may also promote sustained adaptations within central motor pathways, leading ultimately to increases in (intrinsic) functional capacity. There is an obvious practical motivation, in that detailed knowledge concerning the mechanisms of adaptation has the potential to inform neurorehabilitation practice. In addition, responses to NMES provide a means of studying CNS plasticity at a systems level in humans. We summarize the fundamental aspects of NMES, focusing on the forms that are employed most commonly in clinical and experimental practice. Specific attention is devoted to adjuvant techniques that further promote adaptive responses to NMES thereby offering the prospect of increased therapeutic potential. The emergent theme is that an association with centrally initiated neural activity, whether this is generated in the context of NMES triggered by efferent drive or via indirect methods such as mental imagery, may in some circumstances promote the physiological changes that can be induced through peripheral electrical stimulation.

The effect of surface electromyography biofeedback on the activity of extensor and dorsiflexor muscles in elderly adults: a randomized trial

Surface electromyography-biofeedback (sEMG-B) is a technique employed for the rehabilitation of patients with neurological pathologies, such as stroke-derived hemiplegia; however, little is known about its effectiveness in the rehabilitation of the extension and flexion of several muscular groups in elderly patients after a stroke. Therefore, this research was focused on determining the effectiveness of sEMG-B in the muscles responsible for the extension of the hand and the dorsiflexion of the foot in post-stroke elderly subjects. Forty subjects with stroke-derived hemiplegia were randomly divided into intervention or control groups. The intervention consisted of 12 sEMG-B sessions. The control group underwent 12 weeks (24 sessions) of conventional physiotherapy. Muscle activity test and functionality (Barthel index) were determined. Attending to the results obtained, the intervention group showed a higher increase in the average EMG activity of the extensor muscle of the hand and in the dorsal flexion of the foot than the control group (p < 0.001 in both cases), which was associated with an increase in the patients' Barthel index score (p = 0.006); In addition, Fugl-Meyer test revealed higher effectiveness in the lower limb (p = 0.007). Thus, the sEMG-B seems to be more effective than conventional physiotherapy, and the use of this technology may be essential for improving muscular disorders in elderly patients with physical disabilities resulting from a stroke.

Self-Paced Online vs. Cue-Based Offline Brain-Computer Interfaces for Inducing Neural Plasticity

: Brain-computer interfaces (BCIs), operated in a cue-based (offline) or self-paced (online) mode, can be used for inducing cortical plasticity for stroke rehabilitation by the pairing of movement-related brain activity with peripheral electrical stimulation. The aim of this study was to compare the difference in cortical plasticity induced by the two BCI modes. Fifteen healthy participants participated in two experimental sessions: cue-based BCI and self-paced BCI. In both sessions, imagined dorsiflexions were extracted from continuous electroencephalogram (EEG) and paired 50 times with the electrical stimulation of the common peroneal nerve. Before, immediately after, and 30 minutes after each intervention, the cortical excitability was measured through the motor-evoked potentials (MEPs) of tibialis anterior elicited through transcranial magnetic stimulation. Linear mixed regression models showed that the MEP amplitudes increased significantly (p < 0.05) from pre- to post- and 30-minutes post-intervention in terms of both the absolute and relative units, regardless of the intervention type. Compared to pre-interventions, the absolute MEP size increased by 79% in post- and 68% in 30-minutes post-intervention in the self-paced mode (with a true positive rate of ~75%), and by 37% in post- and 55% in 30-minutes post-intervention in the cue-based mode. The two modes were significantly different (p = 0.03) at post-intervention (relative units) but were similar at both post timepoints (absolute units). These findings suggest that immediate changes in cortical excitability may have implications for stroke rehabilitation, where it could be used as a priming protocol in conjunction with another intervention; however, the findings need to be validated in studies involving stroke patients.

To jump or not to jump - The Bereitschaftspotential required to jump into 192-meter abyss

Self-initiated voluntary acts, such as pressing a button, are preceded by a surface-negative electrical brain potential, the Bereitschaftspotential (BP), that can be recorded over the human scalp using electroencephalography (EEG). While the BP's early component (BP1, generated in the supplementary and cingulate motor area) was linked to motivational, intentional and timing properties, the BP's late component (BP2, generated in the primary motor cortex) was found to be linked to motor execution and performance. Up to now, the BP required to initiate voluntary acts has only been recorded under well-controlled laboratory conditions, and it was unknown whether possible life-threatening decision making, e.g. required to jump into a 192-meter abyss, would impact this form of brain activity. Here we document for the first time pre-movement brain activity preceding 192-meter bungee jumping. We found that the BP's spatiotemporal dynamics reflected by BP1 and BP2 are comparable before 192-meter bungee jumping and jumping from 1-meter. These results, possible through recent advancements in wireless and portable EEG technology, suggest that possible life-threatening decision-making has no impact on the BP's spatiotemporal dynamics.

Investigation of Optimal Afferent Feedback Modality for Inducing Neural Plasticity with A Self-Paced Brain-Computer Interface

Brain-computer interfaces (BCIs) can be used to induce neural plasticity in the human nervous system by pairing motor cortical activity with relevant afferent feedback, which can be used in neurorehabilitation. The aim of this study was to identify the optimal type or combination of afferent feedback modalities to increase cortical excitability in a BCI training intervention. In three experimental sessions, 12 healthy participants imagined a dorsiflexion that was decoded by a BCI which activated relevant afferent feedback: (1) electrical nerve stimulation (ES) (peroneal nerve-innervating tibialis anterior), (2) passive movement (PM) of the ankle joint, or (3) combined electrical stimulation and passive movement (Comb). The cortical excitability was assessed with transcranial magnetic stimulation determining motor evoked potentials (MEPs) in tibialis anterior before, immediately after and 30 min after the BCI training. Linear mixed regression models were used to assess the changes in MEPs. The three interventions led to a significant (p < 0.05) increase in MEP amplitudes immediately and 30 min after the training. The effect sizes of Comb paradigm were larger than ES and PM, although, these differences were not statistically significant (p > 0.05). These results indicate that the timing of movement imagery and afferent feedback is the main determinant of induced cortical plasticity whereas the specific type of feedback has a moderate impact. These findings can be important for the translation of such a BCI protocol to the clinical practice where by combining the BCI with the already available equipment cortical plasticity can be effectively induced. The findings in the current study need to be validated in stroke populations.

Comparison of the Efficacy of a Real-Time and Offline Associative Brain-Computer-Interface

An associative brain-computer-interface (BCI) that correlates in time a peripherally generated afferent volley with the peak negativity (PN) of the movement related cortical potential (MRCP) induces plastic changes in the human motor cortex. However, in this associative BCI the movement timed to a cue is not detected in real time. Thus, possible changes in reaction time caused by factors such as attention shifts or fatigue will lead to a decreased accuracy, less pairings, and likely reduced plasticity. The aim of the current study was to compare the effectiveness of this associative BCI intervention on plasticity induction when the MRCP PN time is pre-determined from a training data set (BCI_offline), or detected online (BCI_online). Ten healthy participants completed both interventions in randomized order. The average detection accuracy for the BCI_online intervention was 71 ± 3% with 2.8 ± 0.7 min^-1 false detections. For the BCI_online intervention the PN did not differ significantly between the training set and the actual intervention (t₉ = 0.87, p = 0.41). The peak-to-peak motor evoked potentials (MEPs) were quantified prior to, immediately following, and 30 min after the cessation of each intervention. MEP results revealed a significant main effect of time, F_(2,18) = 4.46, p = 0.027. The mean TA MEP amplitudes were significantly larger 30 min after (277 ± 72 μV) the BCI interventions compared to pre-intervention MEPs (233 ± 64 μV) regardless of intervention type and stimulation intensity (p = 0.029). These results provide further strong support for the associative nature of the associative BCI but also suggest that they likely differ to the associative long-term potentiation protocol they were modeled on in the exact sites of plasticity.

Motor imagery training induces changes in brain neural networks in stroke patients

Motor imagery is the mental representation of an action without overt movement or muscle activation. However, the effects of motor imagery on stroke-induced hand dysfunction and brain neural networks are still unknown. We conducted a randomized controlled trial in the China Rehabilitation Research Center. Twenty stroke patients, including 13 males and 7 females, 32–51 years old, were recruited and randomly assigned to the traditional rehabilitation treatment group (PP group, n = 10) or the motor imagery training combined with traditional rehabilitation treatment group (MP group, n = 10). All patients received rehabilitation training once a day, 45 minutes per session, five times per week, for 4 consecutive weeks. In the MP group, motor imagery training was performed for 45 minutes after traditional rehabilitation training, daily. Action Research Arm Test and the Fugl-Meyer Assessment of the upper extremity were used to evaluate hand functions before and after treatment. Transcranial magnetic stimulation was used to analyze motor evoked potentials in the affected extremity. Diffusion tensor imaging was used to assess changes in brain neural networks. Compared with the PP group, the MP group showed better recovery of hand function, higher amplitude of the motor evoked potential in the abductor pollicis brevis, greater fractional anisotropy of the right dorsal pathway, and an increase in the fractional anisotropy of the bilateral dorsal pathway. Our findings indicate that 4 weeks of motor imagery training combined with traditional rehabilitation treatment improves hand function in stroke patients by enhancing the dorsal pathway. This trial has been registered with the Chinese Clinical Trial Registry (registration number: ChiCTR-OCH-12002238).