High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports

Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches

The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.

Challenges of neural interfaces for stroke motor rehabilitation

More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.

Neural Suppression Elicited During Motor Imagery Following the Observation of Biological Motion From Point-Light Walker Stimuli

Introduction: Advantageous effects of biological motion (BM) detection, a low-perceptual mechanism that allows the rapid recognition and understanding of spatiotemporal characteristics of movement via salient kinematics information, can be amplified when combined with motor imagery (MI), i.e., the mental simulation of motor acts. According to Jeannerod's neurostimulation theory, asynchronous firing and reduction of mu and beta rhythm oscillations, referred to as suppression over the sensorimotor area, are sensitive to both MI and action observation (AO) of BM. Yet, not many studies investigated the use of BM stimuli using combined AO-MI tasks. In this study, we assessed the neural response in the form of event-related synchronization and desynchronization (ERD/S) patterns following the observation of point-light-walkers and concordant MI, as compared to MI alone. Methods: Twenty right-handed healthy participants accomplished the experimental task by observing BM stimuli and subsequently performing the same movement using kinesthetic MI (walking, cycling, and jumping conditions). We recorded an electroencephalogram (EEG) with 32 channels and performed time-frequency analysis on alpha (8-13 Hz) and beta (18-24 Hz) frequency bands during the MI task. A two-way repeated-measures ANOVA was performed to test statistical significance among conditions and electrodes of interest. Results: The results revealed significant ERD/S patterns in the alpha frequency band between conditions and electrode positions. Post hoc comparisons showed significant differences between condition 1 (walking) and condition 3 (jumping) over the left primary motor cortex. For the beta band, a significantly less difference in ERD patterns (p < 0.01) was detected only between condition 3 (jumping) and condition 4 (reference). Discussion: Our results confirmed that the observation of BM combined with MI elicits a neural suppression, although just in the case of jumping. This is in line with previous findings of AO and MI (AOMI) eliciting a neural suppression for simulated whole-body movements. In the last years, increasing evidence started to support the integration of AOMI training as an adjuvant neurorehabilitation tool in Parkinson's disease (PD). Conclusion: We concluded that using BM stimuli in AOMI training could be promising, as it promotes attention to kinematic features and imitative motor learning.

Brain Connectivity Changes During Bimanual and Rotated Motor Imagery

Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients’ impairment.

The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study

Objective.In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas.Approach.We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas.Main results.Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task.Significance.To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.

EEG miniaturization limits for stimulus decoding with EEG sensor networks

Objective. Unobtrusive electroencephalography (EEG) monitoring in everyday life requires the availability of highly miniaturized EEG devices (mini-EEGs), which ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. By attaching a multitude of mini-EEGs at relevant positions on the scalp, a wireless 'EEG sensor network' (WESN) can be formed. However, each mini-EEG in the network only has access to its own local electrodes, thereby recording local scalp potentials with short inter-electrode distances. This is unlike using traditional cap-EEG, which by the virtue of re-referencing can measure EEG across arbitrarily large distances on the scalp. We evaluate the implications and limitations of such far-driven miniaturization on neural decoding performance.Approach. We collected 255-channel EEG data in an auditory attention decoding (AAD) task. As opposed to previous studies with a lower channel density, this new high-density dataset allows emulation of mini-EEGs with inter-electrode distances down to 1 cm in order to identify and quantify the lower bound on miniaturization for EEG-based stimulus decoding.Main results. We demonstrate that the performance remains reasonably stable for inter-electrode distances down to 3 cm, but decreases quickly for shorter distances if the mini-EEG nodes can be placed at optimal scalp locations and orientations selected by a data-driven algorithm.Significance. The results indicate the potential for the use of mini-EEGs in a WESN context for AAD applications and provide guidance on inter-electrode distances while designing such devices for neuro-steered hearing devices.

The Impact of Context on EEG Motor Imagery Neurofeedback and Related Motor Domains

Neurofeedback (NF) is a versatile non-invasive neuromodulation technique. In combination with motor imagery (MI), NF has considerable potential for enhancing motor performance or supplementing motor rehabilitation. However, not all users achieve reliable NF control. While research has focused on various brain signal properties and the optimisation of signal processing to solve this issue, the impact of context, i.e. the conditions in which NF motor tasks occur, is comparatively unknown. We review current research on the impact of context on MI NF and related motor domains. We identify long-term factors that act at the level of the individual or of the intervention, and short-term factors, with levels before/after and during a session. The reviewed literature indicates that context plays a significant role. We propose considering context factors as well as within-level and across-level interactions when studying MI NF.

Development of a combined, sequential real-time fMRI and fNIRS neurofeedback system to enhance motor learning after stroke

BACKGROUND

After stroke, wrist extension dyscoordination precludes functional arm/hand. We developed a more spatially precise brain signal for use in brain computer interface (BCI's) for stroke survivors.

NEW METHOD

Combination BCI protocol of real-time functional magnetic resonance imaging (rt-fMRI) sequentially followed by functional near infrared spectroscopy (rt-fNIRS) neurofeedback, interleaved with motor learning sessions without neural feedback. Custom Matlab and Python code was developed to provide rt-fNIRS-based feedback to the chronic stroke survivor, system user.

RESULTS

The user achieved a maximum of 71 % brain signal accuracy during rt-fNIRS neural training; progressive focus of brain activation across rt-fMRI neural training; increasing trend of brain signal amplitude during wrist extension across rt-fNIRS training; and clinically significant recovery of arm coordination and active wrist extension.

COMPARISON WITH EXISTING METHODS

Neurorehabilitation, peripherally directed, shows limited efficacy, as do EEG-based BCIs, for motor recovery of moderate/severely impaired stroke survivors. EEG-based BCIs are based on electrophysiological signal; whereas, rt-fMRI and rt-fNIRS are based on neurovascular signal.

CONCLUSION

The system functioned well during user testing. Methods are detailed for others' use. The system user successfully engaged rt-fMRI and rt-fNIRS neurofeedback systems, modulated brain signal during rt-fMRI and rt-fNIRS training, according to volume of brain activation and intensity of signal, respectively, and clinically significantly improved limb coordination and active wrist extension. fNIRS use in this case demonstrates a feasible/practical BCI system for further study with regard to use in chronic stroke rehab, and fMRI worked in concept, but cost and some patient-use issues make it less feasible for clinical practice.

On the (in)efficacy of motor imagery training without feedback and event-related desynchronizations considerations

Motor imagery (MI) constitutes a recurrent strategy for signals generation in brain-computer interfaces (BCIs) - systems that aim to control external devices by directly associating brain responses to distinct commands. Although great improvement has been achieved in MI-BCIs performance over recent years, they still suffer from inter- and intra-subject variability issues. As an attempt to cope with this, some studies have suggested that MI training should aid users to appropriately modulate their response for BCI usage: generally, this training is performed based on the sensorimotor rhythms' modulation over the primary sensorimotor cortex (PMC), with the signal being feedbacked to the user. Nonetheless, recent studies have revisited the actual involvement of the PMC into MI, and little to no attention has been devoted to understanding the participation of other cortical areas into training protocols. Therefore, in this work, our aim was to analyze the response induced by hands MI of 10 healthy subjects in the form of event-related desynchronizations (ERDs) and to assess whether features from beyond the PMC might be useful for hands MI classification. We investigated how this response occurs for distinct frequency intervals between 7-30 Hz, and ex0plored changes in their evocation pattern across 12 MI training sessions without feedback. Overall, we found that ERD patterns occur differently for the frequencies encompassed by the μ and β bands, with its evocation being favored for the first band. Over time, the no-feedback approach was inefficient to aid in enhancing ERD evocation (EO). Moreover, to some extent, EO tends to decrease over blocks within a given run, and runs within an MI session, but remains stable within an MI block. We also found that the C3/C4 pair is not necessarily optimal for data classification, and both spectral and spatial subjects' specificities should be considered when designing training protocols.

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback

Optimizing neurofeedback (NF) and brain–computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user’s control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8–30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants’ MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.

A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients

Traditional rehabilitation techniques present limitations and the majority of patients show poor 1-year post-stroke recovery. Thus, Neurofeedback (NF) or Brain-Computer-Interface applications for stroke rehabilitation purposes are gaining increased attention. Indeed, NF has the potential to enhance volitional control of targeted cortical areas and thus impact on motor function recovery. However, current implementations are limited by temporal, spatial or practical constraints of the specific imaging modality used. In this pilot work and for the first time in literature, we applied bimodal EEG-fMRI NF for upper limb stroke recovery on four stroke-patients with different stroke characteristics and motor impairment severity. We also propose a novel, multi-target training approach that guides the training towards the activation of the ipsilesional primary motor cortex. In addition to fMRI and EEG outcomes, we assess the integrity of the corticospinal tract (CST) with tractography. Preliminary results suggest the feasibility of our approach and show its potential to induce an augmented activation of ipsilesional motor areas, depending on the severity of the stroke deficit. Only the two patients with a preserved CST and subcortical lesions succeeded in upregulating the ipsilesional primary motor cortex and exhibited a functional improvement of upper limb motricity. These findings highlight the importance of taking into account the variability of the stroke patients' population and enabled to identify inclusion criteria for the design of future clinical studies.

Investigating Priming Effects of Physical Practice on Motor Imagery-Induced Event-Related Desynchronization

For motor imagery (MI) to be effective, an internal representation of the to-be-imagined movement may be required. A representation can be achieved through prior motor execution (ME), but the neural correlates of MI that are primed by ME practice are currently unknown. In this study, young healthy adults performed MI practice of a unimanual visuo-motor task (Group MI, n = 19) or ME practice combined with subsequent MI practice (Group ME&MI, n = 18) while electroencephalography (EEG) was recorded. Data analysis focused on the MI-induced event-related desynchronization (ERD). Specifically, changes in the ERD and movement times (MT) between a short familiarization block of ME (Block pre-ME), conducted before the MI or the ME combined with MI practice phase, and a short block of ME conducted after the practice phase (Block post-ME) were analyzed. Neither priming effects of ME practice on MI-induced ERD were found nor performance-enhancing effects of MI practice in general. We found enhancements of the ERD and MT in Block post-ME compared to Block pre-ME, but only for Group ME&MI. A comparison of ME performance measures before and after the MI phase indicated however that these changes could not be attributed to the combination of ME and MI practice. The mixed results of this study may be a consequence of the considerable intra- and inter-individual differences in the ERD, introduced by specifics of the experimental setup, in particular the individual and variable task duration, and suggest that task and experimental setup can affect the interplay of ME and MI.

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Neurofeedback-guided motor-imagery training (NF-MIT) has been proposed as a promising intervention following upper limb motor impairment. In this intervention, paretic stroke patients receive online feedback about their brain activity while conducting a motor-imagery (MI) task with the paretic limb. Typically, the feedback provided in NF-MIT protocols is an abstract visual signal based on a fixed trial. Here we developed a self-paced NF-MIT paradigm with an embodiable feedback signal (EFS), which was designed to resemble the content of the mental act as closely as possible. To this end, the feedback was delivered via an embodiable, anthropomorphic robotic hand (RH), which was integrated into a closed-looped EEG-based brain-computer interface (BCI). Whenever the BCI identified a new instance of a hand-flexion or hand-extension imagination by the participant, the RH carried out the corresponding movement with minimum delay. Nine stroke patients and nine healthy participants were instructed to control RH movements as accurately as possible, using mental activity alone. We evaluated the general feasibility of our paradigm on electrophysiological, subjective and performance levels. Regarding electrophysiological measures, individuals showed the predicted event-related desynchronization (ERD) patterns over sensorimotor brain areas. On the subjective level, we found that most individuals integrated the RH into their body scheme. With respect to RH control, none of our participants achieved a high level of control, but most managed to control the RH actions to some degree. Importantly, patients and controls achieved similar performance levels. The results support the view that self-paced embodiable NF-MIT is feasible for stroke patients and can complement classical NF-MIT.

Cortical Activations and BCI Performances at Different Speeds of Visual and Proprioceptive Stimulation

Motor imagery based brain-computer interface (MI-BCI) is one of the most common paradigms utilized in neurofeedback training (NFT) for rehabilitation engineering. Specifically, finding an appropriate feedback protocol is significantly important to improve the effectiveness of the motor training system. To this end, we investigated the electroencephalography(EEG) oscillatory patterns measured by event-related desynchronization (ERD) when sixteen participants accepted the visual and proprioceptive stimulation achieving the kinematic hand grasping movements at three different speeds (i.e. 1/3 Hz, 2/3 Hz and 1 Hz). The EEG results indicated that the ERD patterns showed no significant difference in sensorimotor cortex (i.e. C3 and FC3 channels) by comparing the three conditions. Nevertheless, the 2/3 Hz stimulation speed could achieve a significantly better classification performance than the other two conditions across all participants. Therefore, the visual and proprioceptive electrical stimulation achieving the kinematic hand grasping at 2/3 Hz speed might provide an available approach for the online MI-BCI system based NFT system in the future.

Does Fractional Anisotropy Predict Motor Imagery Neurofeedback Performance in Healthy Older Adults?

Motor imagery neurofeedback training has been proposed as a potential add-on therapy for motor impairment after stroke, but not everyone benefits from it. Previous work has used white matter integrity to predict motor imagery neurofeedback aptitude in healthy young adults. We set out to test this approach with motor imagery neurofeedback that is closer to that used for stroke rehabilitation and in a sample whose age is closer to that of typical stroke patients. Using shrinkage linear discriminant analysis with fractional anisotropy values in 48 white matter regions as predictors, we predicted whether each participant in a sample of 21 healthy older adults (48–77 years old) was a good or a bad performer with 84.8% accuracy. However, the regions used for prediction in our sample differed from those identified previously, and previously suggested regions did not yield significant prediction in our sample. Including demographic and cognitive variables which may correlate with motor imagery neurofeedback performance and white matter structure as candidate predictors revealed an association with age but also led to loss of statistical significance and somewhat poorer prediction accuracy (69.6%). Our results suggest cast doubt on the feasibility of predicting the benefit of motor imagery neurofeedback from fractional anisotropy. At the very least, such predictions should be based on data collected using the same paradigm and with subjects whose characteristics match those of the target case as closely as possible.

EEG neurofeedback research: A fertile ground for psychiatry?

The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human-computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed: Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human-computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human-computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.

Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges

Mobile electroencephalography (mobile EEG) represents a next-generation neuroscientific technology – to study real-time brain activity – that is relatively inexpensive, non-invasive and portable. Mobile EEG leverages state-of-the-art hardware alongside established advantages of traditional EEG and recent advances in signal processing. In this review, we propose that mobile EEG could open unprecedented possibilities for studying neurodevelopmental disorders. We first present a brief overview of recent developments in mobile EEG technologies, emphasising the proliferation of studies in several neuroscientific domains. As these developments have yet to be exploited by neurodevelopmentalists, we then identify three research opportunities: 1) increase in the ease and flexibility of brain data acquisition in neurodevelopmental populations; 2) integration into powerful developmentally-informative research designs; 3) development of innovative non-stationary EEG-based paradigms. Critically, we address key challenges that should be considered to fully realise the potential of mobile EEG for neurodevelopmental research and for understanding developmental psychopathology more broadly, and suggest future research directions.

Behavioral Outcomes Following Brain-Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial

Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain-computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as "responders" (<1 = "non-responders") and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotal score at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p = 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors. Clinical Trial Registration: ClinicalTrials.gov, NCT02098265.

Reactivating the Dormant Motor Cortex After Spinal Cord Injury With EEG Neurofeedback: A Case Study With a Chronic, Complete C4 Patient

Chronic spinal cord injury (SCI) patients present poor motor cortex activation during movement attempts. The reactivation of this brain region can be beneficial for them, for instance, allowing them to use brain-machine interfaces for motor rehabilitation or restoration. These brain-machine interfacess generally use electroencephalography (EEG) to measure the cortical activation during the attempts of movement, quantifying it as the event-related desynchronization (ERD) of the alpha/mu rhythm. Based on previous evidence showing that higher tonic EEG alpha power is associated with higher ERD, we hypothesized that artificially increasing the alpha power over the motor cortex of these patients could enhance their ERD (ie, motor cortical activation) during movement attempts. We used EEG neurofeedback (NF) to enhance the tonic EEG alpha power, providing real-time visual feedback of the alpha oscillations measured over the motor cortex. This approach was evaluated in a C4, ASIA A, SCI patient (9 months after the injury) who did not present ERD during the movement attempts of his paralyzed hands. The patient performed 4 NF sessions (in 4 consecutive days) and screenings of his EEG activity before and after each session. After the intervention, the patient presented a significant increase in the alpha power over the motor cortex, and a significant enhancement of the mu ERD in the contralateral motor cortex when he attempted to close the assessed right hand. As a proof of concept investigation, this article shows how a short NF intervention might be used to enhance the motor cortical activation in patients with chronic tetraplegia.

Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis

Brain‐computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta‐analysis evaluating the clinical effectiveness of BCI‐based post‐stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post‐stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random‐effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post‐stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta‐analysis. Motor improvements, mostly quantified by the upper limb Fugl‐Meyer Assessment (FMA‐UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA‐UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI‐induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post‐stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

Modulating hemispheric lateralization by brain stimulation yields gain in mental and physical activity

Imagery plays an important role in our life. Motor imagery is the mental simulation of a motor act without overt motor output. Previous studies have documented the effect of motor imagery practice. However, its translational potential for patients as well as for athletes, musicians and other groups, depends largely on the transfer from mental practice to overt physical performance. We used bilateral transcranial direct current stimulation (tDCS) over sensorimotor areas to modulate neural lateralization patterns induced by unilateral mental motor imagery and the performance of a physical motor task. Twenty-six healthy older adults participated (mean age = 67.1 years) in a double-blind cross-over sham-controlled study. We found stimulation-related changes at the neural and behavioural level, which were polarity-dependent. Specifically, for the hand contralateral to the anode, electroencephalographic activity induced by motor imagery was more lateralized and motor performance improved. In contrast, for the hand contralateral to the cathode, hemispheric lateralization was reduced. The stimulation-related increase and decrease in neural lateralization were negatively related. Further, the degree of stimulation-related change in neural lateralization correlated with the stimulation-related change on behavioural level. These convergent neurophysiological and behavioural effects underline the potential of tDCS to improve mental and physical motor performance.