Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality

Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation

Objective. Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) could excite the central nervous system to enhance upper limb motor recovery. Our current study assessed the effectiveness of motor and prefrontal cortical activity-based BCI-FES to help elucidate the underlying neuromodulation mechanisms of this neurorehabilitation approach.Approach. The primary motor cortex (M1) and prefrontal cortex (PFC) BCI-FES interventions were performed for 25 min on separate days with twelve non-disabled participants. During the interventions, a single electrode from the contralateral M1 or PFC was used to detect event-related desynchronization (ERD) in the calibrated frequency range. If the BCI system detected ERD within 15 s of motor imagery, FES activated wrist extensor muscles. Otherwise, if the BCI system did not detect ERD within 15 s, a subsequent trial was initiated without FES. To evaluate neuromodulation effects, corticospinal excitability was assessed using single-pulse transcranial magnetic stimulation, and cortical excitability was assessed by motor imagery ERD and resting-state functional connectivity before, immediately, 30 min, and 60 min after each intervention.Main results. M1 and PFC BCI-FES interventions had similar success rates of approximately 80%, while the M1 intervention was faster in detecting ERD activity. Consequently, only the M1 intervention effectively elicited corticospinal excitability changes for at least 60 min around the targeted cortical area in the M1, suggesting a degree of spatial localization. However, cortical excitability measures did not indicate changes after either M1 or PFC BCI-FES.Significance. Neural mechanisms underlying the effectiveness of BCI-FES neuromodulation may be attributed to the M1 direct corticospinal projections and/or the closer timing between ERD detection and FES, which likely enhanced Hebbian-like plasticity by synchronizing cortical activation detected by the BCI system with the sensory nerve activation and movement related reafference elicited by FES.

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

Introduction

Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer.

Methods

Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement.

Results

A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon.

Discussion

We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.

Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients

Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.

Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review

Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.

Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method

Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb. In addition, upper triangle filter bank sparse spatial pattern (UTFB-SSP) is proposed to automatically select the optimal frequency sub-bands related to desynchronization rhythm during enhanced imaginary movement to improve the decoding performance. The effectiveness of the proposed MI-BCI system is demonstrated on an the in-house experimental dataset and the BCI competition IV IIa dataset. The experimental results show that the proposed system can effectively enhance the MI performance by inducing the α, β and γ rhythms in lower-limb movement imagery tasks.

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

Human behavior requires inter-regional crosstalk to employ the sensorimotor processes in the brain. Although external neuromodulation techniques have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We addressed this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable the participants to modulate their bilateral sensorimotor activities. Here, we report that participants learn to up- and down-regulate the ipsilateral excitability to the imagined hand while maintaining constant contralateral excitability; this modulates the magnitude of interhemispheric inhibition (IHI) assessed by the paired-pulse transcranial magnetic stimulation (TMS) paradigm. Further physiological analyses revealed that the manipulation capability of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results show an interesting approach for neuromodulation, which might identify new treatment opportunities, e.g., in patients suffering from a stroke.

Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study

Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated the efficacy of the sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback for post-stroke rehabilitation. Furthermore, we discussed the interaction between training intensity and training duration in BCI training. Twenty-four stroke patients with severe upper limb (UL) motor deficits were randomly assigned to two groups: 2-week SMR-BCI training combined with conventional treatment (BCI Group, BG, n = 12) and 2-week conventional treatment without SMR-BCI intervention (Control Group, CG, n = 12). Motor function was measured using clinical measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Motor Functional Test (WMFT), and Modified Barthel Index (MBI), at baseline (Week 0), post-intervention (Week 2), and follow-up week (Week 4). EEG data from patients allocated to the BG was recorded at Week 0 and Week 2 and quantified by mu suppression means event-related desynchronization (ERD) in mu rhythm (8–12 Hz). All functional assessment scores (FMA-UE, WMFT, and MBI) significantly improved at Week 2 for both groups (p < 0.05). The BG had significantly higher FMA-UE and WMFT improvement at Week 4 compared to the CG. The mu suppression of bilateral hemisphere both had a positive trend with the motor function scores at Week 2. This study proposes a new effective SMR-BCI system and demonstrates that the SMR-BCI training with audio-cue, motor observation and multisensory feedback, together with conventional therapy may promote long-lasting UL motor improvement.

Clinical Trial Registration: [http://www.chictr.org.cn], identifier [ChiCTR2000041119].

The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training

Brain–computer interfaces (BCI) can be designed with several feedback modalities. To promote appropriate brain plasticity in therapeutic applications, the feedback should guide the user to elicit the desired brain activity and preferably be similar to the imagined action. In this study, we employed magnetoencephalography (MEG) to measure neurophysiological changes in healthy subjects performing motor imagery (MI) -based BCI training with two different feedback modalities. The MI-BCI task used in this study lasted 40–60 min and involved imagery of right- or left-hand movements. 8 subjects performed the task with visual and 14 subjects with proprioceptive feedback. We analysed power changes across the session at multiple frequencies in the range of 4–40 Hz with a generalized linear model to find those frequencies at which the power increased significantly during training. In addition, the power increase was analysed for each gradiometer, separately for alpha (8–13 Hz), beta (14–30 Hz) and gamma (30–40 Hz) bands, to find channels showing significant linear power increase over the session. These analyses were applied during three different conditions: rest, preparation, and MI. Visual feedback enhanced the amplitude of mainly high beta and gamma bands (24–40 Hz) in all conditions in occipital and left temporal channels. During proprioceptive feedback, in contrast, power increased mainly in alpha and beta bands. The alpha-band enhancement was found in multiple parietal, occipital, and temporal channels in all conditions, whereas the beta-band increase occurred during rest and preparation mainly in the parieto-occipital region and during MI in the parietal channels above hand motor regions. Our results show that BCI training with proprioceptive feedback increases the power of sensorimotor rhythms in the motor cortex, whereas visual feedback causes mainly a gamma-band increase in the visual cortex. MI-BCIs should involve proprioceptive feedback to facilitate plasticity in the motor cortex.

Brain Network Organization Following Post-Stroke Neurorehabilitation

Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.

A High-Density EEG Study Investigating VR Film Editing and Cognitive Event Segmentation Theory

This paper introduces a cognitive psychological experiment that was conducted to analyze how traditional film editing methods and the application of cognitive event segmentation theory perform in virtual reality (VR). Thirty volunteers were recruited and asked to watch a series of short VR videos designed in three dimensions: time, action (characters), and space. Electroencephalograms (EEG) were recorded simultaneously during their participation. Subjective results show that any of the editing methods used would lead to an increased load and reduced immersion. Furthermore, the cognition of event segmentation theory also plays an instructive role in VR editing, with differences mainly focusing on frontal, parietal, and central regions. On this basis, visual evoked potential (VEP) analysis was performed, and the standardized low-resolution brain electromagnetic tomography algorithm (sLORETA) traceability method was used to analyze the data. The results of the VEP analysis suggest that shearing usually elicits a late event-related potential component, while the sources of VEP are mainly the frontal and parietal lobes. The insights derived from this work can be used as guidance for VR content creation, allowing VR image editing to reveal greater richness and unique beauty.

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.

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.

Workplace Mental State Monitoring during VR-Based Training for Offshore Environment

Adults are constantly exposed to stressful conditions at their workplace, and this can lead to decreased job performance followed by detrimental clinical health problems. Advancement of sensor technologies has allowed the electroencephalography (EEG) devices to be portable and used in real-time to monitor mental health. However, real-time monitoring is not often practical in workplace environments with complex operations such as kindergarten, firefighting and offshore facilities. Integrating the EEG with virtual reality (VR) that emulates workplace conditions can be a tool to assess and monitor mental health of adults within their working environment. This paper evaluates the mental states induced when performing a stressful task in a VR-based offshore environment. The theta, alpha and beta frequency bands are analysed to assess changes in mental states due to physical discomfort, stress and concentration. During the VR trials, mental states of discomfort and disorientation are observed with the drop of theta activity, whilst the stress induced from the conditional tasks is reflected in the changes of low-alpha and high-beta activities. The deflection of frontal alpha asymmetry from negative to positive direction reflects the learning effects from emotion-focus to problem-solving strategies adopted to accomplish the VR task. This study highlights the need for an integrated VR-EEG system in workplace settings as a tool to monitor and assess mental health of working adults.

Peripheral Electrical Stimulation Modulates Cortical Beta-Band Activity

Low-frequency peripheral electrical stimulation using a matrix electrode (PEMS) modulates spinal nociceptive pathways. However, the effects of this intervention on cortical oscillatory activity have not been assessed yet. The aim of this study was to investigate the effects of low-frequency PEMS (4 Hz) on cortical oscillatory activity in different brain states in healthy pain-free participants. In experiment 1, PEMS was compared to sham stimulation. In experiment 2, motor imagery (MI) was used to modulate the sensorimotor brain state. PEMS was applied either during MI-induced oscillatory desynchronization (concurrent PEMS) or after MI (delayed PEMS) in a cross-over design. For both experiments, PEMS was applied on the left forearm and resting-state electroencephalography (EEG) was recording before and after each stimulation condition. Experiment 1 showed a significant decrease of global resting-state beta power after PEMS compared to sham (p = 0.016), with a median change from baseline of −16% for PEMS and −0.54% for sham. A cluster-based permutation test showed a significant difference in resting-state beta power comparing pre- and post-PEMS (p = 0.018) that was most pronounced over bilateral central and left frontal sensors. Experiment 2 did not identify a significant difference in the change from baseline of global EEG power for concurrent PEMS compared to delayed PEMS. Two cluster-based permutation tests suggested that frontal beta power may be increased following both concurrent and delayed PEMS. This study provides novel evidence for supraspinal effects of low-frequency PEMS and an initial indication that the presence of a cognitive task such as MI may influence the effects of PEMS on beta activity. Chronic pain has been associated with changes in beta activity, in particular an increase of beta power in frontal regions. Thus, brain state-dependent PEMS may offer a novel approach to the treatment of chronic pain. However, further studies are warranted to investigate optimal stimulation conditions to achieve a reduction of pain.

Brain State-dependent Gain Modulation of Corticospinal Output in the Active Motor System

The communication through coherence hypothesis suggests that only coherently oscillating neuronal groups can interact effectively and predicts an intrinsic response modulation along the oscillatory rhythm. For the motor cortex (MC) at rest, the oscillatory cycle has been shown to determine the brain's responsiveness to external stimuli. For the active MC, however, the demonstration of such a phase-specific modulation of corticospinal excitability (CSE) along the rhythm cycle is still missing. Motor evoked potentials in response to transcranial magnetic stimulation (TMS) over the MC were used to probe the effect of cortical oscillations on CSE during several motor conditions. A brain-machine interface (BMI) with a robotic hand orthosis allowed investigating effects of cortical activity on CSE without the confounding effects of voluntary muscle activation. Only this BMI approach (and not active or passive hand opening alone) revealed a frequency- and phase-specific cortical modulation of CSE by sensorimotor beta-band activity that peaked once per oscillatory cycle and was independent of muscle activity. The active MC follows an intrinsic response modulation in accordance with the communication through coherence hypothesis. Furthermore, the BMI approach may facilitate and strengthen effective corticospinal communication in a therapeutic context, for example, when voluntary hand opening is no longer possible after stroke.

Characterization of kinesthetic motor imagery compared with visual motor imageries

Motor imagery (MI) is the only way for disabled subjects to robustly use a robot arm with a brain-machine interface. There are two main types of MI. Kinesthetic motor imagery (KMI) is proprioceptive (OR somato-) sensory imagination and Visual motor imagery (VMI) represents a visualization of the corresponding movement incorporating the visual network. Because these imagery tactics may use different networks, we hypothesized that the connectivity measures could characterize the two imageries better than the local activity. Electroencephalography data were recorded. Subjects performed different conditions, including motor execution (ME), KMI, VMI, and visual observation (VO). We tried to classify the KMI and VMI by conventional power analysis and by the connectivity measures. The mean accuracies of the classification of the KMI and VMI were 98.5% and 99.29% by connectivity measures (alpha and beta, respectively), which were higher than those by the normalized power (p < 0.01, Wilcoxon paired rank test). Additionally, the connectivity patterns were correlated between the ME-KMI and between the VO-VMI. The degree centrality (DC) was significantly higher in the left-S1 at the alpha-band in the KMI than in the VMI. The MI could be well classified because the KMI recruits a similar network to the ME. These findings could contribute to MI training methods.

Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review

BACKGROUND

Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective.

METHODS

A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures.

RESULTS

30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery.

CONCLUSION

19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

Oscillatory EEG Signatures of Affective Processes during Interaction with Adaptive Computer Systems

Affect monitoring is being discussed as a novel strategy to make adaptive systems more user-oriented. Basic knowledge about oscillatory processes and functional connectivity underlying affect during naturalistic human-computer interactions (HCI) is, however, scarce. This study assessed local oscillatory power entrainment and distributed functional connectivity in a close-to-naturalistic HCI-paradigm. Sixteen participants interacted with a simulated assistance system which deliberately evoked positive (supporting goal-achievement) and negative (impeding goal-achievement) affective reactions. Electroencephalography (EEG) was used to examine the reactivity of the cortical system during the interaction by studying both event-related (de-)synchronization (ERD/ERS) and event-related functional coupling of cortical networks towards system-initiated assistance. Significantly higher α-band and β-band ERD in centro-parietal and parieto-occipital regions and β-band ERD in bi-lateral fronto-central regions were observed during impeding system behavior. Supportive system behavior activated significantly higher γ-band ERS in bi-hemispheric parietal-occipital regions. This was accompanied by functional coupling of remote β-band and γ-band activity in the medial frontal, left fronto-central and parietal regions, respectively. Our findings identify oscillatory signatures of positive and negative affective processes as reactions to system-initiated assistance. The findings contribute to the development of EEG-based neuroadaptive assistance loops by suggesting a non-obtrusive method for monitoring affect in HCI.

Backward Walking Induces Significantly Larger Upper-Mu-Rhythm Suppression Effects Than Forward Walking Does

Studies have compared the differences and similarities between backward walking and forward walking, and demonstrated the potential of backward walking for gait rehabilitation. However, current evidence supporting the benefits of backward walking over forward walking remains inconclusive. Considering the proven association between gait and the cerebral cortex, we used electroencephalograms (EEG) to differentiate the effects of backward walking and forward walking on cortical activities, by comparing the sensorimotor rhythm (8–12 Hz, also called mu rhythm) of EEG signals. A systematic signal procedure was used to eliminate the motion artifacts induced by walking to safeguard EEG signal fidelity. Statistical test results of our experimental data demonstrated that walking motions significantly suppressed mu rhythm. Moreover, backward walking exhibited significantly larger upper mu rhythm (10–12 Hz) suppression effects than forward walking did. This finding implies that backward walking induces more sensorimotor cortex activity than forward walking does, and provides a basis to support the potential benefits of backward walking over forward walking. By monitoring the upper mu rhythm throughout the rehabilitation process, medical experts can adaptively adjust the intensity and duration of each walking training session to improve the efficacy of a walking ability recovery program.

A review of user training methods in brain computer interfaces based on mental tasks

Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training - notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.

Low Cost MR Compatible Haptic Stimulation with Application to fMRI Neurofeedback

The most common feedback displays in the fMRI environment are visual, e.g., in which participants try to increase or decrease the level of a thermometer. However, haptic feedback is increasingly valued in computer interaction tasks, particularly for real-time fMRI feedback. fMRI-neurofeedback is a clinical intervention that has not yet taken advantage of this trend. Here we describe a low-cost, user-friendly, MR-compatible system that can provide graded haptic vibrotactile stimulation in an initial application to fMRI neurofeedback. We also present a feasibility demonstration showing that we could successfully set up the system and obtain data in the context of a neurofeedback paradigm. We conclude that vibrotactile stimulation using this low-cost system is a viable method of feedback presentation, and encourage neurofeedback researchers to incorporate this type of feedback into their studies.

State-Dependent Gain Modulation of Spinal Motor Output

Afferent somatosensory information plays a crucial role in modulating efferent motor output. A better understanding of this sensorimotor interplay may inform the design of neurorehabilitation interfaces. Current neurotechnological approaches that address motor restoration after trauma or stroke combine motor imagery (MI) and contingent somatosensory feedback, e.g., via peripheral stimulation, to induce corticospinal reorganization. These interventions may, however, change the motor output already at the spinal level dependent on alterations of the afferent input. Neuromuscular electrical stimulation (NMES) was combined with measurements of wrist deflection using a kinematic glove during either MI or rest. We investigated 360 NMES bursts to the right forearm of 12 healthy subjects at two frequencies (30 and 100 Hz) in random order. For each frequency, stimulation was assessed at nine intensities. Measuring the induced wrist deflection across different intensities allowed us to estimate the input-output curve (IOC) of the spinal motor output. MI decreased the slope of the IOC independent of the stimulation frequency. NMES with 100 Hz vs. 30 Hz decreased the threshold of the IOC. Human-machine interfaces for neurorehabilitation that combine MI and NMES need to consider bidirectional communication and may utilize the gain modulation of spinal circuitries by applying low-intensity, high-frequency stimulation.

Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere

Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.

Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?

Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17–21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = −0.027 to −0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.

A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies-Related Issues and Future Directions

Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep's effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with 'sleep and stimulation' and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.

Brain-computer interface performance analysis of monozygotic twins with discordant hand dominance: A case study

Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, performance variations across individuals have limited their use to laboratory environments. Handedness could contribute to these variations, especially when motor imagery (MI) tasks are used for BCI control. To further understand how handedness affects BCI control, performance differences between two monozygotic twins were analysed during offline movement and MI tasks, and while twins controlled a BCI using right-hand MI. Quantitative electroencephalography (qEEG), brain structures' volumes, and neuropsychological tests were assessed to evaluate physiological, anatomical and psychological relationships with BCI performance. Results showed that both twins had good motor imagery and attention abilities, similar volumes on most subcortical brain structures, more pronounced event-related desynchronization elicited by the twin performing non-dominant MI, and that this twin also obtained significant higher performances with the BCI. Linear regression analysis implied a strong association between twins' BCI performance, and more pronounced cortical activations in the contralateral hemisphere relative to hand MI. Therefore, it is possible that BCI performance was related with the ability of each twin to elicit cortical activations during hand MI, and less associated with subcortical brain structures' volumes and neuropsychological tests.

Different oscillatory entrainment of cortical networks during motor imagery and neurofeedback in right and left handers

Volitional modulation and neurofeedback of sensorimotor oscillatory activity is currently being evaluated as a strategy to facilitate motor restoration following stroke. Knowledge on the interplay between this regional brain self-regulation, distributed network entrainment and handedness is, however, limited. In a randomized cross-over design, twenty-one healthy subjects (twelve right-handers [RH], nine left-handers [LH]) performed kinesthetic motor imagery of left (48 trials) and right finger extension (48 trials). A brain-machine interface turned event-related desynchronization in the beta frequency-band (16-22 Hz) during motor imagery into passive hand opening by a robotic orthosis. Thereby, every participant subsequently activated either the dominant (DH) or non-dominant hemisphere (NDH) to control contralateral hand opening. The task-related cortical networks were studied with electroencephalography. The magnitude of the induced oscillatory modulation range in the sensorimotor cortex was independent of both handedness (RH, LH) and hemispheric specialization (DH, NDH). However, the regional beta-band modulation was associated with different alpha-band networks in RH and LH: RH presented a stronger inter-hemispheric connectivity, while LH revealed a stronger intra-hemispheric interaction. Notably, these distinct network entrainments were independent of hemispheric specialization. In healthy subjects, sensorimotor beta-band activity can be robustly modulated by motor imagery and proprioceptive feedback in both hemispheres independent of handedness. However, right and left handers show different oscillatory entrainment of cortical alpha-band networks during neurofeedback. This finding may inform neurofeedback interventions in future to align them more precisely with the underlying physiology.

Combined endogenous and exogenous disinhibition of intracortical circuits augments plasticity induction in the human motor cortex

BACKGROUND

Motor imagery (MI) engages cortical areas in the human brain similar to motor practice. Corticospinal excitability (CSE) is facilitated during but not after MI practice. We hypothesized that lasting CSE changes could be achieved by associatively pairing this endogenous modulation with exogenous stimulation of the same intracortical circuits.

METHODS

We combined MI with a disinhibition protocol (DIS) targeting intracortical circuits by paired-pulse repetitive transcranial magnetic stimulation in one main and three subsequent experiments. The follow-up experiments were applied to increase effects, e.g., by individualizing inter-stimulus intervals, adding neuromuscular stimulation and expanding the intervention period. CSE was captured during (online) and after (offline) the interventions via input-output changes and cortical maps of motor evoked potentials. A total of 35 healthy subjects (mean age 26.1 ± 2.6 years, 20 females) participated in this study.

RESULTS

A short intervention (48 stimuli within ∼90s) increased CSE. This plasticity developed rapidly, was associative (with MI_on, but not MI_off or REST) and persisted beyond the intervention period. Follow-up experiments revealed the relevance of individualizing inter-stimulus intervals and of consistent inter-burst periods for online and offline effects, respectively. Expanding this combined MI/DIS intervention to 480 stimuli amplified the sustainability of CSE changes. When concurrent neuromuscular electrical stimulation was applied, the plasticity induction was cancelled.

CONCLUSIONS

This novel associative stimulation protocol augmented plasticity induction in the human motor cortex within a remarkably short period of time and in the absence of active movements. The combination of endogenous and exogenous disinhibition of intracortical circuits may provide a therapeutic backdoor when active movements are no longer possible, e.g., for hand paralysis after stroke.

Volitional control of single-electrode high gamma local field potentials by people with paralysis

Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user's movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such "biomimetic" BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40-400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW & NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.

Reaction Time Predicts Brain-Computer Interface Aptitude

There is evidence that 15-30% of the general population cannot effectively operate brain-computer interfaces (BCIs). Thus the BCI performance predictors are critically required to pre-screen participants. Current neurophysiological and psychological tests either require complicated equipment or suffer from subjectivity. Thus, a simple and objective BCI performance predictor is desirable. Neurofeedback (NFB) training involves performing a cognitive task (motor imagery) instructed via sensory stimuli and re-adjusted through ongoing real-time feedback. A simple reaction time (SRT) test reflects the time required for a subject to respond to a defined stimulus. Thus, we postulated that individuals with shorter reaction times operate a BCI with rapidly updated feedback better than individuals with longer reaction times. Furthermore, we investigated how changing the feedback update interval (FUI), i.e., modification of the feedback provision frequency, affects the correlation between the SRT and BCI performance. Ten participants attended four NFB sessions with FUIs of 16, 24, 48, and 96 ms in a randomized order. We found that: 1) SRT is correlated with the BCI performance with FUIs of 16 and 96 ms; 2) good and poor performers elicit stronger ERDs and control BCIs more effectively (i.e., produced larger information transfer rates) with 16 and 96 ms FUIs, respectively. Our findings suggest that SRT may be used as a simple and objective surrogate for BCI aptitude with FUIs of 16 and 96 ms. It also implies that the FUI customization according to participants SRT measure may enhance the BCI performance.

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.

Modulating functional connectivity after stroke with neurofeedback: Effect on motor deficits in a controlled cross-over study

Synchronization of neural activity as measured with functional connectivity (FC) is increasingly used to study the neural basis of brain disease and to develop new treatment targets. However, solid evidence for a causal role of FC in disease and therapy is lacking. Here, we manipulated FC of the ipsilesional primary motor cortex in ten chronic human stroke patients through brain-computer interface technology with visual neurofeedback. We conducted a double-blind controlled crossover study to test whether manipulation of FC through neurofeedback had a behavioral effect on motor performance. Patients succeeded in increasing FC in the motor cortex. This led to improvement in motor function that was significantly greater than during neurofeedback training of a control brain area and proportional to the degree of FC enhancement. This result provides evidence that FC has a causal role in neurological function and that it can be effectively targeted with therapy.

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Stroke patients participated in clinical trial on neurofeedback of functional connectivity.

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Patients learned to enhance synchrony of neural activity in their motor cortex.

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This led to reduced motor impairment.

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Evidence for a causal role of neural synchrony in neurological deficits and recovery.

Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation

Standard brain stimulation protocols modify human motor cortex excitability by modulating the gain of the activated corticospinal pathways. However, the restoration of motor function following lesions of the corticospinal tract requires also the recruitment of additional neurons to increase the net corticospinal output. For this purpose, we investigated a novel protocol based on brain state-dependent paired associative stimulation.Motor imagery (MI)-related electroencephalography was recorded in healthy males and females for brain state-dependent control of both cortical and peripheral stimulation in a brain-machine interface environment. State-dependency was investigated with concurrent, delayed, and independent stimulation relative to the MI task. Specifically, sensorimotor event-related desynchronization (ERD) in the β-band (16-22 Hz) triggered peripheral stimulation through passive hand opening by a robotic orthosis and transcranial magnetic stimulation to the respective cortical motor representation, either synchronously or subsequently. These MI-related paradigms were compared with paired cortical and peripheral input applied independent of the brain state. Cortical stimulation resulted in a significant increase in corticospinal excitability only when applied brain state-dependently and synchronously to peripheral input. These gains were resistant to a depotentiation task, revealed a nonlinear evolution of plasticity, and were mediated via the recruitment of additional corticospinal neurons rather than via synchronization of neuronal firing. Recruitment of additional corticospinal pathways may be achieved when cortical and peripheral inputs are applied concurrently, and during β-ERD. These findings resemble a gating mechanism and are potentially important for developing closed-loop brain stimulation for the treatment of hand paralysis following lesions of the corticospinal tract.SIGNIFICANCE STATEMENT The activity state of the motor system influences the excitability of corticospinal pathways to external input. State-dependent interventions harness this property to increase the connectivity between motor cortex and muscles. These stimulation protocols modulate the gain of the activated pathways, but not the overall corticospinal recruitment. In this study, a brain-machine interface paired peripheral stimulation through passive hand opening with transcranial magnetic stimulation to the respective cortical motor representation during volitional β-band desynchronization. Cortical stimulation resulted in the recruitment of additional corticospinal pathways, but only when applied brain state-dependently and synchronously to peripheral input. These effects resemble a gating mechanism and may be important for the restoration of motor function following lesions of the corticospinal tract.

Acquisition of a mental strategy to control a virtual tail via brain-computer interface

The objective of the present study was to clarify the variation in and properties of mental images and policies used to regulate specific image selection when learning to control a brain-computer interface. Healthy volunteers performed a reaching task with a virtually generated monkey tail-like object on a computer monitor by regulating event-related desynchronization (ERD) on the buttock area of the sensorimotor cortex as recorded by electroencephalogram (EEG). Participants were instructed to find a free image by which the tail was well controlled. Seven participants frequently returned to specific images that were mostly unrelated to a tail, and returned to these images on the last day of training. The ERD levels were greater during use of those selected images versus when selected images were not employed. Our results suggest that individuals adopted a mental strategy where they imagine what would reduce the prediction error between the predicted and actual BCI actions.

Investigating the impact of feedback update interval on the efficacy of restorative brain-computer interfaces

Restorative brain-computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored. There is evidence that sensory feedback disinhibits the motor cortex. Thus, in this study, we explore how shorter than usual FUIs affect behavioural and neurophysiological measures following BCI training for stroke patients using a single-case proof-of-principle study design. The action research arm test was used as the primary behavioural measure and showed a clinically significant increase (36%) over the course of training. The neurophysiological measures including motor evoked potentials and maximum voluntary contraction showed distinctive changes in early and late phases of BCI training. Thus, this preliminary study may pave the way for running larger studies to further investigate the effect of FUI magnitude on the efficacy of restorative BCIs. It may also elucidate the role of early and late phases of motor learning along the course of BCI training.

Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis

Motor recovery in severely impaired stroke patients is often very limited. To refine therapeutic interventions for regaining motor control in this patient group, the functionally relevant mechanisms of neuronal plasticity need to be detected. Cortico-muscular coherence (CMC) may provide physiological and topographic insights to achieve this goal. Synchronizing limb movements to motor-related brain activation is hypothesized to reestablish cortico-motor control indexed by CMC. In the present study, right-handed, chronic stroke patients with right-hemispheric lesions and left hand paralysis participated in a four-week training for their left upper extremity. A brain-robot interface turned event-related beta-band desynchronization of the lesioned sensorimotor cortex during kinesthetic motor-imagery into the opening of the paralyzed hand by a robotic orthosis. Simultaneous MEG/EMG recordings and individual models from MRIs were used for CMC detection and source reconstruction of cortico-muscular connectivity to the affected finger extensors before and after the training program. The upper extremity-FMA of the patients improved significantly from 16.23 ± 6.79 to 19.52 ± 7.91 (p = 0.0015). All patients showed significantly increased CMC in the beta frequency-band, with a distributed, bi-hemispheric pattern and considerable inter-individual variability. The location of CMC changes was not correlated to the severity of the motor impairment, the motor improvement or the lesion volume. Group analysis of the cortical overlap revealed a common feature in all patients following the intervention: a significantly increased level of ipsilesional premotor CMC that extended from the superior to the middle and inferior frontal gyrus, along with a confined area of increased CMC in the contralesional premotor cortex. In conclusion, functionally relevant modulations of CMC can be detected in patients with long-term, severe motor deficits after a brain-robot assisted rehabilitation training. Premotor beta-band CMC may serve as a biomarker and therapeutic target for novel treatment approaches in this patient group.

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Closed-loop neuroprosthetics aim to compensate for lost function, e.g., by controlling external devices such as prostheses or wheelchairs. Such assistive approaches seek to maximize speed and classification accuracy for high-dimensional control. More recent approaches use similar technology, but aim to restore lost motor function in the long term. To achieve this goal, restorative neuroprosthetics attempt to facilitate motor re-learning and to strengthen damaged and/or alternative neural connections on the basis of neurofeedback training within rehabilitative environments. Such a restorative approach requires reinforcement learning of self-modulated brain activity which is considered to be beneficial for functional rehabilitation, e.g., improvement of β-power modulation over sensorimotor areas for post-stroke movement restoration. Patients with motor impairments, however, may also have a compromised ability for motor task-related regulation of the targeted brain activity. This would affect the estimation of feature weights and hence the classification accuracy of the feedback device. This, in turn, can frustrate the patients and compromise their motor learning. Furthermore, the feedback training may even become erroneous when unconstrained classifier adaptation-which is often used in assistive approaches-is also applied in this rehabilitation context. In conclusion, the conceptual switch from assistance toward restoration necessitates a methodological paradigm shift from classification accuracy toward instructional efficiency. Furthermore, a constrained feature space, a priori regularized feature weights, and difficulty adaptation present key elements of restorative brain interfaces. These factors need, therefore, to be addressed within a therapeutic framework to facilitate reinforcement learning of brain self-regulation for restorative purposes.

EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial

Neurofeedback is attracting renewed interest as a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. It not only promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of electroencephalography (EEG) neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.

Proprioceptive Feedback Facilitates Motor Imagery-Related Operant Learning of Sensorimotor β-Band Modulation

Motor imagery (MI) activates the sensorimotor system independent of actual movements and might be facilitated by neurofeedback. Knowledge on the interaction between feedback modality and the involved frequency bands during MI-related brain self-regulation is still scarce. Previous studies compared the cortical activity during the MI task with concurrent feedback (MI with feedback condition) to cortical activity during the relaxation task where no feedback was provided (relaxation without feedback condition). The observed differences might, therefore, be related to either the task or the feedback. A proper comparison would necessitate studying a relaxation condition with feedback and a MI task condition without feedback as well. Right-handed healthy subjects performed two tasks, i.e., MI and relaxation, in alternating order. Each of the tasks (MI vs. relaxation) was studied with and without feedback. The respective event-driven oscillatory activity, i.e., sensorimotor desynchronization (during MI) or synchronization (during relaxation), was rewarded with contingent feedback. Importantly, feedback onset was delayed to study the task-related cortical activity in the absence of feedback provision during the delay period. The reward modality was alternated every 15 trials between proprioceptive and visual feedback. Proprioceptive input was superior to visual input to increase the range of task-related spectral perturbations in the α- and β-band, and was necessary to consistently achieve MI-related sensorimotor desynchronization (ERD) significantly below baseline. These effects occurred in task periods without feedback as well. The increased accuracy and duration of learned brain self-regulation achieved in the proprioceptive condition was specific to the β-band. MI-related operant learning of brain self-regulation is facilitated by proprioceptive feedback and mediated in the sensorimotor β-band.

Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects' abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

What Turns Assistive into Restorative Brain-Machine Interfaces?

Brain-machine interfaces (BMI) may support motor impaired patients during activities of daily living by controlling external devices such as prostheses (assistive BMI). Moreover, BMIs are applied in conjunction with robotic orthoses for rehabilitation of lost motor function via neurofeedback training (restorative BMI). Using assistive BMI in a rehabilitation context does not automatically turn them into restorative devices. This perspective article suggests key features of restorative BMI and provides the supporting evidence: In summary, BMI may be referred to as restorative tools when demonstrating subsequently (i) operant learning and progressive evolution of specific brain states/dynamics, (ii) correlated modulations of functional networks related to the therapeutic goal, (iii) subsequent improvement in a specific task, and (iv) an explicit correlation between the modulated brain dynamics and the achieved behavioral gains. Such findings would provide the rationale for translating BMI-based interventions into clinical settings for reinforcement learning and motor rehabilitation following stroke.