Brain Self-regulation Research Articles

Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.

While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Amygdala-related electrical fingerprint is modulated with neurofeedback training and correlates with deep-brain activation: proof-of-concept in borderline personality disorder.

The modulation of brain circuits of emotion is a promising pathway to treat borderline personality disorder (BPD). Precise and scalable approaches have yet to be established. Two studies investigating the amygdala-related electrical fingerprint (Amyg-EFP) in BPD are presented: one study addressing the deep-brain correlates of Amyg-EFP, and a second study investigating neurofeedback (NF) as a means to improve brain self-regulation. Study 1 combined electroencephalography (EEG) and simultaneous functional magnetic resonance imaging to investigate the replicability of Amyg-EFP-related brain activation found in the reference dataset (N = 24 healthy subjects, 8 female; re-analysis of published data) in the replication dataset (N = 16 female individuals with BPD). In the replication dataset, we additionally explored how the Amyg-EFP would map to neural circuits defined by the research domain criteria. Study 2 investigated a 10-session Amyg-EFP NF training in parallel to a 12-weeks residential dialectical behavior therapy (DBT) program. Fifteen patients with BPD completed the training, N = 15 matched patients served as DBT-only controls. Study 1 replicated previous findings and showed significant amygdala blood oxygenation level dependent activation in a whole-brain regression analysis with the Amyg-EFP. Neurocircuitry activation (negative affect, salience, and cognitive control) was correlated with the Amyg-EFP signal. Study 2 showed Amyg-EFP modulation with NF training, but patients received reversed feedback for technical reasons, which limited interpretation of results. Recorded via scalp EEG, the Amyg-EFP picks up brain activation of high relevance for emotion. Administering Amyg-EFP NF in addition to standardized BPD treatment was shown to be feasible. Clinical utility remains to be investigated.

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task.

Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation. We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning. Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning. The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

Infralow neurofeedback in the treatment of substance use disorders: a randomized controlled trial.

BackgroundInfralow neurofeedback (ILF-NF) was recently developed as a subtype of traditional, frequency-based neurofeedback that targets cerebral rhythmic activity below 0.5 Hz and improves brain self-regulation. The efficacy of ILF-NF in the treatment of substance use disorder has not yet been evaluated, but clinical evidence suggests that it may prevent relapse by improving functioning in various life domains. The current study aimed to fill this research gap and extend empirical evidence related to this issue.MethodsNinety-three patients with substance use disorders at an outpatient unit in Norway were randomized to receive 20 sessions (30 minutes each) of ILF-NF training combined with treatment as usual (TAU), or TAU alone. The primary outcome was quality of life post-treatment as an overall measure of functioning. We analyzed between-group differences using Student t tests.ResultsWe found no significant differences in quality of life between groups. We found similar nonsignificant results for most of the secondary outcome measures, including drug use, sleep, anxiety and depression. Compared to TAU, the ILF-NF + TAU group reported significantly lower restlessness scores post-treatment (mean difference −1.8, 95 % confidence interval −3.1 to −0.5; p = 0.006).Limitations: This study was limited by broad inclusion criteria and a lack of placebo control (sham neurofeedback treatment).ConclusionILF-NF offered limited additional benefit when combined with TAU, except in the area of restlessness. Future studies could further investigate the relationship between ILF-NF, restlessness and substance use in targeted subpopulations to illuminate relapse mechanisms.Clinical trial registrationClinicalTrials.gov: NCT03356210

Weighted neurofeedback facilitates greater self-regulation of functional connectivity between the primary motor area and cerebellum

Objective. Brain–computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance. Approach. Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self‐regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance. Main results. A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed. Significance. The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.

Design of a Neurofeedback Training System for Meditation Based on EEG Technology

Meditation is a form of mental training that has therapeutic potential and cognitive benefits that may enhance attention, mental well-being, and neuroplasticity. However, the learning process is not easy because meditators do not receive immediate feedback that lets them know if they are correctly doing the activity. EEG Neurofeedback training is one of the techniques to train brain self-regulation and it has the potential to increase the effectiveness of meditation. However, the benefits greatly differ between subjects with a high percentage of inefficacy. In this work, an EEG Neurofeedback Training System is proposed based on user-centered design methodology to provide real-time performance feedback to meditators to increase levels of attention and relaxation through a visual, sound and smell stimuli interface. Levels of attention and relaxation of nine participants were measured with a mobile Neurosky EEG headset biosensor during meditation practice to analyze the incidence of each type of stimuli during activity. Visual stimuli feedback was able to increase attention levels of 78% of the participants by 11.8% compared to a meditation session without any stimuli. The sound stimuli feedback was able to increase the relaxation levels of 44.4% of the participants by 16% compared to a session without any stimuli. These results might bring new insights for the design of a neurofeedback system interface for meditation. Further research on neurofeedback training interfaces for meditators is suggested to validate these results with more participants.

The role of maternal BMI on brain food cue reactivity in children: a preliminary study.

Children of overweight and obese parents have an increased risk of obesity. Little is known the neural mechanisms underlying this relationship, specifically the brain systems implicated in self-regulation of food intake. The primary goal here is to examine relationships between maternal body mass index (BMI) and brain responses to food cues in children. Seventy-six children (8.62 ± 1.02years; 28M,48F) were included in this study. Height and weight were assessed for children and their biological parents. Maternal height and weight before pregnancy were extracted from the Electronic Medical Records (EMR). BMI (kg/m2) or BMIz (age- and sex-specific BMI) were calculated. Children underwent a magnetic resonance imaging session where they viewed food and non-food images before and after glucose ingestion. The dorsolateral prefrontal cortex (dlPFC) and anterior cingulate cortex (ACC) food cue reactivity was the measurement of interest for region-of-interest (ROI) analyses. Whole-brain exploratory analysis was performed as well. Non-parametric methods were used for data analysis. ROI and whole brain analyses showed that maternal current BMI was inversely associated with child's ACC and dlPFC food cue reactivity after glucose ingestion, adjusting for age and sex. No significant relationships were found between paternal BMI and child's food cue reactivity. Child BMIz was negatively associated with the ACC food cue reactivity after glucose ingestion. Our results supported the role of maternal adiposity on child's responses to appetitive food cues in brain self-regulation circuitry, which may influence eating behavior and obesity risk in children.

Balancing the brain of offenders with psychopathy? Resting state EEG and electrodermal activity after a pilot study of brain self-regulation training

Although investigation of the brains of criminals began quite early in the history of psychophysiological research, little is known about brain plasticity of offenders with psychopathy. Building on our preliminary study reporting successful brain self-regulation using slow cortical potential (SCP) neurofeedback in offenders with psychopathy, we investigated the central nervous and autonomic peripheral changes occurring after brain self-regulation in a group of severe male offenders with psychopathy. Regarding the central nervous system, an overall suppression of the psychopathic overrepresentation of slow frequency bands was found, such as delta and theta band activity, after EEG neurofeedback. In addition, an increase in alpha band activity could be observed after the SCP self-regulation training. Electrodermal activity adaptively changed according to the regulation task, and this flexibility improved over training time. The results of this study point towards a constructive learning process and plasticity in neural and peripheral measures of offenders with psychopathy.

Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback

Autism spectrum disorder is (ASD) characterized by a persisting triad of impairments of social interaction, language as well as inflexible, stereotyped and ritualistic behaviors. Increasingly, scientific evidence suggests a neurobiological basis of these emotional, social and cognitive deficits in individuals with ASD.The aim of this randomized controlled brain self-regulation intervention study was to investigate whether the core symptomatology of ASD could be reduced via an electroencephalography (EEG) based brain self-regulation training of Slow Cortical Potentials (SCP). 41 male adolescents with ASD were recruited and allocated to a) an experimental group undergoing 24 sessions of EEG-based brain training (n1 = 21), or to b) an active control group undergoing conventional treatment (n2 = 20), that is, clinical counseling during a 3-months intervention period. We employed real-time neurofeedback training recorded from a fronto-central electrode intended to enable participants to volitionally regulate their brain activity.Core autistic symptomatology was measured at six time points during the intervention and analyzed with Bayesian multilevel approach to characterize changes in core symptomatology. Additional Bayesian models were formulated to describe the neural dynamics of the training process as indexed by SCP (time-domain) and power density (PSD, frequency-domain) measures.The analysis revealed a substantial improvement in the core symptomatology of ASD in the experimental group (reduction of 21.38 points on the Social Responsiveness Scale, SD = 5.29), which was slightly superior to that observed in the control group (evidence Ratio = 5.79). Changes in SCP manifested themselves as different trajectories depending on the different feedback conditions and tasks. Further, the model of PSD revealed a continuous decrease in delta power, parallel to an increase in alpha power. Most notably, a non-linear (quadratic) model turned out to be better at predicting the data than a linear model across all analyses. Taken together, our analyses suggest that behavioral and neural processes of change related to neurofeedback training are complex and non-linear. Moreover, they have implications for the design of future trials and training protocols.

Training the anxious brain: using fMRI‐based neurofeedback to change brain activity in adolescence

Anxiety disorders are a leading cause of morbidity and entail a lot of costs. Adolescence is characterized by social fears and poor emotion regulation abilities which together increase the likelihood of the emergence of anxiety disorders. This emotion dysregulation is potentially caused by the emotion regulating brain areas, such as the prefrontal cortex and temporal cortex, that are still undergoing developmental changes throughout late adolescence. Recently, new approaches have used functional magnetic resonance imaging-based neurofeedback to help participants gain control over emotion regulation brain networks by receiving real-time feedback on their brain activity and to use effective emotion regulation abilities. In this review, we provide an overview of the developmental changes in the brain and the corresponding behavioural changes, and explore how these can be influenced during adolescence using neurofeedback. We conclude that recent studies show promising results that children and adolescents can self-regulate emotion regulation brain networks thereby supporting the development of effective emotion regulation abilities. WHAT THIS PAPER ADDS: Functional magnetic resonance imaging-based neurofeedback can be used for brain self-regulation in development. The emotion regulation networks play a key role in treating social anxiety with neurofeedback.

Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements

Neurofeedback (NF) is a research and clinical technique, characterized by live demonstration of brain activation to the subject. The technique has become increasingly popular as a tool for the training of brain self-regulation, fueled by the superiority in spatial resolution and fidelity brought along with real-time analysis of fMRI (functional magnetic resonance imaging) data, compared to the more traditional EEG (electroencephalography) approach. NF learning is a complex phenomenon and a controversial discussion on its feasibility and mechanisms has arisen in the literature. Critical aspects of the design of fMRI-NF studies include the localization of neural targets, cognitive and operant aspects of the training procedure, personalization of training, and the definition of training success, both through neural effects and (for studies with therapeutic aims) through clinical effects. In this paper, we argue that a developmental perspective should inform neural target selection particularly for pediatric populations, and different success metrics may allow in-depth analysis of NF learning. The relevance of the functional neuroanatomy of NF learning for brain target selection is discussed. Furthermore, we address controversial topics such as the role of strategy instructions, sometimes given to subjects in order to facilitate learning, and the timing of feedback. Discussion of these topics opens sight on problems that require further conceptual and empirical work, in order to improve the impact that fMRI-NF could have on basic and applied research in future.

The orbitofrontal cortex processes neurofeedback failure signals

Receiving feedback from neural activity, dubbed neurofeedback, can reinforce brain self-regulation. In a real-time functional magnetic resonance imaging (fMRI) experiment, healthy participants received amygdala neurofeedback via a visual brain-computer interface. The brain response to signals of reward and failure was modeled. In contrast to previous analyses, we take into account feedback that immediately preceded these signals. That means we tested whether responses were modulated while participants observed sequent reward and failure signals. The orbitofrontal cortex (OFC) showed a negative Blood Oxygenation Level Dependent (BOLD) response to failure signals, when they were preceded by more failure signals. When failure signals were preceded by reward, in contrast, the response was less pronounced. The results suggest weighted processing of neurofeedback value in the OFC. Learning to self-regulate the brain with neurofeedback may involve similar neural networks as the learning of goal-directed action.

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.

Does priming subjects, with not only resting state but also with mindfulness or/and guided imagery, affect self-regulation of SMR neurofeedback? Framework to improve brain self-regulation and support the rehabilitation of disorders such as depression, anxiety, stress and attention control.

Does priming subjects, with not only resting state but also with mindfulness or/and guided imagery, affect self-regulation of SMR neurofeedback? Framework to improve brain self-regulation and support the rehabilitation of disorders such as depression, anxiety, stress and attention control.

Prefrontal Activation in the EEG Biofeedback training using a General-Purpose Brainwave Meter

Commercialized EEG monitoring technologies present no standard for evaluating conditions on practicing brain self-regulation; in order to verify the standardization of brain self-regulation through a Human Computer Interface(HCI) reference posture. Both Autogenic Training(AT) and ANSI/HFES 100 HCI reference postures were studied for similarities and differences. From these, six postures were similar and two other postures were somewhat different. To verify availability in applying the reference posture of ANSI/HFES 100–2007 in measuring brain waves as a standard posture, this thesis measured the brain wave of users after practicing AT in an upright sitting posture, for those already having acquired AT. Brain waves recorded for 120 seconds from 6 participants at an upright sitting posture, who had acquired the ability to enter an autogenic shift within 10 seconds. The α-wave made up 16∼21% while the θ-wave was 27∼29% of the brain waves. Training was applicable to all the users despite the users having differences positioning their head downward or not and their arms and hands resting on their knees or not, with differences in the leaning posture of AT and the comfortable workspace posture of ANSI/HFES 100. The results indicated that measuring brain waves after training for self-regulation just like the reference posture allows for maintaining a state of very high concentration. As such, there is a need for defining concentration standards by conducting a comparison of brain waves in general and after training self-regulating brain, both under the HCI conditions. In addition, a study on brain wave changes for untrained and subsequently trained users, both at the HCI posture, seems necessary. The methodology allows for quantifying how much concentration is present for a user interacting with a computer in situations such as controlling equipment remotely or in an education setting.

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.

Brain self-regulation in criminal psychopaths

Introduction: Psychopaths belong to a larger group of people with antisocial personality disorders and can be characterized by several symptoms: deficits in empathy, lack of remorse, impulsivity, aggressiveness and lack of behavioral control. Recent studies support the hypothesis that psychopathic offenders show structural and functional deficits in paralimbic and frontal areas associated with anticipatory planning, emotion regulation, empathy and behavioral inhibition and control. Additionally psychophysiological findings reveal differences in spontaneous brain activity as well as in event related potentials. Based on studies using feedback of slow cortical potentials (SCP-Feedback) in children with Attention Deficit-/Hyperactivity Disorder, we hypothesize that also psychopathic patients can gain volitional control of their brain activity and may profit from learned prefrontal control.

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.

Baduanjin Mind-Body Intervention Improves the Executive Control Function.

This study aims at comparing the effects of the Baduanjin mind-body (BMB) intervention with a conventional relaxation training program on enhancing the executive function. The study also attempts to explore the neural substrates underlying the cognitive effect of BMB intervention using near-infrared spectroscopy (NIRS) technique. Forty-two healthy college students were randomly allocated into either the Baduanjin intervention group or relaxation training (control) group. Training lasted for 8 weeks (90 min/day, 5 days/week). Each participant was administered the shortened Profile of Mood States to evaluate their mood status and the flanker task to evaluate executive function before and after training. While performing the flanker task, the NIRS data were collected from each participant. After training, individuals who have participated in BMB exercise showed a significant reduction in depressive mood compared with the same measure before the intervention. However, participants in the control group showed no such reduction. The before vs. after measurement difference in the flanker task incongruent trails was significant only for the Baduanjin intervention group. Interestingly, an increase in oxygenated hemoglobin in the left prefrontal cortex was observed during the Incongruent Trails test only after the BMB exercise intervention. These findings implicate that Baduanjin is an effective and easy-to-administering mind-body exercise for improving executive function and perhaps brain self-regulation in a young and healthy population.

Physiological and behavioral effects of β-tACS on brain self-regulation in chronic stroke.

Unlike in healthy controls, sensorimotor β-desynchronization (β-ERD) is compromised in stroke patients, i.e., the more severe the patient's motor impairment, the less β-ERD. This, in turn, provides a target substrate for therapeutic brain self-regulation and neurofeedback. Transcranial alternating current stimulation (tACS) has been shown to modulate brain oscillations during and after stimulation, and may thus facilitate brain self-regulation during neurofeedback interventions. Twenty severely impaired, chronic stroke patients performed kinesthetic motor-imagery while a brain-robot interface transformed β-ERD (17-23Hz) of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. In a parallel group design, β-tACS (20Hz, 1.1mA peak-to-peak amplitude) was applied to the lesioned motor cortex either continuously (c-tACS) before or intermittently (i-tACS) during the intervention. Physiological effects of β-tACS were studied using electroencephalography. The patients' ability for brain self-regulation was captured by neurofeedback performance metrics. i-tACS - but not c-tACS - improved the classification accuracy of the neurofeedback intervention in comparison to baseline. This effect was mediated via the increased specificity of the classification, i.e., reduced variance of resting oscillations. Neither i-tACS nor c-tACS had aftereffects following the stimulation period. β-tACS may constitute an adjunct neuromodulation technique during neurofeedback-based interventions for stroke rehabilitation.