Severe upper limb paresis can represent an immense burden for stroke survivors. Given the rising prevalence of stroke, restoration of severe upper limb motor impairment remains a major challenge for rehabilitation medicine because effective treatment strategies are lacking. Commonly applied interventions in Germany, such as mirror therapy and impairment-oriented training, are limited in efficacy, demanding for new strategies to be found. By translating brain signals into control commands of external devices, brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) represent promising, neurotechnology-based alternatives for stroke patients with highly restricted arm and hand function. In this mini-review, we outline perspectives on how BCI-based therapy can be integrated into the different stages of neurorehabilitation in Germany to meet a long-term treatment approach: We found that it is most appropriate to start therapy with BCI-based neurofeedback immediately after early rehabilitation. BCI-driven functional electrical stimulation (FES) and BMI robotic therapy are well suited for subsequent post hospital curative treatment in the subacute stage. BCI-based hand exoskeleton training can be continued within outpatient occupational therapy to further improve hand function and address motivational issues in chronic stroke patients. Once the rehabilitation potential is exhausted, BCI technology can be used to drive assistive devices to compensate for impaired function. However, there are several challenges yet to overcome before such long-term treatment strategies can be implemented within broad clinical application: 1. developing reliable BCI systems with better usability; 2. conducting more research to improve BCI training paradigms and 3. establishing reliable methods to identify suitable patients.

The development of brain–computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting brain/neural activity into control signals of wearable actuators, brain/neural exoskeletons (B/NEs) enable the execution of movements despite impaired motor function. Beyond the use as assistive devices, it was shown that—upon repeated use over several weeks—B/NEs can trigger motor recovery, even in chronic paralysis. Recent development of lightweight robotic actuators, comfortable and portable real-world brain recordings, as well as reliable brain/neural control strategies have paved the way for B/NEs to enter clinical care. Although B/NEs are now technically ready for broader clinical use, their promotion will critically depend on early adopters, for example, research-oriented physiotherapists or clinicians who are open for innovation. Data collected by early adopters will further elucidate the underlying mechanisms of B/NE-triggered motor recovery and play a key role in increasing efficacy of personalized treatment strategies. Moreover, early adopters will provide indispensable feedback to the manufacturers necessary to further improve robustness, applicability, and adoption of B/NEs into existing therapy plans.

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Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

Voluntary control of sensorimotor rhythms (SMR, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g., in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. It would thus be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline during voluntary control of SMR-ERD across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 minutes each. During the second run, task difficulty was gradually increased. While control performance (p = .18) and HRV (p = .16) remained unchanged across participants during the first run, during the second run, both measures declined over time at high correlation (performance: -0.61%/10s, p = 0; HRV: -0.007ms/10s, p < .001). We found that HRV Granger-caused BCI control performance (p < .001) exhibited predictive characteristics of HRV on an individual participant level. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g., to individualize and optimize assistive BCI systems in stroke.

Objective. Voluntary control of sensorimotor rhythms (SMRs, 8–12 Hz) can be used for brain–computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g. in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. Therefore, it would be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man–machine interaction scheme. Approach. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline of SMR-ERD control across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 min each. During the 2nd run, task difficulty was gradually increased. Main results. While control performance (p = .18) and HRV (p = .16) remained unchanged across participants during the 1st run, during the 2nd run, both measures declined over time at high correlation (performance: −0.61%/10 s, p = 0; HRV: −0.007 ms/10 s, p < .001). We found that HRV exhibited predictive characteristics with regard to within-session BCI control performance on an individual participant level (p < .001). Significance. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g. to individualize and optimize assistive BCI systems in stroke.

Background Assistive hand exoskeletons are promising tools to restore hand function after cervical spinal cord injury (SCI) but assessing their specific impact on bimanual hand and arm function is limited due to lack of reliable and valid clinical tests. Here, we introduce the Berlin Bimanual Test for Tetraplegia (BeBiTT) and demonstrate its psychometric properties and sensitivity to assistive hand exoskeleton-related improvements in bimanual task performance. Methods Fourteen study participants with subacute cervical SCI performed the BeBiTT unassisted (baseline). Thereafter, participants repeated the BeBiTT while wearing a brain/neural hand exoskeleton (B/NHE) (intervention). Online control of the B/NHE was established via a hybrid sensorimotor rhythm-based brain-computer interface (BCI) translating electroencephalographic (EEG) and electrooculographic (EOG) signals into open/close commands. For reliability assessment, BeBiTT scores were obtained by four independent observers. Besides internal consistency analysis, construct validity was assessed by correlating baseline BeBiTT scores with the Spinal Cord Independence Measure III (SCIM III) and Quadriplegia Index of Function (QIF). Sensitivity to differences in bimanual task performance was assessed with a bootstrapped paired t-test. Results The BeBiTT showed excellent interrater reliability (intraclass correlation coefficients > 0.9) and internal consistency (α = 0.91). Validity of the BeBiTT was evidenced by strong correlations between BeBiTT scores and SCIM III as well as QIF. Wearing a B/NHE (intervention) improved the BeBiTT score significantly (p < 0.05) with high effect size (d = 1.063), documenting high sensitivity to intervention-related differences in bimanual task performance. Conclusion The BeBiTT is a reliable and valid test for evaluating bimanual task performance in persons with tetraplegia, suitable to assess the impact of assistive hand exoskeletons on bimanual function.

Background: Assistive hand exoskeletons are promising tools to restore hand function after cervical spinal cord injury (SCI) but assessing their speci c impact on bimanual hand and arm function is limited due to lack of reliable and valid clinical tests. Here, we introduce the Berlin Bimanual Test for Tetraplegia (BeBiTT) and demonstrate its sensitivity to brain/neural hand exoskeleton(B/NHE)-related improvements in bimanual task performance. Methods: Fourteen study participants with subacute cervical SCI performed the BeBiTT unassisted (baseline). Thereafter, participants repeated the BeBiTT while wearing a B/NHE (intervention). Online control of the B/NHE was established via a hybrid sensorimotor rhythm-based brain-computer interface (BCI) translating electroencephalographic (EEG) and electrooculographic (EOG) signals into open/close commands. For reliability assessment, BeBiTT scores were obtained by four independent observers.Besides internal consistency analysis, construct validity was assessed by correlating baseline BeBiTT scores with the Spinal Cord Independence Measure III (SCIM III) and Quadriplegia Index of Function (QIF). Sensitivity to differences in bimanual task performance was assessed with a bootstrapped paired t-test.Results: The BeBiTT showed excellent interrater reliability (intraclass correlation coe cients > 0.9) and internal consistency (α = 0.91). Validity of the BeBiTT was evidenced by strong correlations between BeBiTT scores and SCIM III as well as QIF. Wearing a B/NHE (intervention) improved the BeBiTT score signi cantly (p < 0.05) with high effect size (d =1.063), documenting high sensitivity to interventionrelated differences in bimanual task performance. Conclusion:The BeBiTT is a reliable and valid test for evaluating bimanual task performance in persons with tetraplegia, suitable to assess the impact of assistive hand exoskeletons on bimanual function.