About half of all severely affected stroke survivors remain with persistent motor deficits in the chronic disease stage despite therapeutic interventions on the basis of the current standard of care (Winters et al., 2015). Since these patients cannot use the affected hand for activities of daily living, novel interventions investigate different neurotechnological devices to facilitate upper limb motor rehabilitation, such as brain-machine interfaces (BMI), robotic orthoses, neuromuscular functional electrical stimulation (FES), and brain stimulation (Coscia et al., 2019). BMI approaches, for example, aim at closing the impaired sensorimotor loop in severe chronic stroke patients. They use robotic orthoses (Ang et al., 2015; Kasashima-Shindo et al., 2015; Belardinelli et al., 2017), FES devices (Kim et al., 2016; Biasiucci et al., 2018), and their combination (Grimm et al., 2016c; Resquín et al., 2017) to provide natural sensory and proprioceptive neurofeedback during movement intention or imagery. It is hypothesized that this approach will lead to reorganization of the corticospinal network through repetitive practice, and might ultimately restore the lost motor function (Naros and Gharabaghi, 2015, 2017; Belardinelli et al., 2017; Guggenberger et al., 2018).
However, these novel approaches often result in no relevant clinical improvements in severe chronic stroke patients yet (Coscia et al., 2019). Therefore, recent research has taken a refined and rather mechanistic approach, e.g., by targeting physiologically grounded and clinically relevant biomarkers with BMI neurofeedback; this has led to the conceptional differentiation between restorative therapeutic BMIs on the one side (as those applied in this study) and classical assistive BMIs on the other side like those applied to control devices such as wheel-chairs (Gharabaghi, 2016): While assistive BMIs intend to maximize the decoding accuracy, restorative BMIs want to enhance behaviorally relevant biomarkers. Specifically, brain oscillations in the beta frequency band have been suggested as potential candidate markers and therapeutic targets for technology-assisted stroke rehabilitation with restorative BMIs (Naros and Gharabaghi, 2015, 2017; Belardinelli et al., 2017), since they are known to enhance signal propagation in the motor system and to determine the input-output ratio of corticospinal excitability in a frequency- and phase-specific way (Raco et al., 2016; Khademi et al., 2018, 2019; Naros et al., 2019).
However, these restorative BMI devices differ from their predecessors, i.e., assistive BMIs, by an intentionally regularized and restricted feature space, e.g., by using the beta frequency band as a feedback signal for BMI control (Gharabaghi, 2016; Bauer and Gharabaghi, 2017). Such a more specific approach is inherently different from previous more flexible algorithms that select and weight brain signal features to maximize the decoding accuracy of the applied technology; restorative BMIs like the those applied in this study have, therefore, relevantly less classification accuracy than classical assistive BMIs (Vidaurre et al., 2011; Bryan et al., 2013). As the regularized and restricted feature space of such restorative BMI devices leads to a lower classification accuracy in comparison to more flexible approaches, it may be frustrating even for healthy participants (Fels et al., 2015). IN the context of the present study, we conjectured that such challenging tasks will increase the relevance of extraneous load aspects like the workload (Schnotz and Kürschner, 2007). Furthermore, the modulation range of the oscillatory beta frequency band is compromised in stroke patients, proportionally to their motor impairment level (Rossiter et al., 2014; Shiner et al., 2015). That means that more severely affected patients show less oscillatory event-related desynchronization (ERD) and synchronization (ERS) during motor execution or imagery (Pfurtscheller and Lopes da Silva, 1999). To our understanding, this underlines the relevance of beta oscillations as a therapeutic target for post-stroke rehabilitation. At the same time, however, this poses a major challenge for the affected patients and may, thereby, compromise their therapeutic benefit (Gomez-Rodriguez et al., 2011a,b; Brauchle et al., 2015).
To overcome these hurdles that are inherent to restorative BMI devices, we have investigated different approaches in the past: (i) Reducing the brain signal attenuation by the skull via the application of epidural interfaces (Gharabaghi et al., 2014b,c; Spüler et al., 2014), (ii) Augmenting the afferent feedback of the robotic orthosis by providing concurrent virtual reality input (Grimm et al., 2016a,b), (iii) combining the orthosis-assisted movements with neuromuscular (Grimm and Gharabaghi, 2016; Grimm et al., 2016c) or transcranial electrical stimulation (Naros et al., 2016a) to enhance the cortical modulation range (Reynolds et al., 2015), and (iv) optimizing the mental workload related to the use of BMI devices.
In this study, we focus on the latter approach, i.e., optimizing the mental workload related to the use of BMI devices. For the latter approach it would be necessary to better understand the workloads related to different technologies applied in the context of BMI feedback (robotics vs. FES). We, therefore, investigated the mental demand, physical demand, temporal demand, performance, effort, and frustration of healthy subjects controlling a BMI by motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the beta frequency-band was turned into passive opening of the contralateral hand by a robotic exoskeleton or FES in a randomized, cross-over block design. The respective workloads were compared to the task-related aspects of active hand movement with EMG feedback. We conjectured a feedback-specific workload profile that would be informative for more personalized future BMI approaches.
We recruited 20 healthy subjects (age = 23.5 ± 1.08 yeas [mean ± SD], range 19–27, 15 female) for this study. Subjects were not naive to the tasks. All were right-handed and reached a score equal or above 60 in the Edinburgh Handedness Inventory (Oldfield, 1971). The subjects gave their written informed consent before participation and the study protocol was approved by the Ethics Committee of the Medical Faculty of the University of Tübingen. They received monetary compensation.
We used Ag/AgCl electrodes in a 32 channel setup according to the international 10-20 system (Fp1, Fp2, F3, Fz, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, TP9, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P3, Pz, P4, O1, O2 with TP10 as Reference and AFz as Ground) to examine the cortical activation pattern during the training session. Electrode impedances were set below 10 kΩ. All signals are digitalized at a sampling frequency of 1,000 Hz (robotic block) or 5,000 Hz (FES block) using Brain Products Amplifiers and transmitted online to BCI2000 software. BCI2000 controlled in combination with a custom-made software the respective feedback device, i.e., either the robotic orthosis or the functional electrical stimulation. Depending on the task, one of the following preparations was performed. Either the robotic hand orthosis (Amadeo, Tyromotion) was attached to the subject’s left hand (Figure 1A), fixated with Velcro strips across the forearm and with magnetic pads on the fingertips (Gharabaghi et al., 2014a; Naros et al., 2016b); or functional electrical stimulation (FES, Figure 1B) was applied to the M. extensor digitorum communis (EDC) by the RehaMove2 (Hasomed GmbH, Magdeburg) with two self-adhering electrodes (50 mm, HAN-SEN Trading & Consulting GmbH, Hamburg). First an electrode was fixed to the distal end of the EDC’s muscle belly serving as ground. Then a rectangular electrode prepared with contact gel was used to find the optimal place for the second electrode where maximal extension of the left hand could be achieved. Here a custom written Matlab script was executed to detect the current threshold needed for the extension. Starting at 1 mA, the current was increased in steps of 0.5–1 mA. During each trial, FES was applied for 3 s with a pulse width of 1,000 μs and a frequency of 100 Hz. At the beginning of stimulation, a ramping protocol was implemented for 500 ms. Once, the correct position and threshold of stimulation were found, the temporary electrode was replaced by the second stimulation electrode and both were fixed with tape. A mean stimulation intensity of 6.5 mA (SD = 2.27) was required to cause the desired contraction in this study.
Figure 1. Experimental set-up. (Left) Robotic hand orthosis as feedback device (Amadeo, Tyromotion GmbH, Graz). (Middle) Neuromuscular forearm stimulation as feedback device (RehaMove 2, Hasomed GmbH, Magdeburg). In both cases, a brain-machine interface (BMI) detected motor imagery-related oscillations in the beta frequency band by an electroencephalogram (EEG) and provided via a BCI2000-system contingent feedback by moving the hand with either the robot or the electrical stimulation. (Right) The EEG montage used in this study.