Abstracts:We thank Professors Grill and Wongsarnpigoon for their detailed responses prompted by our recent publication <xref ref-type="bibr" rid="ref1">[1]</xref> in this journal. We welcome the opportunity we have had for discussion and thank the Editor for his invitation to make this further contribution.

Abstracts:We read with interest the recent publication on the efficiency of neural stimulation with Gaussian-shaped stimulation waveforms <xref ref-type="bibr" rid="ref1">[1]</xref>. We were gratified that the authors chose to follow up on our work designing waveform shapes to minimize the energy required to stimulate nerve fibers <xref ref-type="bibr" rid="ref2">[2]</xref>. The results are in remarkable agreement with our published simulations and experimental measurements on the relative energy required for nerve activation using different waveform shapes. However, we are writing to respond to their conclusion that their findings challenge the predicted range of up to 60&#x0025; energy savings using our optimal waveforms.

Abstracts:Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (&#x00B1;0.14) and 0.43 (&#x00B1;0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.

Abstracts:The characterization of brain cortical activity in heart-failure patients affected by Cheyne-Stokes Respiration might provide relevant information about the mechanism underlying this pathology. Central autonomic network is gaining increasing attention for its role in the regulation of breathing and cardiac functions. In this scenario, evaluating changes in cortical connectivity associated with Cheyne-Stokes Respiration may be of interest in the study of specific brain-activity related to such disease. Nonetheless, the inter subject variability, the temporal dynamics of Central-Apnea/Hyperpnea cycles and the limitations of clinical setups lead to different methodological challenges. To this aim, we present a framework for the assessment of cortico-cortical interactions from Electroencephalographic signals acquired using low-density caps and block-design paradigms, arising from endogenous triggers. The framework combines ICA-decomposition, unsupervised clustering, MVAR modelling and a permutation-bootstrap strategy for evaluating significant connectivity differences between conditions. A common network, lateralized towards the left hemisphere, was depicted across 8 patients exhibiting Cheyne-Stokes Respiration patterns during acquisitions. Significant differences in connectivity at the group level were observed based on patients&#x2019; ventilatory condition. Interactions were significantly higher during hyperpnea periods with respect to central apneas and occurred mainly in the delta band. Opposite-sign differences were observed for higher frequencies (i.e. beta, low-gamma).

Abstracts:We evaluated different muscle excitation estimation techniques, and their sensitivity to Motor Unit (MU) distribution in muscle tissue. For this purpose, the Convolution Kernel Compensation (CKC) method was used to identify the MU spike trains from High-Density ElectroMyoGrams (HDEMG). Afterwards, Cumulative MU Spike Train (CST) was calculated by summing up the identified MU spike trains. Muscle excitation estimation from CST was compared to the recently introduced Cumulative Motor Unit Activity Index (CAI) and classically used Root-Mean-Square (RMS) amplitude envelop of EMG. To emphasize their dependence on the MU distribution further, all three muscle excitation estimates were used to calculate the agonist-antagonist co-activation index. We showed on synthetic HDEMG that RMS envelopes are the most sensitive to MU distribution (10 &#x0025; dispersion around the real value), followed by the CST (7 &#x0025; dispersion) and CAI (5 &#x0025; dispersion). In experimental HDEMG from wrist extensors and flexors of post-stroke subjects, RMS envelopes yielded significantly smaller excitations of antagonistic muscles than CST and CAI. As a result, RMS-based co-activation estimates differed significantly from the ones produced by CST and CAI, illuminating the problem of large diversity of muscle excitation estimates when multiple muscles are studied in pathological conditions. Similar results were also observed in experimental HDEMG of six intact young males.

Abstracts:Objective: Brain-computer interface (BCI) based communication remains a challenge for people with later-stage amyotrophic lateral sclerosis (ALS) who lose all voluntary muscle control. Although recent studies have demonstrated the feasibility of functional near-infrared spectroscopy (fNIRS) to successfully control BCIs primarily for healthy cohorts, these systems are yet inefficient for people with severe motor disabilities like ALS. In this study, we developed a new fNIRS-based BCI system in concert with a single-trial Visuo-Mental (VM) paradigm to investigate the feasibility of enhanced communication for ALS patients, particularly those in the later stages of the disease. Methods: In the first part of the study, we recorded data from six ALS patients using our proposed protocol (fNIRS-VM) and compared the results with the conventional electroencephalography (EEG)-based multi-trial P3Speller (P3S). In the second part, we recorded longitudinal data from one patient in the late locked-in state (LIS) who had fully lost eye-gaze control. Using statistical parametric mapping (SPM) and correlation analysis, the optimal channels and hemodynamic features were selected and used in linear discriminant analysis (LDA). Results: Over all the subjects, we obtained an average accuracy of 81.3&#x0025;&#x00B1;5.7&#x0025; within comparatively short times (&#x003C; 4 sec) in the fNIRS-VM protocol relative to an average accuracy of 74.0&#x0025;&#x00B1;8.9&#x0025; in the P3S, though not competitive in patients with no substantial visual problems. Our longitudinal analysis showed substantially superior accuracy using the proposed fNIRS-VM protocol (73.2&#x0025;&#x00B1;2.0&#x0025;) over the P3S (61.8&#x0025;&#x00B1;1.5&#x0025;). Significance: Our findings indicate the potential efficacy of our proposed system for communication and co- trol for late-stage ALS patients.

Abstracts:Objective: Blindness and low vision have severe effects on individuals&#x2019; quality of life and socioeconomic cost; a main contributor of which is a prevalent and acutely decreased mobility level. To alleviate this, numerous technological solutions have been proposed in the last 70 years; however, none has become widespread. Method: In this paper, we introduce the vision-to-audio, super-scale sensory substitution/supplementation device Audomni; we address the field-encompassing issues of ill-motivated and overabundant test methodologies and metrics; and we utilize our proposed Desire of Use model to evaluate proposed pilot user tests, their results, and Audomni itself. Results: Audomni holds a spatial resolution of 80 x 60 pixels at &#x007E;1.2&#x00B0; angular resolution and close to real-time temporal resolution, outdoor-viable technology, and several novel differentiation methods. The tests indicated that Audomni has a low learning curve, and several key mobility subtasks were accomplished; however, the tests would benefit from higher real-life motivation and data collection affordability. Conclusion: Audomni shows promise to be a viable mobility device &#x2013; with some addressable issues. Employing Desire of Use to design future tests should provide both high real-life motivation and relevance to them. Significance: As far as we know, Audomni features the greatest information conveyance rate in the field, yet seems to offer comprehensible and fairly intuitive sonification; this work is also the first to utilize Desire of Use as a tool to evaluate user tests, a device, and to lay out an overarching project aim.

Abstracts:Induced contraction of the suprahyoid muscles via magnetic stimulation is considered to be effective for the rehabilitation of dysphagia. In our previous study, a magnetic stimulation coil with a U-shaped core for stimulating the suprahyoid muscles was developed based on the results of numerical analysis using a simplified human head model. It was confirmed that magnetic stimulation by the coil causes large contraction of the muscles. However, the human head has a complex structure that includes bone structures through which current cannot easily pass. To accurately predict the current density distribution induced by magnetic stimulation, a model that accurately describes the human head is required for numerical analysis. Therefore, in this study, numerical analysis using the finite element method with a human head model that includes the bone structure obtained from computed tomography scans was performed. The results for the model with bone structure show that the coil with a U-shaped core can stimulate the motor points of the suprahyoid muscles in the middle of the submental region. When compared with the current density observed in a model without the bone structure, that in the model with the bone structure was reduced by 29&#x0025; at a point 20 mm below the mandibular surface. It is thus necessary to perform a numerical analysis using a model with the bone structure to obtain accurate analysis results.

Abstracts:Upper limb functions are severely affected in 23&#x0025; of the chronic stroke patients, compromising their life quality. To re-enable hand use, providing a degree of functionality and motivating against learned non-use, we propose a robotic supernumerary limb, the <italic>SoftHand X</italic> (SHX), consisting of a robotic hand, a gravity support system, and different sensors to detect the patient&#x2019;s intent for controlling the robotic hand. In this paper, this novel compensational approach is introduced and experimentally evaluated in stroke patients, assessing its efficacy, usability and safety. Ten patients were asked to perform tasks of a modified Action Research Arm Test with the SHX, by using three input methods. The mARAT scores rated the potentiality of the system. Usability was evaluated with the System Usability Scale, while spasticity before and after use was measured by the modified Ashworth Scale (mAS). Nine patients, not able to perform any tasks without external support, completed the whole experimental procedure using the proposed system with a median score greater than 12/30. Among the three input methods tested, the usability of one was rated as &#x201C;good&#x201D; while the other two were rated as &#x201C;ok&#x201D;. Seven patients exhibited a reduction of the mAS. All nine patients stated that they would use the system frequently. Results obtained suggest that the SHX has the potential to partially compensate severely impaired hand function in stroke patients.

Abstracts:Powered exoskeletons are among the emerging technologies claiming to assist functional ambulation. The potential to adapt robotic assistance based on specific motor abilities of incomplete spinal cord injury (iSCI) subjects, is crucial to optimize Human-Robot Interaction (HRI). Achilles, an autonomous wearable robot able to assist ankle during walking, was developed for iSCI subjects and utilizes a NeuroMuscular Controller (NMC). NMC can be used to adapt robotic assistance based on specific residual functional abilities of subjects. The main aim of this pilot study was to analyze the effects of the NMC-controlled Achilles, used as an assistive device, on chronic iSCI participants&#x2019; performance, by assessing gait speed during 10-session training of robot-aided walking. Secondary aims were to assess training impact on participants&#x2019; motion, clinical and functional features and to evaluate subjective perspective in terms of attitude towards technology, workload, usability and satisfaction. Results showed that 5 training sessions were necessary to significantly improve robot-aided gait speed on short paths and consequently to optimize HRI. Moreover, the training allowed participants who initially were not able to walk for 6 minutes, to improve gait endurance during Achilles-aided walking and to reduce perceived fatigue. Improvements were obtained also in gait speed during free walking, thus suggesting a potential rehabilitative impact, even if Achilles-aided walking was not faster than free walking. Participants&#x2019; subjective evaluations indicated a positive experience.