Evaluation of electrical impedance tomography for determination of urinary bladder volume: comparison with standard ultrasound methods in healthy volunteers.

BACKGROUND: Continuous non-invasive urinary bladder volume measurement (cystovolumetry) would allow better management of urinary tract disease. Electrical impedance tomography (EIT) represents a promising method to overcome the limitations of non-continuous ultrasound measurements. The aim of this study was to compare the measurement accuracy of EIT to standard ultrasound in healthy volunteers. METHODS: For EIT of the bladder a commercial device (Goe MF II) was used with 4 different configurations of 16 standard ECG electrodes attached to the lower abdomen of healthy participants. To estimate maximum bladder capacity (BCmax) and residual urine (RU) two ultrasound methods (US-Ellipsoid and US-L × W × H) and a bedside bladder scanner (BS), were performed at the point of urgency and after voiding. For volume reference, BCmax and RU were validated by urine collection in a weight measuring pitcher. The global impedance method was used offline to estimate BCmax and RU from EIT. RESULTS: The mean error of US-Ellipsoid (37 ± 17%) and US-L × W × H (36 ± 15%) and EIT (32 ± 18%) showed no significant differences in the estimation of BCmax (mean 743 ± 200 ml) normalized to pitcher volumetry. BS showed significantly worse accuracy (55 ± 9%). Volumetry of RU (mean 152.1 ± 64 ml) revealed comparable higher errors for both EIT (72 ± 58%) and BS (63 ± 24%) compared to US-Ellipsoid (54 ± 25%). In case of RU, EIT accuracy is dependent on electrode configuration, as the Stripes (41 ± 25%) and Matrix (38 ± 27%) configurations revealed significantly superior accuracy to the 1 × 16 (116 ± 62%) configuration. CONCLUSIONS: EIT-cystovolumetry compares well with ultrasound techniques. For estimation of RU, the selection of the EIT electrode configuration is important. Also, the development of an algorithm should consider the impact of movement artefacts. Finally, the accuracy of non-invasive ultrasound accepted as gold standard of cystovolumetry should be reconsidered.

Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury.

BACKGROUND: The bilateral loss of the grasp function associated with a lesion of the cervical spinal cord severely limits the affected individuals' ability to live independently and return to gainful employment after sustaining a spinal cord injury (SCI). Any improvement in lost or limited grasp function is highly desirable. With current neuroprostheses, relevant improvements can be achieved in end users with preserved shoulder and elbow, but missing hand function. OBJECTIVE: The aim of this single case study is to show that (1) with the support of hybrid neuroprostheses combining functional electrical stimulation (FES) with orthoses, restoration of hand, finger and elbow function is possible in users with high-level SCI and (2) shared control principles can be effectively used to allow for a brain-computer interface (BCI) control, even if only moderate BCI performance is achieved after extensive training. PATIENT AND METHODS: The individual in this study is a right-handed 41-year-old man who sustained a traumatic SCI in 2009 and has a complete motor and sensory lesion at the level of C4. He is unable to generate functionally relevant movements of the elbow, hand and fingers on either side. He underwent extensive FES training (30-45min, 2-3 times per week for 6 months) and motor imagery (MI) BCI training (415 runs in 43 sessions over 12 months). To meet individual needs, the system was designed in a modular fashion including an intelligent control approach encompassing two input modalities, namely an MI-BCI and shoulder movements. RESULTS: After one year of training, the end user's MI-BCI performance ranged from 50% to 93% (average: 70.5%). The performance of the hybrid system was evaluated with different functional assessments. The user was able to transfer objects of the grasp-and-release-test and he succeeded in eating a pretzel stick, signing a document and eating an ice cream cone, which he was unable to do without the system. CONCLUSION: This proof-of-concept study has demonstrated that with the support of hybrid FES systems consisting of FES and a semiactive orthosis, restoring hand, finger and elbow function is possible in a tetraplegic end-user. Remarkably, even after one year of training and 415 MI-BCI runs, the end user's average BCI performance remained at about 70%. This supports the view that in high-level tetraplegic subjects, an initially moderate BCI performance cannot be improved by extensive training. However, this aspect has to be validated in future studies with a larger population.

Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS.

A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES) of the affected limb, BCI is expected to have an even greater benefit due to the contingency established between motor imagery and afferent, haptic feedback from stimulation. Yet, few studies have explored such an approach, presumably due to the difficulty in dissociating and thus decoding the hemodynamic response (HDR) between motor imagery and peripheral stimulation. Here, for the first time, we demonstrate that NIRS signals elicited by motor imagery can be reliably discriminated from those due to FES, by first performing a univariate analysis of the NIRS signals, and subsequently by multivariate pattern classification. Our results showing that robust classification of motor imagery from the rest condition is possible support previous findings that imagery could be used to drive a BCI based on NIRS. More importantly, we demonstrate for the first time the successful classification of motor imagery and FES, indicating that it is technically feasible to implement a contingent NIRS-BCI with FES.

Development of a non-invasive, multifunctional grasp neuroprosthesis and its evaluation in an individual with a high spinal cord injury.

Over the last decade the improvement of a missing hand function by application of neuroprostheses in particular the implantable Freehand system has been successfully shown in high spinal cord injured individuals. The clinically proven advantages of the Freehand system is its ease of use, the reproducible generation of two distinct functional grasp patterns and an analog control scheme based on movements of the contralateral shoulder. However, after the Freehand system is not commercially available for more than ten years, alternative grasp neuroprosthesis with a comparable functionality are still missing. Therefore, the aim of this study was to develop a non-invasive neuroprosthesis and to show that a degree of functional restoration can be provided to end users comparable to implanted devices. By introduction of an easy to handle forearm electrode sleeve the reproducible generation of two grasp patterns has been achieved. Generated grasp forces of the palmar grasp are in the range of the implanted system. Though pinch force of the lateral grasp is significantly lower, it can effectively used by a tetraplegic subject to perform functional tasks. The non-invasive grasp neuroprosthesis developed in this work may serve as an easy to apply and inexpensive way to restore a missing hand and finger function at any time after spinal cord injury.

Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI.

The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated.