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

Quantifying the impact on navigation performance in visually impaired: Auditory information loss versus information gain enabled through electronic travel aids

<div><p>This study’s purpose was to analyze and quantify the impact of auditory information loss versus information gain provided by electronic travel aids (ETAs) on navigation performance in people with low vision. Navigation performance of ten subjects (age: 54.9±11.2 years) with visual acuities >1.0 LogMAR was assessed via the Graz Mobility Test (GMT). Subjects passed through a maze in three different modalities: ‘Normal’ with visual and auditory information available, ‘Auditory Information Loss’ with artificially reduced hearing (leaving only visual information), and ‘ETA’ with a vibrating ETA based on ultrasonic waves, thereby facilitating visual, auditory, and tactile information. Main performance measures comprised passage time and number of contacts. Additionally, head tracking was used to relate head movements to motion direction. When comparing ‘Auditory Information Loss’ to ‘Normal’, subjects needed significantly more time (p<0.001), made more contacts (p<0.001), had higher relative viewing angles (p = 0.002), and a higher percentage of orientation losses (p = 0.011). The only significant difference when comparing ‘ETA’ to ‘Normal’ was a reduced number of contacts (p<0.001). Our study provides objective, quantifiable measures of the impact of reduced hearing on the navigation performance in low vision subjects. Significant effects of ‘Auditory Information Loss’ were found for all measures; for example, passage time increased by 17.4%. These findings show that low vision subjects rely on auditory information for navigation. In contrast, the impact of the ETA was not significant but further analysis of head movements revealed two different coping strategies: half of the subjects used the ETA to increase speed, whereas the other half aimed at avoiding contacts.</p></div

Endpoints.

<p>The four measured endpoints (Passage time, Contacts, MARVA, and Orientation loss) in the three modalities (‘Normal’ with visual and auditory information available, ‘Auditory information loss’ with only visual information, and ‘ETA’ with tactile, visual, and auditory information) for each subject.</p

Angle distributions for the two groups EF (A) and ES (B).

<p>The different behaviors of the two groups are reflected in the width of the distributions when comparing ‘Normal’ to ‘ETA’. Subjects who were faster with ‘ETA’ had narrower distributions which indicates that they spent more time in the lower angle range. The opposite effect can be observed in the ES group.</p

Subjects participating in the study.

<p>Subjects participating in the study.</p

Graz mobility test.

<p>The schematic shows the four different maze variants A, B, C, and D. Obstacles (small, medium, large, and the step) and the walls can be repositioned without effort between runs. Arrows show the entrance and exit of each variant, depending on the walking direction.</p