BackgroundGoal-directed reaching for real-world objects by humans is enabled through visual depth cues. In virtual environments, the number and quality of available visual depth cues is limited, which may affect reaching performance and quality of reaching movements.MethodsWe assessed three-dimensional reaching movements in five experimental groups each with ten healthy volunteers. Three groups used a two-dimensional computer screen and two groups used a head-mounted display. The first screen group received the typically recreated visual depth cues, such as aerial and linear perspective, occlusion, shadows, and texture gradients. The second screen group received an abstract minimal rendering lacking those. The third screen group received the cues of the first screen group and absolute depth cues enabled by retinal image size of a known object, which realized with visual renderings of the handheld device and a ghost handheld at the target location. The two head-mounted display groups received the same virtually recreated visual depth cues as the second or the third screen group respectively. Additionally, they could rely on stereopsis and motion parallax due to head-movements.Results and conclusionAll groups using the screen performed significantly worse than both groups using the head-mounted display in terms of completion time normalized by the straight-line distance to the target. Both groups using the head-mounted display achieved the optimal minimum in number of speed peaks and in hand path ratio, indicating that our subjects performed natural movements when using a head-mounted display. Virtually recreated visual depth cues had a minor impact on reaching performance. Only the screen group with rendered handhelds could outperform the other screen groups. Thus, if reaching performance in virtual environments is in the main scope of a study, we suggest applying a head-mounted display. Otherwise, when two-dimensional screens are used, achievable performance is likely limited by the reduced depth perception and not just by subjects’ motor skills.

This paper presents a new approach to benchmarking brain-computer interfaces (BCIs) outside the lab. A computer game was created that mimics a real-world application of assistive BCIs, with the main outcome metric being the time needed to complete the game. This approach was used at the Cybathlon 2016, a competition for people with disabilities who use assistive technology to achieve tasks. The paper summarizes the technical challenges of BCIs, describes the design of the benchmarking game, then describes the rules for acceptable hardware, software and inclusion of human pilots in the BCI competition at the Cybathlon. The 11 participating teams, their approaches, and their results at the Cybathlon are presented. Though the benchmarking procedure has some limitations (for instance, we were unable to identify any factors that clearly contribute to BCI performance), it can be successfully used to analyze BCI performance in realistic, less structured conditions. In the future, the parameters of the benchmarking game could be modified to better mimic different applications (e.g., the need to use some commands more frequently than others). Furthermore, the Cybathlon has the potential to showcase such devices to the general public.

A multitude of robotic systems have been developed to foster motor learning. Some of these robotic systems featured augmented visual or haptic feedback, which was automatically adjusted to the trainee’s performance. However, selecting the type of feedback to achieve the training goal usually remained up to a human trainer. We automated this feedback selection within a robotic rowing simulator: Four spatial errors and one velocity error were considered, all related to trunk-arm sweep rowing set as the training goal to be learned. In an alternating sequence of assessments without augmented feedback and training sessions with augmented, concurrent feedback, the experimental group received feedback, thus addressing the main shortcoming of the previous assessment. With this approach, each participant of the experimental group received an individual sequence of 10 training sessions with feedback. The training sequences from participants in the experimental group were consecutively applied for participants in the control group. Both groups were able to reduce spatial and velocity errors due to training. The learning rate of the requested velocity profile was significantly higher for the experimental group compared with the control group. Thus, our robotic rowing simulator accelerated motor learning by automated feedback selection. This demonstration of a working, closed-loop selection of types of feedback, i.e., training conditions, could serve as the basis for other robotic trainers incorporating further human expertise and artificial intelligence.