Abstract-We propose an on-line hybrid BCI system that combines P300 and ERD. By employing both brain activity patterns (BAPs) in parallel and asynchronously, the system can issue different types of commands, for example, in robotic control scenarios. We present a method for reliably distinguishing between the two BAPs. We examined the level of false positives in P300 classification while a subject tries to evoke an ERD. We found this level to be as low as for regular P300 trials. Our system thus assumes the presence of ERD whenever classification of all P300 symbols is negative. Empirical results indicate that subjects can achieve good control over the hybrid BCI. In particular, subjects can switch spontaneously and reliably between the two BAPs.

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We present a study in which participants were trained in several sessions to control a (comparatively simple) robot via an EEG-/motor imagery-based Brain-Computer Interface (BCI). In the final (experiment) session pairs of participants were formed and each participant controlled one of two robots in a shared space. EEG data was recorded synchronously from both participants. We performed a joint data analysis on the datasets and found increases of phase-locking in μ- and θ-band. One such phase-locking effect appears to be time-locked to the start of the robotic action.

Data are increasingly important in central facets of modern life: academics, professions, and society at large. Educating aspiring minds to meet highest standards in these facets is the mandate of institutions of higher education. This, naturally, includes the preparation for excelling in today’s data-driven world. In recent years, an intensive academic discussion has resulted in the distinction between two different modes of data related education: data science and data literacy education. As a large number of study programs and offers is emerging around the world, data literacy in higher education is a particular focus of this paper. These programs, despite sharing the same name, differ substantially in their educational content, i.e., a high plurality can be observed. This paper explores this plurality, comments on the role it might play and suggests ways it can be dealt with by maintaining a high degree of adaptiveness and plurality while simultaneously establishing a consistent educational “essence”. It identifies a skill set, data self-empowerment, as a potential part of this essence. Data science and literacy education are still experiencing changeability in their emergence as fields of study, while additionally being stirred up by rapid developments, bringing about a need for flexibility and dialectic.