STRUM: A New Dataset for Neuroergonomics Research

The past decade has seen a gradual expansion of Brain-Computer Interface applications from their clinical roots into entertainment, automotive, workplace, and military domains. However, many of these new-found applications have yet to pass the prototype stage, among others due to challenges posed by real-world data noise levels and increased context variability and complexity. Tackling these challenges requires sufficiently realistic and rich datasets that allow for benchmarking competing approaches, and in this paper, we present a task battery modeled after a complex real-world scenario, together with a new open dataset for BCI research. Results from an exemplary analysis of a slice of this large trove of data are presented, and it is found that the data mirror some of the challenges encountered in real-world deployments, with various well-known BCI algorithms showing a pronounced performance differential to highly simplified lab experiments. The results also show significant performance differences among alternative methods, indicating possible trajectories for future improvements BCI methodology applied to complex real-world contexts.