Individually adapted imagery improves brain-computer interface performance in end-users with disability

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage

Neurophysiological methods for monitoring brain activity in serious games and virtual environments: a review

The use of serious games and virtual environments for learning is increasing worldwide. These technologies have the potential to collect live data from users through game-play and can be combined with neuroscientific methods such as EEG, fNIRS and fMRI. The several learning processes triggered by serious games are associated with specific patterns of activation that distributed in time and space over different neural networks. This paper explores the opportunities offered and challenges posed by neuroscientific methods when capturing user feedback and using the data to create greater user adaptivity in-game. Existing neuroscientific studies examining cortical correlates of game-based learning do not form a common or homogenous field. In contrast, they often have disparate research questions and are represented through a broad range of study designs and game genres. In this article, the range of studies and applications of neuroscientific methods in gamebased learning are reviewed