Brain–computer interfacing: more than the sum of its parts

Abstract

The performance of non-invasive electroencephalogram-based (EEG) brain---computer interfaces (BCIs) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio of the EEG, which limit the bandwidth and hence the available applications. Optimization of both individual components of BCIs and the interrelationship between them is crucial to enhance bandwidth. In other words, neuroscientific knowledge and machine learning need to be optimized by considering concepts from human---computer interaction research and usability. In this paper, we present results of ongoing relevant research in our lab that addresses several important issues for BCIs based on the detection of transient changes in oscillatory EEG activity. First, we report on the long-term stability and robustness of detection of oscillatory EEG components modulated by distinct mental tasks, and show that the use of mental task pairs "mental subtraction versus motor imagery" achieves robust and reliable performance (Cohen's ? > 0.6) in seven out of nine subjects over a period of 4 days. Second, we report on restricted Boltzmann machines (RBMs) as promising tools for the recognition of oscillatory EEG patterns. In an off-line BCI simulation we computed average peak accuracies, averaged over ten subjects, of 80.8 ± 7.2 %. Third, we present the basic framework of the context-aware hybrid Graz-BCI that allows interacting with the massive multiplayer online role playing game World of Warcraft. We show how a more integrated design approach that considers all components of BCIs, their interrelationships, other input signals and contextual information can increase interaction efficacy.