Brain-Computer Interfacing: More than the sum of its parts
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 talk, I will discuss the big challenges in the field and review ongoing relevant research in our lab that addresses several important issues for BCIs based on the detection of transient changes in oscillatory EEG activity.