The use of MEG-based brain computer interface for classification of wrist movements in four different directions

A brain-computer interface (BCI) is a communication system that does not require any peripheral muscular activity. Such interfaces can be considered as being the only way of communication for people affected by a number of motor disabilities. Many recent studies have demonstrated that BCIs based on Electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG, and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study we will validate signal processing and classification methods for Brain-Computer Interfaces to classify the direction of wrist movements using brain activity that was recorded with MEG from two healthy, right-handed subjects.