An analytic spatial filter and a hidden Markov model for enhanced information transfer rate in EEG-based brain computer interfaces

We propose a new classification method, termed the Common Spatial Analytic Pattern, for brain-computer interfaces based on a simple EEG signal source and channel model. This blind source separation procedure recovers underlying source signals near the motor cortex which are indicative of motor imagery. A hidden Markov source model is applied to the evolution of the source signals and is used to estimate the type (left or right) of motor imagery performed by a subject. As a whole, the resulting asynchronous classifier offers significant improvement upon the current prevailing techniques in classification. Experiments show information transfer rates between subject and computer as high as 60.9 bits/minute.