Incorporation of a language model into a brain computer interface based speller through HMMs

Brain computer interface (BCI) research deals with the problem of establishing direct communication pathways between the brain and external devices. The primary motivation is to enable patients with limited or no muscular control to use external devices by automatically interpreting their intent based on brain electrical activity, measured by, e.g., electroencephalography (EEG). A widely studied BCI set up involves having subjects type letters based on so-called P300 signals generated by their brains in response to visual stimuli. Due to the low signal-to-noise ratio (SNR) of EEG signals, brain signals generated for a single letter often have to be recorded many times to obtain acceptable accuracy, which reduces the typing speed of the system. Conventionally the measured signals for each letter are processed and classified separately. However, in the context of typing letters within words in a particular language, neighboring letters would provide information about the current letter as well. Based on this observation, we propose an approach for incorporation of such information into a BCI-based speller through hidden Markov models (HMM) trained by a language model. We then describe filtering and smoothing algorithms for inference over such a model. Experiments on real EEG data collected in our laboratory demonstrate that incorporation of the language model in this manner results in significant improvements in classification accuracy and bit rate.