title: Recursive Bayesian Coding for BCIs.
abstract: Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g., language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g., P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP "Shuffle" Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier's mistakes across a particular user's SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.
authors.forename: Matt
authors.initials: M
authors.lastname: Higger
authors.forename: Fernando
authors.initials: F
authors.lastname: Quivira
authors.forename: Murat
authors.initials: M
authors.lastname: Akcakaya
authors.forename: Mohammad
authors.initials: M
authors.lastname: Moghadamfalahi
authors.forename: Hooman
authors.initials: H
authors.lastname: Nezamfar
authors.forename: Mujdat
authors.initials: M
authors.lastname: Cetin
authors.forename: Deniz
authors.initials: D
authors.lastname: Erdogmus
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology; D008297:Male; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D013647:Task Performance and Analysis; D014796:Visual Perception / Q000502:physiology*; D015443:Word Processing / Q000379:methods*