Recursive Bayesian Coding for BCIs

Abstract

Brain Computer Interfaces (BCI) 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.

Effects of simulated visual acuity and ocular motility impairments on SSVEP brain-computer interface performance: an experiment with Shuffle Speller

“…Shuffle Speller is an algorithm that adapts to an individual user’s unique abilities to improve typing performance. While full technical details are reported in previous work, [26] we describe here the features of Shuffle Speller which are relevant to mitigating the effects of visual impairments, and how the user interface was configured for this study. See supplemental online materials for videos and additional information.…”

“…Shuffle Speller is a typing interface which makes the most of the control signals produced by individual users. [26] Its algorithm learns each user’s unique pattern of responses and errors, and adapts stimulus presentation accordingly. It aggregates results from multiple user selections before typing a letter, increasing the likelihood that the letter will match the user’s intent.…”

Behind the Scenes of Noninvasive Brain–Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

“…Therefore, the BCI decoding algorithm selects the target item by identifying the stimuli associated with the largest magnitude of response during the trial. With the SSVEP Shuffle Speller (Higger et al, 2017), the individual selects different boxes of letters, each flickering at their own specific frequency. Through a language model, selections are made until the individual has only one letter left to select.…”

Ethical Considerations in Ending Exploratory Brain–Computer Interface Research Studies in Locked-in Syndrome

“…The study team developed a system to aggregate repeated, error-prone trials into a single, accurate response selection. 19 The first modality attempted was an eyegaze-based interface given that the participant's strongest apparent input signal was his horizontal eye movement. A system using vertical bars of different colors moving horizontally across the screen as a means for binary yes/no communication (i.e., tracking a green bar indicating "yes" or red bar indicating "no") was constructed, but was ultimately unsuccessful.…”