  We study the problem of inferring user intent from noninvasive
electroencephalography (EEG) to restore communication for people with severe
speech and physical impairments (SSPI). The focus of this work is improving the
estimation of posterior symbol probabilities in a typing task. At each
iteration of the typing procedure, a subset of symbols is chosen for the next
query based on the current probability estimate. Evidence about the user's
response is collected from event-related potentials (ERP) in order to update
symbol probabilities, until one symbol exceeds a predefined confidence
threshold. We provide a graphical model describing this task, and derive a
recursive Bayesian update rule based on a discriminative probability over label
vectors for each query, which we approximate using a neural network classifier.
We evaluate the proposed method in a simulated typing task and show that it
outperforms previous approaches based on generative modeling.
