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We study the problem of inferring user intent from noninvasiveelectroencephalography (EEG) to restore communication for people with severespeech and physical impairments (SSPI). The focus of this work is improving theestimation of posterior symbol probabilities in a typing task. At eachiteration of the typing procedure, a subset of symbols is chosen for the nextquery based on the current probability estimate. Evidence about the user'sresponse is collected from event-related potentials (ERP) in order to updatesymbol probabilities, until one symbol exceeds a predefined confidencethreshold. We provide a graphical model describing this task, and derive arecursive Bayesian update rule based on a discriminative probability over labelvectors for each query, which we approximate using a neural network classifier.We evaluate the proposed method in a simulated typing task and show that itoutperforms previous approaches based on generative modeling.

Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models