Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models

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...

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Bibliographic Details
Published in:arXiv.org 2022-10
Main Authors: Smedemark-Margulies, Niklas, Celik, Basak, Tales Imbiriba, Aziz Kocanaogullari, Erdogmus, Deniz
Format: Article
Language:English
Subjects:
Electroencephalography
Iterative methods
Neural networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422