Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models

TL;DR

This paper introduces a discriminative neural network-based recursive Bayesian method for inferring user intent from noninvasive EEG signals to improve communication for individuals with severe speech impairments.

Contribution

It presents a novel discriminative model for updating symbol probabilities in EEG-based typing, outperforming previous generative approaches.

Findings

Discriminative model improves symbol probability estimation accuracy.

Method outperforms previous generative models in simulated typing tasks.

Neural network approximates Bayesian updates effectively.

Abstract

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.