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An active recursive state estimation framework for brain-interfaced typing systems

An active recursive state estimation framework for brain-interfaced typing systems Typing systems driven by noninvasive electroencephalogram (EEG)-based brain–computer interfaces (BCIs) can help people with severe communication disorders (including locked-in state) communicate. These systems mainly suffer from lack of sufficient accuracy and speed due to inefficient querying to surpass a hard pre-defined threshold. We introduce a novel recursive state estimation framework for BCI-based typing systems using active querying and stopping. Previously, we proposed a history-based objective called Momentum which is a function of posterior changes across sequences. In this paper, we first extend the definition of the Momentum, propose a unified framework that employs this extended Momentum objective both for querying and stopping. To provide a practical example, we employ a language-model-assisted EEG-based BCI typing system called RSVP Keyboard. Our results show that proposed framework on average improves the information transfer rate (ITR) and accuracy at least 52% and 8.7%, respectively, when compared to alternative approaches (random or mutual information). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

An active recursive state estimation framework for brain-interfaced typing systems

An active recursive state estimation framework for brain-interfaced typing systems

Abstract

Typing systems driven by noninvasive electroencephalogram (EEG)-based brain–computer interfaces (BCIs) can help people with severe communication disorders (including locked-in state) communicate. These systems mainly suffer from lack of sufficient accuracy and speed due to inefficient querying to surpass a hard pre-defined threshold. We introduce a novel recursive state estimation framework for BCI-based typing systems using active querying and stopping. Previously, we proposed a...
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Publisher
Taylor & Francis
Copyright
© 2020 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2020.1729652
Publisher site
See Article on Publisher Site

Abstract

Typing systems driven by noninvasive electroencephalogram (EEG)-based brain–computer interfaces (BCIs) can help people with severe communication disorders (including locked-in state) communicate. These systems mainly suffer from lack of sufficient accuracy and speed due to inefficient querying to surpass a hard pre-defined threshold. We introduce a novel recursive state estimation framework for BCI-based typing systems using active querying and stopping. Previously, we proposed a history-based objective called Momentum which is a function of posterior changes across sequences. In this paper, we first extend the definition of the Momentum, propose a unified framework that employs this extended Momentum objective both for querying and stopping. To provide a practical example, we employ a language-model-assisted EEG-based BCI typing system called RSVP Keyboard. Our results show that proposed framework on average improves the information transfer rate (ITR) and accuracy at least 52% and 8.7%, respectively, when compared to alternative approaches (random or mutual information).

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Oct 2, 2019

Keywords: Recursive state estimation; active querying; stopping criterion; BCI typing interface; RSVP Keyboard

References