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 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).

KEYWORDS:

• Recursive state estimation
• active querying
• stopping criterion
• BCI typing interface
• RSVP Keyboard

Acknowledgments

We thank Bahar Azari for helping us in data collection.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by NSF (IIS-1149570, CNS-1544895, IIS-1715858, IIS-1717654, IIS-1844885, IIS-1915083, CMMI-1854827), DHHS (90RE5017-02-01), and NIH (R01DC009834).