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Improved inference and autotyping in EEG-based BCI typing systems

Published: 21 October 2013 Publication History

Abstract

The RSVP Keyboard™ is a brain-computer interface (BCI)-based typing system for people with severe physical disabilities, specifically those with locked-in syndrome (LIS). It uses signals from an electroencephalogram (EEG) combined with information from an n-gram language model to select letters to be typed. One characteristic of the system as currently configured is that it does not keep track of past EEG observations, i.e., observations of user intent made while the user was in a different part of a typed message. We present a principled approach for taking all past observations into account, and show that this method results in a 20% increase in simulated typing speed under a variety of conditions on realistic stimuli. We also show that this method allows for a principled and improved estimate of the probability of the backspace symbol, by which mis-typed symbols are corrected. Finally, we demonstrate the utility of automatically typing likely letters in certain contexts, a technique that achieves increased typing speed under our new method, though not under the baseline approach.

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cover image ACM Conferences
ASSETS '13: Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility
October 2013
343 pages
ISBN:9781450324052
DOI:10.1145/2513383
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 21 October 2013

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Author Tags

  1. brain computer interfaces
  2. language models
  3. text entry

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ASSETS '13 Paper Acceptance Rate 28 of 98 submissions, 29%;
Overall Acceptance Rate 436 of 1,556 submissions, 28%

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View all
  • (2023)Decode Brain Signal Into Thai Word Using EEG and L-SVM2023 18th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)10.1109/iSAI-NLP60301.2023.10354583(1-5)Online publication date: 27-Nov-2023
  • (2022)Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based ElectrodesResearch10.34133/2022/98304572022Online publication date: Jan-2022
  • (2022)Accessibility-Related Publication Distribution in HCI Based on a Meta-AnalysisExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519701(1-28)Online publication date: 27-Apr-2022
  • (2019)Brain Computer Interface for Neurodegenerative Person Using ElectroencephalogramIEEE Access10.1109/ACCESS.2018.28867087(2439-2452)Online publication date: 2019
  • (2018)Can Automatic Abbreviation Expansion Improve the Text Entry Rates of Norwegian Text with Compound Words?Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion10.1145/3218585.3218586(1-7)Online publication date: 20-Jun-2018
  • (2017)Teachable machines for accessibilityACM SIGACCESS Accessibility and Computing10.1145/3167902.3167904(10-18)Online publication date: 27-Nov-2017

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