Literature DB >> 24838278

A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening.

N Jeremy Hill1, Erin Ricci, Sameah Haider, Lynn M McCane, Susan Heckman, Jonathan R Wolpaw, Theresa M Vaughan.   

Abstract

OBJECTIVE: Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state. APPROACH: We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions. MAIN
RESULTS: The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects. SIGNIFICANCE: Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.

Entities:  

Mesh:

Year:  2014        PMID: 24838278      PMCID: PMC4096243          DOI: 10.1088/1741-2560/11/3/035003

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  18 in total

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Authors:  Gerwin Schalk; Dennis J McFarland; Thilo Hinterberger; Niels Birbaumer; Jonathan R Wolpaw
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2.  Varieties of the locked-in syndrome.

Authors:  G Bauer; F Gerstenbrand; E Rumpl
Journal:  J Neurol       Date:  1979-08       Impact factor: 4.849

3.  Interactions between pre-processing and classification methods for event-related-potential classification: best-practice guidelines for brain-computer interfacing.

Authors:  J Farquhar; N J Hill
Journal:  Neuroinformatics       Date:  2013-04

4.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.

Authors:  L A Farwell; E Donchin
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1988-12

5.  Electrical signs of selective attention in the human brain.

Authors:  S A Hillyard; R F Hink; V L Schwent; T W Picton
Journal:  Science       Date:  1973-10-12       Impact factor: 47.728

Review 6.  The mismatch negativity: a powerful tool for cognitive neuroscience.

Authors:  R Näätänen
Journal:  Ear Hear       Date:  1995-02       Impact factor: 3.570

7.  Toward a high-throughput auditory P300-based brain-computer interface.

Authors:  D S Klobassa; T M Vaughan; P Brunner; N E Schwartz; J R Wolpaw; C Neuper; E W Sellers
Journal:  Clin Neurophysiol       Date:  2009-07-01       Impact factor: 3.708

8.  An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli.

Authors:  N J Hill; B Schölkopf
Journal:  J Neural Eng       Date:  2012-02-15       Impact factor: 5.379

9.  Communication and control by listening: toward optimal design of a two-class auditory streaming brain-computer interface.

Authors:  N Jeremy Hill; Aisha Moinuddin; Ann-Katrin Häuser; Stephan Kienzle; Gerwin Schalk
Journal:  Front Neurosci       Date:  2012-12-19       Impact factor: 4.677

10.  A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System.

Authors:  Johannes Höhne; Martijn Schreuder; Benjamin Blankertz; Michael Tangermann
Journal:  Front Neurosci       Date:  2011-08-22       Impact factor: 4.677

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  22 in total

Review 1.  Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.

Authors:  Kevin M Pitt; Jonathan S Brumberg
Journal:  Am J Speech Lang Pathol       Date:  2018-08-06       Impact factor: 2.408

Review 2.  Brain-Computer Interfaces for Augmentative and Alternative Communication: A Tutorial.

Authors:  Jonathan S Brumberg; Kevin M Pitt; Alana Mantie-Kozlowski; Jeremy D Burnison
Journal:  Am J Speech Lang Pathol       Date:  2018-02-06       Impact factor: 2.408

3.  P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.

Authors:  Lynn M McCane; Susan M Heckman; Dennis J McFarland; George Townsend; Joseph N Mak; Eric W Sellers; Debra Zeitlin; Laura M Tenteromano; Jonathan R Wolpaw; Theresa M Vaughan
Journal:  Clin Neurophysiol       Date:  2015-02-07       Impact factor: 3.708

4.  Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.

Authors:  Lynn M McCane; Eric W Sellers; Dennis J McFarland; Joseph N Mak; C Steve Carmack; Debra Zeitlin; Jonathan R Wolpaw; Theresa M Vaughan
Journal:  Amyotroph Lateral Scler Frontotemporal Degener       Date:  2014-02-20       Impact factor: 4.092

5.  Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

Authors:  Jonathan S Brumberg; Anh Nguyen; Kevin M Pitt; Sean D Lorenz
Journal:  Disabil Rehabil Assist Technol       Date:  2018-01-31

6.  Behind the Scenes of Noninvasive Brain-Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter.

Authors:  Kevin M Pitt; Jonathan S Brumberg; Jeremy D Burnison; Jyutika Mehta; Juhi Kidwai
Journal:  Perspect ASHA Spec Interest Groups       Date:  2019-11-09

7.  A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids.

Authors:  Han-Jeong Hwang; Valeria Y Ferreria; Daniel Ulrich; Tayfun Kilic; Xenofon Chatziliadis; Benjamin Blankertz; Matthias Treder
Journal:  Sci Rep       Date:  2015-10-29       Impact factor: 4.379

8.  An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary.

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Journal:  Front Neurosci       Date:  2016-09-30       Impact factor: 4.677

9.  Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis.

Authors:  Jonathan R Wolpaw; Richard S Bedlack; Domenic J Reda; Robert J Ringer; Patricia G Banks; Theresa M Vaughan; Susan M Heckman; Lynn M McCane; Charles S Carmack; Stefan Winden; Dennis J McFarland; Eric W Sellers; Hairong Shi; Tamara Paine; Donald S Higgins; Albert C Lo; Huned S Patwa; Katherine J Hill; Grant D Huang; Robert L Ruff
Journal:  Neurology       Date:  2018-06-27       Impact factor: 11.800

10.  Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: a case study with a participant in the locked-in state.

Authors:  Ivo Käthner; Andrea Kübler; Sebastian Halder
Journal:  J Neuroeng Rehabil       Date:  2015-09-04       Impact factor: 4.262

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