Literature DB >> 9749910

EEG-based communication: improved accuracy by response verification.

J R Wolpaw1, H Ramoser, D J McFarland, G Pfurtscheller.   

Abstract

Humans can learn to control the amplitude of electroencephalographic (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could provide a new augmentative communication channel for individuals with motor disabilities. In the present system, each dimension of cursor movement is controlled by a linear equation. While the intercept in the equation is continually updated, it does not perfectly eliminate the impact of spontaneous variations in EEG amplitude. This imperfection reduces the accuracy of cursor movement. We evaluated a response verification (RV) procedure in which each outcome is determined by two opposite trials (e.g., one top-target trial and one bottom-target trial). Success, or failure, on both is required for a definitive outcome. The RV procedure reduces errors due to imperfection in intercept selection. Accuracy for opposite-trial pairs exceeds that predicted from the accuracies of individual trials, and greatly exceeds that for same-trial pairs. The RV procedure should be particularly valuable when the first trial has >2 possible targets, because the second trial need only confirm or deny the outcome of the first, and it should be applicable to nonlinear as well as to linear algorithms.

Entities:  

Mesh:

Year:  1998        PMID: 9749910     DOI: 10.1109/86.712231

Source DB:  PubMed          Journal:  IEEE Trans Rehabil Eng        ISSN: 1063-6528


  55 in total

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

Authors:  N Jeremy Hill; Erin Ricci; Sameah Haider; Lynn M McCane; Susan Heckman; Jonathan R Wolpaw; Theresa M Vaughan
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6.  Performance measurement for brain-computer or brain-machine interfaces: a tutorial.

Authors:  David E Thompson; Lucia R Quitadamo; Luca Mainardi; Khalil Ur Rehman Laghari; Shangkai Gao; Pieter-Jan Kindermans; John D Simeral; Reza Fazel-Rezai; Matteo Matteucci; Tiago H Falk; Luigi Bianchi; Cynthia A Chestek; Jane E Huggins
Journal:  J Neural Eng       Date:  2014-05-19       Impact factor: 5.379

7.  Novel non-contact control system of electric bed for medical healthcare.

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8.  Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.

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Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2015-05-20       Impact factor: 10.961

9.  Different effects of using pictures as stimuli in a P300 brain-computer interface under rapid serial visual presentation or row-column paradigm.

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Journal:  Med Biol Eng Comput       Date:  2021-03-20       Impact factor: 2.602

10.  A general method for assessing brain-computer interface performance and its limitations.

Authors:  N Jeremy Hill; Ann-Katrin Häuser; Gerwin Schalk
Journal:  J Neural Eng       Date:  2014-03-24       Impact factor: 5.379

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