Literature DB >> 20457551

A dictionary-driven P300 speller with a modified interface.

Sercan Taha Ahi1, Hiroyuki Kambara, Yasuharu Koike.   

Abstract

P300 spellers are mainly composed of an interface, by which alphanumerical characters are presented to users, and a classification system, which identifies the target character by using acquired EEG data. In this study, we proposed modifications both to the interface and to the classification system, in order to reduce the number of required stimulus repetitions and consequently boost the information transfer rate. We initially incorporated a custom-built dictionary into the classification system, and conducted a study on 14 healthy subjects who copy-spelled 15 four letter words. Incorporating the dictionary, the mean accuracy at five trials increased from 72.86% to 95.71%. To further increase the system performance, we first validated the hypothesis that for a conventional P300 system, most target-error pairs lie on the same row or column. Then based on the validated hypothesis, we adjusted letter positions on the well-known from A to Z interface. The same subjects spelled the same 15 words using the modified interface as well, and the mean information transfer rate at two trials reached 55.32 bits/min.

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Mesh:

Year:  2010        PMID: 20457551     DOI: 10.1109/TNSRE.2010.2049373

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  7 in total

Review 1.  Brain computer interfaces, a review.

Authors:  Luis Fernando Nicolas-Alonso; Jaime Gomez-Gil
Journal:  Sensors (Basel)       Date:  2012-01-31       Impact factor: 3.576

2.  Word-level language modeling for P300 spellers based on discriminative graphical models.

Authors:  Jaime F Delgado Saa; Adriana de Pesters; Dennis McFarland; Müjdat Çetin
Journal:  J Neural Eng       Date:  2015-02-16       Impact factor: 5.379

Review 3.  Integrating language models into classifiers for BCI communication: a review.

Authors:  W Speier; C Arnold; N Pouratian
Journal:  J Neural Eng       Date:  2016-05-06       Impact factor: 5.379

4.  Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers.

Authors:  Boyla O Mainsah; Kenneth D Morton; Leslie M Collins; Eric W Sellers; Chandra S Throckmorton
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-11-25       Impact factor: 3.802

5.  Design of a 32-channel EEG system for brain control interface applications.

Authors:  Ching-Sung Wang
Journal:  J Biomed Biotechnol       Date:  2012-06-21

Review 6.  Language model applications to spelling with Brain-Computer Interfaces.

Authors:  Anderson Mora-Cortes; Nikolay V Manyakov; Nikolay Chumerin; Marc M Van Hulle
Journal:  Sensors (Basel)       Date:  2014-03-26       Impact factor: 3.576

Review 7.  Brain-Computer Interface Spellers: A Review.

Authors:  Aya Rezeika; Mihaly Benda; Piotr Stawicki; Felix Gembler; Abdul Saboor; Ivan Volosyak
Journal:  Brain Sci       Date:  2018-03-30
  7 in total

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