Literature DB >> 21067970

The P300-based brain-computer interface (BCI): effects of stimulus rate.

Dennis J McFarland1, William A Sarnacki, George Townsend, Theresa Vaughan, Jonathan R Wolpaw.   

Abstract

OBJECTIVE: Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate.
METHODS: In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time.
RESULTS: For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results.
CONCLUSIONS: The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance. SIGNIFICANCE: These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.
Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2010        PMID: 21067970      PMCID: PMC3050994          DOI: 10.1016/j.clinph.2010.10.029

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  22 in total

Review 1.  Brain-computer interfaces for communication and control.

Authors:  Jonathan R Wolpaw; Niels Birbaumer; Dennis J McFarland; Gert Pfurtscheller; Theresa M Vaughan
Journal:  Clin Neurophysiol       Date:  2002-06       Impact factor: 3.708

2.  Which physiological components are more suitable for visual ERP based brain-computer interface? A preliminary MEG/EEG study.

Authors:  Luigi Bianchi; Saber Sami; Arjan Hillebrand; Ian P Fawcett; Lucia Rita Quitadamo; Stefano Seri
Journal:  Brain Topogr       Date:  2010-04-20       Impact factor: 3.020

3.  A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.

Authors:  G Townsend; B K LaPallo; C B Boulay; D J Krusienski; G E Frye; C K Hauser; N E Schwartz; T M Vaughan; J R Wolpaw; E W Sellers
Journal:  Clin Neurophysiol       Date:  2010-03-26       Impact factor: 3.708

4.  A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.

Authors:  Eric W Sellers; Dean J Krusienski; Dennis J McFarland; Theresa M Vaughan; Jonathan R Wolpaw
Journal:  Biol Psychol       Date:  2006-07-24       Impact factor: 3.251

5.  A comparison of classification techniques for the P300 Speller.

Authors:  Dean J Krusienski; Eric W Sellers; François Cabestaing; Sabri Bayoudh; Dennis J McFarland; Theresa M Vaughan; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2006-10-26       Impact factor: 5.379

Review 6.  A review of classification algorithms for EEG-based brain-computer interfaces.

Authors:  F Lotte; M Congedo; A Lécuyer; F Lamarche; B Arnaldi
Journal:  J Neural Eng       Date:  2007-01-31       Impact factor: 5.379

Review 7.  Updating P300: an integrative theory of P3a and P3b.

Authors:  John Polich
Journal:  Clin Neurophysiol       Date:  2007-06-18       Impact factor: 3.708

8.  Toward enhanced P300 speller performance.

Authors:  D J Krusienski; E W Sellers; D J McFarland; T M Vaughan; J R Wolpaw
Journal:  J Neurosci Methods       Date:  2007-08-01       Impact factor: 2.390

9.  Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.

Authors:  S M M Martens; N J Hill; J Farquhar; B Schölkopf
Journal:  J Neural Eng       Date:  2009-03-02       Impact factor: 5.379

10.  Age, physical fitness, and attention: P3a and P3b.

Authors:  Matthew B Pontifex; Charles H Hillman; John Polich
Journal:  Psychophysiology       Date:  2009-01-26       Impact factor: 4.016

View more
  26 in total

1.  Integrating language information with a hidden Markov model to improve communication rate in the P300 speller.

Authors:  William Speier; Corey Arnold; Jessica Lu; Aniket Deshpande; Nader Pouratian
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-01-21       Impact factor: 3.802

2.  Incorporating advanced language models into the P300 speller using particle filtering.

Authors:  W Speier; C W Arnold; A Deshpande; J Knall; N Pouratian
Journal:  J Neural Eng       Date:  2015-06-10       Impact factor: 5.379

3.  A comparison study of two P300 speller paradigms for brain-computer interface.

Authors:  Jiahui Pan; Yuanqing Li; Zhenghui Gu; Zhuliang Yu
Journal:  Cogn Neurodyn       Date:  2013-04-16       Impact factor: 5.082

4.  Should the parameters of a BCI translation algorithm be continually adapted?

Authors:  Dennis J McFarland; William A Sarnacki; Jonathan R Wolpaw
Journal:  J Neurosci Methods       Date:  2011-05-06       Impact factor: 2.390

5.  Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.

Authors:  William Speier; Nand Chandravadia; Dustin Roberts; S Pendekanti; Nader Pouratian
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2016-11-15

Review 6.  Brain-computer interfaces for amyotrophic lateral sclerosis.

Authors:  Dennis J McFarland
Journal:  Muscle Nerve       Date:  2020-06       Impact factor: 3.217

Review 7.  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

8.  The effects of stimulus timing features on P300 speller performance.

Authors:  Jessica Lu; William Speier; Xiao Hu; Nader Pouratian
Journal:  Clin Neurophysiol       Date:  2012-08-29       Impact factor: 3.708

9.  Natural language processing with dynamic classification improves P300 speller accuracy and bit rate.

Authors:  William Speier; Corey Arnold; Jessica Lu; Ricky K Taira; Nader Pouratian
Journal:  J Neural Eng       Date:  2011-12-12       Impact factor: 5.379

10.  A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems.

Authors:  William Speier; Aniket Deshpande; Nader Pouratian
Journal:  Clin Neurophysiol       Date:  2014-09-28       Impact factor: 3.708

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.