Literature DB >> 16054256

Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system.

Brendan Z Allison1, Jaime A Pineda.   

Abstract

P3 brain-computer interfaces (BCIs) are synchronous communication systems that allow users to communicate interest in a target event by choosing to attend to it while ignoring other events. In such a system, a cogneme refers to the user's response to: "/attend to the event/" or "/ignore the event/". The present study examined subjects' ability to generate more cognemes per minute (by varying stimulus onset asynchrony or SOA), or requiring fewer cognemes to convey a message (by varying the pattern of stimulus presentation). Both of these have implications for improved information throughput in a P3 BCI. SOAs of 125, 250, and 500 ms were used. Additionally, the conventional "single flash" approach was compared to a new "multiple flash" condition in which half of the stimuli in an 8 x 8 grid were flashed simultaneously. In both conditions, P3-like component amplitudes decreased with faster SOAs at low target probabilities, but the trend did not hold for higher probabilities. The multiple flash condition produced more robust ERPs at the faster speeds. The results also indicate that attend/ignore differences were more apparent following multiple flashes for low target probabilities, but less apparent for high target probabilities. Although information throughput alone does not support the superiority of one approach over the other, only six cognemes are needed in the multiple flash conditions to identify a character, compared to sixteen cognemes in the single flash condition. This suggests that the former approach could operate more rapidly. Thus, the present results suggest that the multiple flash approach may be a more efficient and faster basis for a P3 BCI system.

Mesh:

Year:  2005        PMID: 16054256     DOI: 10.1016/j.ijpsycho.2005.02.007

Source DB:  PubMed          Journal:  Int J Psychophysiol        ISSN: 0167-8760            Impact factor:   2.997


  21 in total

1.  Towards an independent brain-computer interface using steady state visual evoked potentials.

Authors:  Brendan Z Allison; Dennis J McFarland; Gerwin Schalk; Shi Dong Zheng; Melody Moore Jackson; Jonathan R Wolpaw
Journal:  Clin Neurophysiol       Date:  2008-02       Impact factor: 3.708

2.  Amplitude modulation of steady-state visual evoked potentials by event-related potentials in a working memory task.

Authors:  Zhenghua Wu; Dezhong Yao; Yu Tang; Yilan Huang; Sheng Su
Journal:  J Biol Phys       Date:  2009-12-04       Impact factor: 1.365

3.  Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface.

Authors:  Jing Jin; Brendan Z Allison; Eric W Sellers; Clemens Brunner; Petar Horki; Xingyu Wang; Christa Neuper
Journal:  Med Biol Eng Comput       Date:  2010-10-02       Impact factor: 2.602

4.  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

5.  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

6.  Novel hold-release functionality in a P300 brain-computer interface.

Authors:  R E Alcaide-Aguirre; J E Huggins
Journal:  J Neural Eng       Date:  2014-11-07       Impact factor: 5.379

7.  Suppressing flashes of items surrounding targets during calibration of a P300-based brain-computer interface improves performance.

Authors:  G E Frye; C K Hauser; G Townsend; E W Sellers
Journal:  J Neural Eng       Date:  2011-03-24       Impact factor: 5.379

8.  A tactile P300 brain-computer interface.

Authors:  Anne-Marie Brouwer; Jan B F van Erp
Journal:  Front Neurosci       Date:  2010-05-06       Impact factor: 4.677

9.  (C)overt attention and visual speller design in an ERP-based brain-computer interface.

Authors:  Matthias S Treder; Benjamin Blankertz
Journal:  Behav Brain Funct       Date:  2010-05-28       Impact factor: 3.759

10.  A general P300 brain-computer interface presentation paradigm based on performance guided constraints.

Authors:  George Townsend; Jessica Shanahan; David B Ryan; Eric W Sellers
Journal:  Neurosci Lett       Date:  2012-08-29       Impact factor: 3.046

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