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A tactile P300 brain-computer interface.

Brouwer AM, van Erp JB - Front Neurosci (2010)

Bottom Line: We did not find a difference in SWLDA classification performance between the different numbers of tactors.In a second set of experiments we reduced the stimulus onset asynchrony (SOA) by shortening the on- and/or off-time of the tactors.The SOA for an optimum performance as measured in our experiments turned out to be close to conventional SOAs of visual P300 BCIs.

View Article: PubMed Central - PubMed

Affiliation: TNO Human Factors Soesterberg, Netherlands. anne-marie.brouwer@tno.nl

ABSTRACT
In this study, we investigated a Brain-Computer Interface (BCI) based on EEG responses to vibro-tactile stimuli around the waist. P300 BCIs based on tactile stimuli have the advantage of not taxing the visual or auditory system and of being potentially unnoticeable to other people. A tactile BCI could be especially suitable for patients whose vision or eye movements are impaired. In Experiment 1, we investigated its feasibility and the effect of the number of equally spaced tactors. Whereas a large number of tactors is expected to enhance the P300 amplitude since the target will be less frequent, it could also negatively affect the P300 since it will be difficult to identify the target when tactor density increases. Participants were asked to attend to the vibrations of a target tactor, embedded within a stream of distracters. The number of tactors was two, four or six. We demonstrated the feasibility of a tactile P300 BCI. We did not find a difference in SWLDA classification performance between the different numbers of tactors. In a second set of experiments we reduced the stimulus onset asynchrony (SOA) by shortening the on- and/or off-time of the tactors. The SOA for an optimum performance as measured in our experiments turned out to be close to conventional SOAs of visual P300 BCIs.

No MeSH data available.


Mean performance of the classification model in the four conditions of Experiment 2b as expressed by classification accuracy (A), classification accuracy corrected for chance (B) and bitrate (C). The stars in (A) indicate chance performance. LL: Long On–Long Off, LS: Long On–Short Off, MM: Medium On–Medium Off, MS: Medium On–Short Off. Error bars represent standard errors of the mean.
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Figure 6: Mean performance of the classification model in the four conditions of Experiment 2b as expressed by classification accuracy (A), classification accuracy corrected for chance (B) and bitrate (C). The stars in (A) indicate chance performance. LL: Long On–Long Off, LS: Long On–Short Off, MM: Medium On–Medium Off, MS: Medium On–Short Off. Error bars represent standard errors of the mean.

Mentions: Figure 6A shows the classification accuracy for each condition and Figure 6B the classification accuracy corrected for chance. One-sample t-tests against zero on classification accuracy corrected for chance revealed that in all conditions except for the Medium On – Short Off condition (p = 0.052), classification performance was significantly higher than chance (p = 0.046 for Medium On–Medium Off, other p-values < 0.01). A repeated measures ANOVA showed a significant effect of condition [F(3,30) = 2.98; p < 0.047]. Tukey post-hoc tests did not show significant differences, although the differences between condition Long on–Long Off compared to conditions Medium On–Medium Off and Medium On–Short Off approached significance (p = 0.10 and p = 0.07 respectively). Figure 6C shows the bitrate and Table 1 the numerical values. A repeated measures ANOVA indicated no effect of condition on bitrate [F(3,30) = 0.57; p = 0.64]. In every condition of Experiment 3, except for the Medium On–Short Off condition, at least one participant achieved the maximal achievable bitrate.


A tactile P300 brain-computer interface.

Brouwer AM, van Erp JB - Front Neurosci (2010)

Mean performance of the classification model in the four conditions of Experiment 2b as expressed by classification accuracy (A), classification accuracy corrected for chance (B) and bitrate (C). The stars in (A) indicate chance performance. LL: Long On–Long Off, LS: Long On–Short Off, MM: Medium On–Medium Off, MS: Medium On–Short Off. Error bars represent standard errors of the mean.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2871714&req=5

Figure 6: Mean performance of the classification model in the four conditions of Experiment 2b as expressed by classification accuracy (A), classification accuracy corrected for chance (B) and bitrate (C). The stars in (A) indicate chance performance. LL: Long On–Long Off, LS: Long On–Short Off, MM: Medium On–Medium Off, MS: Medium On–Short Off. Error bars represent standard errors of the mean.
Mentions: Figure 6A shows the classification accuracy for each condition and Figure 6B the classification accuracy corrected for chance. One-sample t-tests against zero on classification accuracy corrected for chance revealed that in all conditions except for the Medium On – Short Off condition (p = 0.052), classification performance was significantly higher than chance (p = 0.046 for Medium On–Medium Off, other p-values < 0.01). A repeated measures ANOVA showed a significant effect of condition [F(3,30) = 2.98; p < 0.047]. Tukey post-hoc tests did not show significant differences, although the differences between condition Long on–Long Off compared to conditions Medium On–Medium Off and Medium On–Short Off approached significance (p = 0.10 and p = 0.07 respectively). Figure 6C shows the bitrate and Table 1 the numerical values. A repeated measures ANOVA indicated no effect of condition on bitrate [F(3,30) = 0.57; p = 0.64]. In every condition of Experiment 3, except for the Medium On–Short Off condition, at least one participant achieved the maximal achievable bitrate.

Bottom Line: We did not find a difference in SWLDA classification performance between the different numbers of tactors.In a second set of experiments we reduced the stimulus onset asynchrony (SOA) by shortening the on- and/or off-time of the tactors.The SOA for an optimum performance as measured in our experiments turned out to be close to conventional SOAs of visual P300 BCIs.

View Article: PubMed Central - PubMed

Affiliation: TNO Human Factors Soesterberg, Netherlands. anne-marie.brouwer@tno.nl

ABSTRACT
In this study, we investigated a Brain-Computer Interface (BCI) based on EEG responses to vibro-tactile stimuli around the waist. P300 BCIs based on tactile stimuli have the advantage of not taxing the visual or auditory system and of being potentially unnoticeable to other people. A tactile BCI could be especially suitable for patients whose vision or eye movements are impaired. In Experiment 1, we investigated its feasibility and the effect of the number of equally spaced tactors. Whereas a large number of tactors is expected to enhance the P300 amplitude since the target will be less frequent, it could also negatively affect the P300 since it will be difficult to identify the target when tactor density increases. Participants were asked to attend to the vibrations of a target tactor, embedded within a stream of distracters. The number of tactors was two, four or six. We demonstrated the feasibility of a tactile P300 BCI. We did not find a difference in SWLDA classification performance between the different numbers of tactors. In a second set of experiments we reduced the stimulus onset asynchrony (SOA) by shortening the on- and/or off-time of the tactors. The SOA for an optimum performance as measured in our experiments turned out to be close to conventional SOAs of visual P300 BCIs.

No MeSH data available.