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Collaborative brain-computer interface for aiding decision-making.

Poli R, Valeriani D, Cinel C - PLoS ONE (2014)

Bottom Line: We then built a composite neuro-behavioural feature which optimally combines the two measures.For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features.Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule.

View Article: PubMed Central - PubMed

Affiliation: Brain-Computer Interfaces Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.

ABSTRACT
We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.

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Plots of stimulus-locked grand averages and p-values as in Figure 10 but for channels C3, C4, P5 and Pz (see caption of Figure 10 and text for more details).
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pone-0102693-g011: Plots of stimulus-locked grand averages and p-values as in Figure 10 but for channels C3, C4, P5 and Pz (see caption of Figure 10 and text for more details).

Mentions: Figures 10 and 11 show the stimulus-locked grand averages (averages of individual averages) of the ERPs recorded in our experiment for correct and incorrect responses for channels Fz, Cz, Pz, Oz, C3, C4, P5 and P6 (first and third rows) and the p-values of the statistical tests comparing the signals for correct and incorrect trials (second and fourth rows) in the period immediately following the onset of stimulus Set 2. Figures 12 and 13 show corresponding response-locked grand averages.


Collaborative brain-computer interface for aiding decision-making.

Poli R, Valeriani D, Cinel C - PLoS ONE (2014)

Plots of stimulus-locked grand averages and p-values as in Figure 10 but for channels C3, C4, P5 and Pz (see caption of Figure 10 and text for more details).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4114490&req=5

pone-0102693-g011: Plots of stimulus-locked grand averages and p-values as in Figure 10 but for channels C3, C4, P5 and Pz (see caption of Figure 10 and text for more details).
Mentions: Figures 10 and 11 show the stimulus-locked grand averages (averages of individual averages) of the ERPs recorded in our experiment for correct and incorrect responses for channels Fz, Cz, Pz, Oz, C3, C4, P5 and P6 (first and third rows) and the p-values of the statistical tests comparing the signals for correct and incorrect trials (second and fourth rows) in the period immediately following the onset of stimulus Set 2. Figures 12 and 13 show corresponding response-locked grand averages.

Bottom Line: We then built a composite neuro-behavioural feature which optimally combines the two measures.For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features.Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule.

View Article: PubMed Central - PubMed

Affiliation: Brain-Computer Interfaces Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.

ABSTRACT
We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.

Show MeSH