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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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Percentage of erroneous decisions made by each participant in the 224 trials of our experiment.Error rates ranged form 5% to over 20% with an average error rate of 12.5%.
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pone-0102693-g003: Percentage of erroneous decisions made by each participant in the 224 trials of our experiment.Error rates ranged form 5% to over 20% with an average error rate of 12.5%.

Mentions: The average error rate in the visual matching task used in our experiment across all participants was 12.5%. However, as one might expect, participants showed radically different individual levels of performance as illustrated in Figure 3, with error rates ranging from just below 5% to over 20%. Interestingly, if we look at the subset of trials where matching pairs of stimuli were presented, we see that participants gave incorrect decisions in only 0 or 1 out of the 28 matching pairs, thereby showing a very high sensitivity to identical sets. The bulk of the errors, instead, were due to participants having decided to classify as “matching” stimuli that actually did not match.


Collaborative brain-computer interface for aiding decision-making.

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

Percentage of erroneous decisions made by each participant in the 224 trials of our experiment.Error rates ranged form 5% to over 20% with an average error rate of 12.5%.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0102693-g003: Percentage of erroneous decisions made by each participant in the 224 trials of our experiment.Error rates ranged form 5% to over 20% with an average error rate of 12.5%.
Mentions: The average error rate in the visual matching task used in our experiment across all participants was 12.5%. However, as one might expect, participants showed radically different individual levels of performance as illustrated in Figure 3, with error rates ranging from just below 5% to over 20%. Interestingly, if we look at the subset of trials where matching pairs of stimuli were presented, we see that participants gave incorrect decisions in only 0 or 1 out of the 28 matching pairs, thereby showing a very high sensitivity to identical sets. The bulk of the errors, instead, were due to participants having decided to classify as “matching” stimuli that actually did not match.

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