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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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Stimulus sequence used in our experiment.In each trial, a fixation cross was displayed for 1000(a first stimulus composed by three shapes) was presented for 83 ms, followed by a mask (for 250 ms), a black screen (for 1000 ms) and then Set 2 (a second stimulus structurally similar to the first) for 100 ms. The response time, RT, was computed from the onset of Set 2.
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pone-0102693-g001: Stimulus sequence used in our experiment.In each trial, a fixation cross was displayed for 1000(a first stimulus composed by three shapes) was presented for 83 ms, followed by a mask (for 250 ms), a black screen (for 1000 ms) and then Set 2 (a second stimulus structurally similar to the first) for 100 ms. The response time, RT, was computed from the onset of Set 2.

Mentions: Participants underwent a sequence of 8 blocks of trials, each block containing 28 trials, for a total of 224 trials. Each trial (see Figure 1) started with the presentation of a fixation cross in the middle of the screen for 1 second. This time allowed participants to get ready for the presentation of the stimuli and allowed EEG signals to get back to baseline after the response from previous trials. Then observers were presented with a sequence of two displays, each showing a set of shapes. The first set (Set 1) was presented for 83 ms (5 frames of a 60 Hz screen) and was immediately followed by a mask for 250 ms. The mask was a vertical sinusoidal grating with a period of 1 degree subtending approximately 8 degrees. After a delay of 1 second, the second set of stimuli (Set 2) was shown for 100 ms. Following this, observers had to decide, as quickly as possible, whether or not the two sets were identical. Responses were given with the two mouse buttons (left for “identical”, right for “different”), controlled with the right hand, and response times (RTs, expressed in seconds) were recorded (more on this later). Each set consisted of three shapes (subtending approximately 1.5 degrees and being approximately 1.8 degrees apart), which could be any combination of a triangle, square and pentagon (see Sets 1 and 2 in Figure 1). Note that the same shape was allowed to be present multiple times within a set. Each shape was coloured either in pure white (corresponding to normalised RGB (1,1,1)) or light grey (RGB (0.65,0.65,0.65)). Shapes were presented on a black background.


Collaborative brain-computer interface for aiding decision-making.

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

Stimulus sequence used in our experiment.In each trial, a fixation cross was displayed for 1000(a first stimulus composed by three shapes) was presented for 83 ms, followed by a mask (for 250 ms), a black screen (for 1000 ms) and then Set 2 (a second stimulus structurally similar to the first) for 100 ms. The response time, RT, was computed from the onset of Set 2.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0102693-g001: Stimulus sequence used in our experiment.In each trial, a fixation cross was displayed for 1000(a first stimulus composed by three shapes) was presented for 83 ms, followed by a mask (for 250 ms), a black screen (for 1000 ms) and then Set 2 (a second stimulus structurally similar to the first) for 100 ms. The response time, RT, was computed from the onset of Set 2.
Mentions: Participants underwent a sequence of 8 blocks of trials, each block containing 28 trials, for a total of 224 trials. Each trial (see Figure 1) started with the presentation of a fixation cross in the middle of the screen for 1 second. This time allowed participants to get ready for the presentation of the stimuli and allowed EEG signals to get back to baseline after the response from previous trials. Then observers were presented with a sequence of two displays, each showing a set of shapes. The first set (Set 1) was presented for 83 ms (5 frames of a 60 Hz screen) and was immediately followed by a mask for 250 ms. The mask was a vertical sinusoidal grating with a period of 1 degree subtending approximately 8 degrees. After a delay of 1 second, the second set of stimuli (Set 2) was shown for 100 ms. Following this, observers had to decide, as quickly as possible, whether or not the two sets were identical. Responses were given with the two mouse buttons (left for “identical”, right for “different”), controlled with the right hand, and response times (RTs, expressed in seconds) were recorded (more on this later). Each set consisted of three shapes (subtending approximately 1.5 degrees and being approximately 1.8 degrees apart), which could be any combination of a triangle, square and pentagon (see Sets 1 and 2 in Figure 1). Note that the same shape was allowed to be present multiple times within a set. Each shape was coloured either in pure white (corresponding to normalised RGB (1,1,1)) or light grey (RGB (0.65,0.65,0.65)). Shapes were presented on a black background.

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