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We present a collaborative Brain-Computer Interface (cBCI) to aid group decision-making based on realistic video feeds. The cBCI combines neural features extracted from EEG and response times to estimate the decision confidence of users. Confidence estimates are used to weigh individual responses and obtain group decisions. Results obtained with 10 participants indicate that cBCI groups are significantly more accurate than equally-sized groups using standard majority. Also, selecting dyads on the basis of the average performance of their members and then assisting them with our cBCI halves the error rates with respect to majority-based performance. Also, this allows most participants to be included in at least one selected dyad, hence being quite inclusive. Results indicate that this selection strategy makes cBCIs even more effective as methods for human augmentation in realistic scenarios.
|Title of host publication||9th International IEEE EMBS Conference on Neural Engineering, NER 2019|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|Publication status||Published (in print/issue) - 16 May 2019|
|Event||9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States|
Duration: 20 Mar 2019 → 23 Mar 2019
|Name||International IEEE/EMBS Conference on Neural Engineering, NER|
|Conference||9th International IEEE EMBS Conference on Neural Engineering, NER 2019|
|Period||20/03/19 → 23/03/19|
- collaborative decision-making
- Group selection
- Brain computer interfaces
- electroencephalography (EEG)
- response times
- Reported Confidence
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Saugat Bhattacharyya (Participant)Mar 2019
Activity: Participating in or organising an event › Participating in a conference, workshop, ...