Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces

Saugat Bhattacharyya; Davide Valeriani; Caterina Cinel; Luca Citi; Riccardo Poli

doi:10.1109/NER.2019.8717146

Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces

Saugat Bhattacharyya, Davide Valeriani, Caterina Cinel, Luca Citi, Riccardo Poli

University of Essex

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

65 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Publisher	IEEE Computer Society
Pages	159-162
Number of pages	4
ISBN (Electronic)	9781538679210
DOIs	https://doi.org/10.1109/NER.2019.8717146
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

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2019-March
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Conference

Conference	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Country/Territory	United States
City	San Francisco
Period	20/03/19 → 23/03/19

Funding

* This work was funded and supported by the UK Defence Science and Technology Laboratory (Dstl) and Engineering and Physical Research Council (EPSRC) under grant EP/P009204/1. This is part of the collaboration between US DOD, UK MOD and UK EPSRC under the Multidisciplinary University Research Initiative.

Keywords

collaborative decision-making
Group selection
Brain computer interfaces
electroencephalography (EEG)
response times
Reported Confidence

Access to Document

10.1109/NER.2019.8717146

Accepted ManuscriptAuthor Accepted Manuscript -, 684 KB

Cite this

Bhattacharyya, S., Valeriani, D., Cinel, C., Citi, L., & Poli, R. (2019). Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 (pp. 159-162). Article 8717146 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/NER.2019.8717146

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title = "Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces",

abstract = "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.",

keywords = "collaborative decision-making, Group selection, Brain computer interfaces, electroencephalography (EEG), response times, Reported Confidence",

author = "Saugat Bhattacharyya and Davide Valeriani and Caterina Cinel and Luca Citi and Riccardo Poli",

year = "2019",

month = may,

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Bhattacharyya, S, Valeriani, D, Cinel, C, Citi, L & Poli, R 2019, Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces. in 9th International IEEE EMBS Conference on Neural Engineering, NER 2019., 8717146, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2019-March, IEEE Computer Society, pp. 159-162, 9th International IEEE EMBS Conference on Neural Engineering, NER 2019, San Francisco, United States, 20/03/19. https://doi.org/10.1109/NER.2019.8717146

Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces. / Bhattacharyya, Saugat; Valeriani, Davide; Cinel, Caterina et al.
9th International IEEE EMBS Conference on Neural Engineering, NER 2019. IEEE Computer Society, 2019. p. 159-162 8717146 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Citi, Luca

AU - Poli, Riccardo

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KW - Group selection

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Bhattacharyya S, Valeriani D, Cinel C, Citi L, Poli R. Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019. IEEE Computer Society. 2019. p. 159-162. 8717146. (International IEEE/EMBS Conference on Neural Engineering, NER). doi: 10.1109/NER.2019.8717146

Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces

Abstract

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Conference

Funding

Keywords

Access to Document

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9th International IEEE EMBS Conference on Neural Engineering, NER 2019

Cite this

Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

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9th International IEEE EMBS Conference on Neural Engineering, NER 2019

Cite this