Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing

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

Standard

Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing. / Davis, Keith M.; Kangassalo, Lauri; Spapé, Michiel; Ruotsalo, Tuukka.

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2020. p. 1-14.

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

Harvard

Davis, KM, Kangassalo, L, Spapé, M & Ruotsalo, T 2020, Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing. in CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, pp. 1-14. https://doi.org/10.1145/3313831.3376288

APA

Davis, K. M., Kangassalo, L., Spapé, M., & Ruotsalo, T. (2020). Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing. In CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-14). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376288

Vancouver

Davis KM, Kangassalo L, Spapé M, Ruotsalo T. Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing. In CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2020. p. 1-14 https://doi.org/10.1145/3313831.3376288

Author

Davis, Keith M. ; Kangassalo, Lauri ; Spapé, Michiel ; Ruotsalo, Tuukka. / Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing. CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2020. pp. 1-14

Bibtex

@inproceedings{6dd64c927f6b42449365482b53211ee3,
title = "Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing",
abstract = "This paper introduces brainsourcing: utilizing brain responses of a group of human contributors each performing a recognition task to determine classes of stimuli. We investigate to what extent it is possible to infer reliable class labels using data collected utilizing electroencephalography (EEG) from participants given a set of common stimuli. An experiment (N=30) measuring EEG responses to visual features of faces (gender, hair color, age, smile) revealed an improved F1 score of 0.94 for a crowd of twelve participants compared to an F1 score of 0.67 derived from individual participants and a random chance of 0.50. Our results demonstrate the methodological and pragmatic feasibility of brainsourcing in labeling tasks and opens avenues for more general applications using brain-computer interfacing in a crowdsourced setting.",
author = "Davis, {Keith M.} and Lauri Kangassalo and Michiel Spap{\'e} and Tuukka Ruotsalo",
year = "2020",
doi = "10.1145/3313831.3376288",
language = "English",
pages = "1--14",
booktitle = "CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems",
publisher = "Association for Computing Machinery",

}

RIS

TY - GEN

T1 - Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing

AU - Davis, Keith M.

AU - Kangassalo, Lauri

AU - Spapé, Michiel

AU - Ruotsalo, Tuukka

PY - 2020

Y1 - 2020

N2 - This paper introduces brainsourcing: utilizing brain responses of a group of human contributors each performing a recognition task to determine classes of stimuli. We investigate to what extent it is possible to infer reliable class labels using data collected utilizing electroencephalography (EEG) from participants given a set of common stimuli. An experiment (N=30) measuring EEG responses to visual features of faces (gender, hair color, age, smile) revealed an improved F1 score of 0.94 for a crowd of twelve participants compared to an F1 score of 0.67 derived from individual participants and a random chance of 0.50. Our results demonstrate the methodological and pragmatic feasibility of brainsourcing in labeling tasks and opens avenues for more general applications using brain-computer interfacing in a crowdsourced setting.

AB - This paper introduces brainsourcing: utilizing brain responses of a group of human contributors each performing a recognition task to determine classes of stimuli. We investigate to what extent it is possible to infer reliable class labels using data collected utilizing electroencephalography (EEG) from participants given a set of common stimuli. An experiment (N=30) measuring EEG responses to visual features of faces (gender, hair color, age, smile) revealed an improved F1 score of 0.94 for a crowd of twelve participants compared to an F1 score of 0.67 derived from individual participants and a random chance of 0.50. Our results demonstrate the methodological and pragmatic feasibility of brainsourcing in labeling tasks and opens avenues for more general applications using brain-computer interfacing in a crowdsourced setting.

U2 - 10.1145/3313831.3376288

DO - 10.1145/3313831.3376288

M3 - Article in proceedings

SP - 1

EP - 14

BT - CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

ER -

ID: 255209793