Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing
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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
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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