Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing

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.