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.

Keywords
Crowdsourcing
Brainsourcing
Brain-computer interfaces
Authors
Keith M. Davis
University of Helsink, Helsinki, Finland
Lauri Kangassalo
University of Helsinki, Helsinki, Finland
Michiel Spapé
University of Helsinki, Helsinki, Finland
Tuukka Ruotsalo
University of Helsinki, Helsinki, Finland
DOI

10.1145/3313831.3376288

Paper URL

https://doi.org/10.1145/3313831.3376288

Conference: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

Session: Sensing the human

Paper session
312 NI'IHAU
5 items in this session
2020-04-29 09:00:00
2020-04-29 10:15:00
Japanese summary
Loading...