Toward an Open Data Repository and Meta-Analysis of Cognitive Data Using fNIRS Studies of Emotion

HCI research has increasingly incorporated the use of neurophysiological sensors to identify users’ cognitive and affective states. However, a persistent problem in machine learning on cognitive data is generalizability across participants. A proposed solution has been aggregating cognitive and survey data across studies to generate higher sample populations for machine learning and statistical analyses to converge in stable, generalizable results. In this paper, I argue that large data-sharing projects can facilitate the aggregation of results of brain imaging studies to address these issues, by smoothing noise in high-dimensional datasets. This paper contributes a small step towards large cognitive data sharing systems-design by proposing methods that facilitate the merging of currently incompatible fNIRS and FMRI datasets through term-based metadata analysis. To that end, I analyze 20 fNIRS studies of emotion using content analysis for: (1) synonym terms and definitions for ‘emotion,’ (2) the experimental stimuli, and (3) the use or non-use of self-report surveys. Results suggest that fNIRS studies of emotion have stable synonymy, using technical and folk conceptualizations of affective terms within and between publications to refer to emotion. The studies use different stimuli to elicit emotion but also show commonalities between shared use of standardized stimuli materials and self-report surveys. These similarities in conceptual synonymy and standardized experiment materials indicate promise for neuroimaging communities to establish open-data repositories based on metadata term-based analyses. This work contributes to efforts toward merging datasets across studies and between labs, unifying new modalities in neuroimaging such as fNIRS with fMRI datasets, increasing generalizability of machine learning models, and promoting the acceleration of science through open data-sharing infrastructure.