Towards affect detection during human-technology interaction: An empirical study using a combined EEG and fNIRS approach

The present Ph. D. project explores possibilities to apply neurophysiological methods for affect detection during human-technology interaction (HTI). Portable neurophysio-logical methods such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer an objective, ecologically valid and rather convenient way to infer the user's affective state through the monitoring of brain activity. To identify neural signatures for positive and negative affective user reactions an empirical study is proposed. The experimental design of this study enables synchronous data acquisition for EEG, fNIRS and psychophysiological measurements while the user is interacting with an adaptive web-interface. During the interaction process positive and negative affective states are induced by system-generated adaptive actions which are either appropriate and helpful or inappropriate and impedimental. The findings of the empirical study shed light into the question whether EEG, fNIRS or a hybrid approach that combines the employed methods is most reliable for affect detection during HTI.