Stimulus identity rather than emotion drives EEG classification on the FACED dataset

Abstract

Reliable benchmark datasets are critical for advancing EEG-based emotion recognition. The Finer-grained Affective Computing EEG Dataset (FACED) is the largest publicly available EEG emotion dataset (123 subjects, nine emotion categories) and a widely adopted benchmark. We demonstrate that both intra-subject and cross-subject classification on FACED primarily reflects stimulus identity rather than emotion. Using a linear classifier (LinearSVC) and a deep learning model (CLISA), we show that (1) classification performance is comparable for trials where subjects reported feeling versus not feeling the assigned emotion; (2) accuracy drops when stimulus-assigned labels are replaced with individual self-reports; and (3) accuracy increases when reducing to one video per emotion despite discarding two-thirds of the data. These results reflect three design choices in FACED: few stimuli per category, stimulus-assigned labels, and within-video temporal splits for cross-validation. Together, these make the dataset susceptible to temporal autocorrelation and stimulus-identity confounds. To guide future work, we propose five recommendations -- spanning stimulus diversity, temporal independence, and label validation -- for emotion-decoding study designs that mitigate these confounds.