  Recognizing human emotions from complex, multivariate, and non-stationary
electroencephalography (EEG) time series is essential in affective
brain-computer interface. However, because continuous labeling of ever-changing
emotional states is not feasible in practice, existing methods can only assign
a fixed label to all EEG timepoints in a continuous emotion-evoking trial,
which overlooks the highly dynamic emotional states and highly non-stationary
EEG signals. To solve the problems of high reliance on fixed labels and
ignorance of time-changing information, in this paper we propose a time-aware
sampling network (TAS-Net) using deep reinforcement learning (DRL) for
unsupervised emotion recognition, which is able to detect key emotion fragments
and disregard irrelevant and misleading parts. Extensive experiments are
conducted on three public datasets (SEED, DEAP, and MAHNOB-HCI) for emotion
recognition using leave-one-subject-out cross-validation, and the results
demonstrate the superiority of the proposed method against previous
unsupervised emotion recognition methods.
