Selecting transferrable neurophysiological features for inter-individual emotion recognition via a shared-subspace feature elimination approach

Abstract

The interplay between human emotions, personality, and motivation results in individual specificity in neurophysiological data distributions for the same emotional category. To address this issue for building an emotion recognition system based on electroencephalogram (EEG) features, we propose a shared-subspace feature elimination (SSFE) approach to identify EEG variables with common characteristics across multiple individuals. In the SSFE framework, a low-dimensional space defined by a selected number of EEG features is created to represent the inter-emotion discriminant for different pairs of subjects evaluated based on the interclass margin. Using two public databases—DEAP and MAHNOB-HCI—the performance of the SSFE is validated according to the leave-one-subject-out paradigm. The performance of the proposed framework is compared with five other feature-selection methods. The effectiveness and computational cost of the SSFE is investigated across six machine learning models based on their optimal hyperparameters. In the end, the competitive binary classification accuracy from the SSFE of arousal and valence recognitions are determined to be 0.6521 and 0.6635, respectively, for DEAP, and 0.6520 and 0.6537, respectively for MAHNOB-HCI.