Randomly dividing homologous samples leads to overinflated accuracies for emotion recognition

Abstract

There are numerous studies measuring the brain emotional status by analyzing EEGs under the emotional stimuli that have occurred. However, they often randomly divide the homologous samples into training and testing groups, known as randomly dividing homologous samples (RDHS), despite considering the impact of the non-emotional information among them, which would inflate the recognition accuracy. This work proposed a modified method, the integrating homologous samples (IHS), where the homologous samples were either used to build a classifier, or to be tested. The results showed that the classification accuracy was much lower for the IHS than for the RDHS. Furthermore, a positive correlation was found between the accuracy and the overlapping rate of the homologous samples. These findings implied that the overinflated accuracy did exist in those previous studies where the RDHS method was employed for emotion recognition. Moreover, this study performed a feature selection for the IHS condition based on the support vector machine-recursive feature elimination, after which the average accuracies were greatly improved to 85.71% and 77.18% in the picture-induced and video-induced tasks, respectively.

Keywords: Affective computing; Electroencephalography (EEG); Emotion recognition; Feature selection; Overinflated accuracies; Valence.