EEG-Face: A Facial-Image Stimulated EEG Data-Set for Analysis of Brain Perceived Multimedia

摘要

Over recent years, EEG-based brain decoding of perceived multimedia is emerging to be an important multidisciplinary research area. Lack of data sets with multimedia stimuli, however, presents a significant challenge for its further advancement. In this paper, we establish a facial-image stimulated EEG dataset, named as EEG-Face, to address the challenge and provide a crucial support for relevant research, such as brain-computer interface (BCI), face recognition via brain-perceived EEGs, and multimedia content analysis via brain perception activities. As facial images not only distinguish between genders but also dive deeper into individual differences, our proposed EEG-Face provides larger scope, more focus, and greater potential for dedicated research on brain perception of human faces. As shown in Figure 1, the proposed EEG-Face essentially consists of 20,000 brain responded EEG trials stimulated with 40 individual faces, all of whom are Chinese film stars. Following the establishment of the dataset, a range of experiments over EEG-Face is carried out to demonstrate its usability and feasibility, which include: (i) neural correlation of gender perceptions; (ii) EEG-Stimulus pairing verification; and (iii) face recognition via classification of randomized EEG trials. The dataset and the codes for all reported experiments are available from: https://github.com/eeg-wx2024/EEG-Face.