Emotion Recognition Based on High-Resolution EEG Recordings and Reconstructed Brain Sources

TLDR

EEG‑based emotion recognition is a hot topic, yet most studies follow a similar pipeline of stimulus presentation and machine‑learning classification, and results are hard to compare because of differing datasets. This paper introduces a publicly available valence‑analysis database. The database contains 257‑channel EEG and physiological recordings, applies source‑localization to reconstruct cortical activity, and compares valence‑classification performance between source‑space and sensor‑space features while examining parameter effects. Source‑space features yield better valence‑classification accuracy than sensor‑space features.

Abstract

Electroencephalography (EEG)-based emotion recognition is currently a hot issue in the affective computing community. Numerous studies have been published on this topic, following generally the same schema: 1) presentation of emotional stimuli to a number of subjects during the recording of their EEG, 2) application of machine learning techniques to classify the subjects' emotions. The proposed approaches vary mainly in the type of features extracted from the EEG and in the employed classifiers, but it is difficult to compare the reported results due to the use of different datasets. In this paper, we present a new database for the analysis of valence (positive or negative emotions), which is made publicly available. The database comprises physiological recordings and 257-channel EEG data, contrary to all previously published datasets, which include at most 62 EEG channels. Furthermore, we reconstruct the brain activity on the cortical surface by applying source localization techniques. We then compare the performances of valence classification that can be achieved with various features extracted from all source regions (source space features) and from all EEG channels (sensor space features), showing that the source reconstruction improves the classification results. Finally, we discuss the influence of several parameters on the classification scores.