Abstract

The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in the same scenario. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning method for EEG-based Emotion Recognition. Specifically, it constrains each instance to have the same label membership value with its local weighted mean to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and the outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED, and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG-based emotion recognition.

Highlights

  • Emotion is embodied by human beings: we are born with an innate understanding of emotion (Dolan, 2002; Zhang et al, 2016, 2019b)

  • It takes the decision function and label membership function to identify each other’s predicted classification results in order to further enhance the reliability of PCP-ER

  • The decision function and label membership function are adopted in PCP-ER to obtain predictions, and their predictions are usually consistent

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Summary

Introduction

Emotion is embodied by human beings: we are born with an innate understanding of emotion (Dolan, 2002; Zhang et al, 2016, 2019b). The complexity of emotion leads to different people’s understanding of emotion. It is more difficult for machines to accurately understand emotion. We focus on emotional speculation through changes in the body. The representative internal changes of the body include blood pressure, magneto encephalogram, electroencephalogram (EEG), heart rate, respiratory rate (Mühl et al, 2014), and so on. The EEG-based traditional emotion recognition system usually has two parts: feature extraction and recognizer training

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