Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition

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.

Multi-Model Adaptation Learning With Possibilistic Clustering Assumption for EEG-Based Emotion Recognition

“…We will systematically compare our method with such state-of-the-arts as SSPCA ( Dan et al, 2021 ), a baseline without domain adaptation, FastDAM ( Duan et al, 2012b ), Multi-KT ( Tommasi et al, 2014 ) with l 2 -norm constraint on p , A-SVM ( Yang et al, 2007 ), and DSM ( Duan et al, 2012a ). Since existing deep domain adaptation frameworks have achieved many inspiring results on emotion recognition as well as visual recognition, we also additionally present comparisons with several deep (CNN-based) domain adaptation methods with deep features: DAN ( Long et al, 2015 ) and Reverse Grad ( Ganin and Lempitsky, 2015 ).…”

“…where λ s ,λ, C are balance parameters that can be adjusted to avoid overfitting during model training. The details about the other parameters are provided in Dan et al (2021) .…”

“…According to this problem in the SSCCM method, Dan et al (2021) proposed SSPCA that relaxed the constraint in SSCCM and added a fuzzy entropy regularization term ( Kosko, 1986 ; Krishnapuram and Keller, 1993 ; Zhang et al, 2019c ) for increasing the samples’ discriminative information to get a membership function with better generalization and that can also reduce the negative impact of noise and outlier on recognition performance to improve the robustness of the method. Wang and Chen (2013) designed SA-SSCCM.…”

“…The main ideas are as follows: firstly, according to manifold learning ( Tenenbaum et al, 2000 ; Belkin and Niyogi, 2001 ; Gao et al, 2010 ; Nie et al, 2010 ), there is similarity among samples within its local. According to formula (1) in Dan et al (2021) , the local weighted mean (LWM) point is determined by the convex hull of the k-nearest neighbors. It represents the mean value of the local.…”

OGSSL: A Semi-Supervised Classification Model Coupled With Optimal Graph Learning for EEG Emotion Recognition

“…By injecting the domain knowledge of labeled data from multiple source subjects into the customized model of the target subject, a semi-supervised joint domain adaptation model was proposed in [10]. Dan et al developed a probabilistic clustering-promoting semi-supervised emotion recognition model with improved reliability in which each sample was constrained to share the same label membership value with its local weighted mean [11]. In [12], multi-modal signals were processed for emotion recognition by semisupervised learning and neural networks.…”