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

Abstract

In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test data need to be independently identically distributed (IID); any individual user may show a completely different encephalogram (EEG) data in the same situation. The EEG data may be non-IID. In addition, noise/outlier sensitiveness still exist in GSSL approaches. To these ends, we propose in this paper a novel clustering method based on structure risk minimization model, called multi-model adaptation learning with possibilistic clustering assumption for EEG-based emotion recognition (MA-PCA). It can effectively minimize the influence from the noise/outlier samples based on different EEG-based data distribution in some reproduced kernel Hilbert space. Our main ideas are as follows: (1) reducing the negative impact of noise/outlier patterns through fuzzy entropy regularization, (2) considering the training data and test data are IID and non-IID to obtain a better performance by multi-model adaptation learning, and (3) the algorithm implementation and convergence theorem are also given. A large number of experiments and deep analysis on real DEAP datasets and SEED datasets was carried out. The results show that the MA-PCA method has superior or comparable robustness and generalization performance to EEG-based emotion recognition.

Advanced Machine Learning Approaches for Brain Mapping

“…Moreover, even if they are feasible in some specific scenarios, it is also an indispensable task to finetune the classifier to maintain a sound recognition capacity partly because the EEG signals of the same subject are changing now and then . To address the aforementioned challenges, the domain adaptation (DA) learning paradigm Dan et al, 2022) has been proposed and has achieved widespread effective applications, which enhances learning performance in the target domain by transferring and leveraging prior knowledge from other related but differently distributed domains (referred to as source or auxiliary domains), where the target domain has few or even no training samples.…”

Possibilistic distribution distance metric: a robust domain adaptation learning method

“…Moreover, even if they are feasible in some specific scenarios, it is also an indispensable task to finetune the classifier to maintain a sound recognition capacity partly because the EEG signals of the same subject are changing now and then (Zhou et al, 2022). To address the aforementioned challenges, the domain adaptation (DA) learning paradigm (Patel et al, 2015;Tao et al, 2017Tao et al, , 2022Zhang et al, 2019b;Dan et al, 2022) has been proposed and has achieved widespread effective applications, which enhances learning performance in the target domain by transferring and leveraging prior knowledge from other related but differently distributed domains (referred to as source or auxiliary domains), where the target domain has few or even no training samples.…”