Citation: Chen, Z.; Duan, S.; Peng, Y.
EEG-Based Emotion Recognition by
Retargeted Semi-Supervised
Regression with Robust Weights.
Systems 2022, 10, 236. https://
doi.org/10.3390/systems10060236
Academic Editors: Enrico Vezzetti,
Andrea Luigi Guerra, Gabriele
Baronio, Domenico Speranza and
Luca Ulrich
Received: 27 October 2022
Accepted: 25 November 2022
Published: 29 November 2022
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Article
EEG-Based Emotion Recognition by Retargeted
Semi-Supervised Regression with Robust Weights
Ziyuan Chen
1
, Shuzhe Duan
1
and Yong Peng
2,3,
*
1
Zhuoyue Honors College, Hangzhou Dianzi University, Hangzhou 310018, China
2
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
3
Zhejiang Key Laboratory of Brain-Machine Collaborative Intelligence, Hangzhou 310018, China
* Correspondence: yongpeng@hdu.edu.cn
Abstract:
The electroencephalogram (EEG) can objectively reflect the emotional state of human
beings, and has attracted much attention in the academic circles in recent years. However, due
to its weak, non-stationary, and low signal-to-noise properties, it is inclined to cause noise in the
collected EEG data. In addition, EEG features extracted from different frequency bands and channels
usually exhibit different levels of emotional expression abilities in emotion recognition tasks. In this
paper, we fully consider the characteristics of EEG and propose a new model RSRRW (retargeted
semi-supervised regression with robust weights). The advantages of the new model can be listed as
follows. (1) The probability weight is added to each sample so that it could help effectively search
noisy samples in the dataset, and lower the effect of them at the same time. (2) The distance between
samples from different categories is much wider than before by extending the
e
-dragging method
to a semi-supervised paradigm. (3) Automatically discover the EEG emotional activation mode
by adaptively measuring the contribution of sample features through feature weights. In the three
cross-session emotion recognition tasks, the average accuracy of the RSRRW model is 81.51%, which
can be seen in the experimental results on the SEED-IV dataset. In addition, with the support of the
Friedman test and Nemenyi test, the classification of RSRRW model is much more accurate than that
of other models.
Keywords:
electroencephalogram (EEG); robustness; semi-supervised classification; emotion
recognition;
emotional activation pattern mining
1. Introduction
As a complex psychological state, emotion plays a key role in human cognition,
including rational decision-making, perception, interpersonal communications and human
intelligence [
1
]. Therefore, emotion recognition has attracted the attention of researchers
from various disciplines. Usually, researchers investigate emotion recognition from the
data sources of language, body movements, speech, and facial expressions. However, these
patterns have certain drawbacks. (1) When subjects deliberately disguise their emotions,
the performance of the method may be significantly affected by the deceptive data collected
from the subjects based on the above data model. (2) The previous mode is impractical
for people with physical disabilities (deafness, aphasia and etc.) Therefore, we need a
more objective mode of emotional recognition. Firstly, considering that emotion is hard to
measure as the result of spontaneousness, thus it can be observed by the accompanying
physiological reactions in the central nervous system and periphery [
2
,
3
]. Secondly, EEG
is a signal from the central nervous system, which has several advantages, such as large
amounts of information, simple operation and low-cost [
4
]. So, together with the rapid
development of non-stationary signal processing and analysis techniques, EEG-based
emotion recognition has become a research hotspot [5].
Systems 2022, 10, 236. https://doi.org/10.3390/systems10060236 https://www.mdpi.com/journal/systems
