  Emotional recognition through exploring the electroencephalography (EEG)
characteristics has been widely performed in recent studies. Nonlinear analysis
and feature extraction methods for understanding the complex dynamical
phenomena are associated with the EEG patterns of different emotions. The phase
space reconstruction is a typical nonlinear technique to reveal the dynamics of
the brain neural system. Recently, the topological data analysis (TDA) scheme
has been used to explore the properties of space, which provides a powerful
tool to think over the phase space. In this work, we proposed a topological EEG
nonlinear dynamics analysis approach using the phase space reconstruction (PSR)
technique to convert EEG time series into phase space, and the persistent
homology tool explores the topological properties of the phase space. We
perform the topological analysis of EEG signals in different rhythm bands to
build emotion feature vectors, which shows high distinguishing ability. We
evaluate the approach with two well-known benchmark datasets, the DEAP and
DREAMER datasets. The recognition results achieved accuracies of 99.37% and
99.35% in arousal and valence classification tasks with DEAP, and 99.96%,
99.93%, and 99.95% in arousal, valence, and dominance classifications tasks
with DREAMER, respectively. The performances are supposed to be outperformed
current state-of-art approaches in DREAMER (improved by 1% to 10% depends on
temporal length), while comparable to other related works evaluated in DEAP.
The proposed work is the first investigation in the emotion recognition
oriented EEG topological feature analysis, which brought a novel insight into
the brain neural system nonlinear dynamics analysis and feature extraction.
