A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP—STSA) for Emotion Recognition Using EEG Signals

• Published in: Frontiers in human neuroscience Vol. 16; p. 936393
• Main Authors: Tavakkoli, Hoda, Motie Nasrabadi, Ali
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 29.06.2022; Frontiers Media S.A
• Subjects: Artificial intelligence; Brain; Brain research; brain-computer interface; Computer applications; Discriminant analysis; EEG; Electroencephalography; emotion recognition; Emotions; Human Neuroscience; Human-computer interaction; Implants; nonlinear dynamic analysis; phase space partitioning; Physiology; Signal processing; subject-independent classification systems; symbolic time series analysis; Time series
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2022.936393

Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically. There are many approaches due to different features extractions methods for analyzing the EEG signals. Still, Since the brain is supposed to be a nonlinear dynamic system, it seems a nonlinear dynamic analysis tool may yield more convenient results. A novel approach in Symbolic Time Series Analysis (STSA) for signal phase space partitioning and symbol sequence generating is introduced in this study. Symbolic sequences have been produced by means of spherical partitioning of phase space; then, they have been compared and classified based on the maximum value of a similarity index. Obtaining the automatic independent emotion recognition EEG-based system has always been discussed because of the subject-dependent content of emotion. Here we introduce a subject-independent protocol to solve the generalization problem. To prove our method’s effectiveness, we used the DEAP dataset, and we reached an accuracy of 98.44% for classifying happiness from sadness (two- emotion groups). It was 93.75% for three (happiness, sadness, and joy), 89.06% for four (happiness, sadness, joy, and terrible), and 85% for five emotional groups (happiness, sadness, joy, terrible and mellow). According to these results, it is evident that our subject-independent method is more accurate rather than many other methods in different studies. In addition, a subject-independent method has been proposed in this study, which is not considered in most of the studies in this field.