Positive Emotional State Recognition Using Nonlinear Dynamics Analysis and Machine Learning Algorithms with Electroencephalogram Signals

Abstract Electroencephalogram (EEG) signals processing has gathered increased interest from the scientific community for a long time, especially in the field of emotion recognition. The objective of this research is to propose a method that analyzes EEG signals from a publicly available dataset called SJTU Emotion EEG Dataset (SEED) to classify different emotional states, especially positive emotions. The dataset consists of EEG signals recorded from fifteen subjects while watching different film clips, each of which corresponds to a type of emotions. Initial analysis involves the extraction of features from the pre-processed data including time-domain analysis, frequency-domain analysis using Fast Fourier Transform (FFT) and nonlinear dynamics method. Machine learning algorithms are then applied as classifiers in order to determine the emotional states of each subject – categorized as positive, negative, or neutral – with the input is the extracted features. In this work, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Subspace Discriminant (SD) are used for training and testing the labeled data. The classification step achieved the highest accuracy level of 82.2% while the highest micro-F1 score value is 0.92 for positive emotions. The established methodology not only proposes an automated model for determining positive emotional states but also plays an important role at the beginning of the investigation further into emotion recognition by providing better visualization of how different emotional states are determined.