Exploring EEG Features in Cross-Subject Emotion Recognition

Abstract

Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the “leave-one-subject-out” verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition.

Analysis of Personality and EEG Features in Emotion Recognition Using Machine Learning Techniques to Classify Arousal and Valence Labels

“…Feature construction and feature selection are key steps in the data analysis process-in most cases, conditioning the success of any machine learning endeavor [64]. Previous works have shown how applying feature selection process in emotion recognition tasks using EEG traits [57,58], increases the performance of the classifiers while the computational power is reduced. For the purpose of this work, we wanted to perform feature selection process to reduce the number of features, preventing overfitting and improving the classification process.…”

“…Moreover, the rational asymmetry (RASM) and differential asymmetry (DASM) for each of the seven pairs of electrodes in the five bands were calculated (70 features). Because in previews literature [57][58][59], these EEG traits are related to participants' emotional responses, and reports about EEG emotion recognition used the same kind of features to obtain classification above the chance level [14]. We wanted to include these EEG traits to analyze if they can improve the classification performance in contrast with the ones used in the AMIGOS work.…”

Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal

“…After building the models, there are usually two ways to validate the performance of the models: Cross-subject validation [36,41]: If the dataset has N subjects, and one subject provides n samples, this validation uses all the samples (m × n samples for the test set) from m different subjects as the test set, and uses all the remaining samples ((N−m) × n samples for training set) to train the classifier. This way one can obtain M accuracies by training M models if the m subjects selected each time are different from each other.…”

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

“…As is well known, di erent types of entropy measures can capture di erent features from physiological signals [25]. For instance, Li et al investigated nine entropy measures of EEG signals for emotion recognition [26]. (iii) irdly, how to deal with physiological signals in the interest of e ectively extracting entropy measures from them has been becoming one of the most key factors that determine the performance on entropy-based pattern learning tasks.…”