EEG-based Emotion Recognition Using Sub-Band Time-Delay Correlations.

Emotion recognition is a challenging task with many potential applications in psychology, psychiatry, and human-computer interaction (HCI). The use of time-delay information in the controlled time-delay stability (cTDS) algorithm can help to capture the temporal dynamics of EEG signals, including sub-band information and bi-directional coupling that can aid in emotion recognition and identification of specific connectivity patterns between brain rhythms. Incorporating EEG frequency bands can be used to design better emotion recognition systems. This paper evaluates the cTDS algorithm for binary classification tasks of arousal and valence using EEG sub-band signals. This method achieved a high accuracy of 91.1% for arousal and 91.7% for valence based on one electrode recording site at Fp1. The cTDS algorithm is a promising approach to analyzing brain network interactions. It can be particularly applicable to arousal and valence classification tasks, especially within a complex, multimodal feature space associated with understanding psychiatric disorders and HCI applications.

A Comparative Study of Arousal and Valence Dimensional Variations for Emotion Recognition Using Peripheral Physiological Signals Acquired from Wearable Sensors.

Wearable sensors have made an impact on healthcare and medicine by enabling out-of-clinic health monitoring and prediction of pathological events. Further advancements made in the analysis of multimodal signals have been in emotion recognition which utilizes peripheral physiological signals captured by sensors in wearable devices. There is no universally accepted emotion model, though multidimensional methods are often used, the most popular of which is the two-dimensional Russell's model based on arousal and valence. Arousal and valence values are discrete, usually being either binary with low and high labels along each dimension creating four quadrants or 3-valued with low, neutral, and high labels. In day-to-day life, the neutral emotion class is the most dominant leaving emotion datasets with the inherent problem of class imbalance. In this study, we show how the choice of values in the two-dimensional model affects the emotion recognition using multiple machine learning algorithms. Binary classification resulted in an accuracy of 87.2% for arousal and up to 89.5% for valence. Maximal 3-class classification accuracy was 80.9% for arousal and 81.1% for valence. For the joined classification of arousal and valence, the four-quadrant model reached 87.8%, while the nine-class model had an accuracy of 75.8%. This study can be used as a basis for further research into feature extraction for better overall classification performance.