Single-Channel EEG Signal-Based Automated Emotion Recognition System Using Local Mean Decomposition

Abstract

Emotions play a pivotal role in human experience, profoundly influencing behavior and decision-making. Identifying and understanding emotions is paramount in the field of neurological research. The objective of this study is to minimize the complexity of EEG-based emotion recognition by reducing the number of channels while achieving high recognition performance. In this study, we propose local mean decomposition (LMD)-based emotion recognition using single-channel electroencephalography (EEG). The EEG signals are decomposed into their product functions using LMD. Non-linear features are computed from each product function. The Kruskal-Wallis test is then conducted to identify significant features, ensuring the analysis focuses on the most informative elements. A decision tree classifier is employed to classify different emotion states. The proposed analysis achieves a commendable average accuracy of 98.7% for EEG recordings from the F3 channel in the frontal region. The accuracy results are further compared with other channels and reviewed studies, and it has been acknowledged that frontal neurons are best suited for emotion recognition. The experiment justifies the use of a focused EEG dataset acquired from the frontal region of the skull, and this study gives the scope and use of a single-channel EEG signal for emotion recognition.