کنفرانس مهندسی برق ایران

صفحه اصلی / سی و سومین کنفرانس بین المللی مهندسی برق

Emotion Recognition from EEG Signals During REM Sleep

نویسندگان :

Asghar Zarei¹ Ali Mahmoudi²

1- دانشگاه صنعتی سهند تبریز 2- دانشگاه صنعتی سهند

کلمات کلیدی :

Emotion recognition،Dream،EEG signal،Machine learning،Entropy-based features

چکیده :

This paper utilizes EEG signals for automatic emotion recognition that occurs while dreaming in Rapid Eye Movement (REM) sleep. As dream contents could be affected by one’s mental health, emotion recognition during REM sleep would give an insight into a person’s mental well-being. With data from the Dream Emotion Evaluation Dataset (DEED), EEG signals were analyzed to assess brain activity patterns representative of different emotional states (such as positive, negative, and neutral) experienced while dreaming. In this study, the proposed method involves EEG signal preprocessing, feature extraction using Empirical Mode Decomposition (EMD) and Sample Entropy, and classification of emotional content using machine learning models like Random Forest, Support Vector Machine, and K-Nearest Neighbors. The results show that the proposed classification algorithm is capable of classifying different emotions in dream reports with an average accuracy of 93.83%. It is therefore believed that these findings expand the understanding of neural mechanisms behind dream emotions and how they can introduce further development into EEG-based approaches for clinical environments dealing with emotional assessment and intervention.