EEG-based emotion recognition using AR burg and ensemble machine learning models

Abstract

Emotion recognition plays a crucial role in human-computer interaction, affective computing, and mental health assessment. In recent years, electroencephalography (EEG) has emerged as a promising modality for detecting and interpreting human emotions. The popularity of processing data using machine learning is becoming popular day by day. Because of a complex nature of the EEG signals and presence of artifacts and noise, the automated recognition is usually limited to a small number of emotion classes. This chapter proposes a novel approach for EEG-based emotion recognition using autoregressive (AR) Burg modeling combined with ensemble machine learning models. Relevant features are extracted from EEG signals using the AR Burg approach, which captures the spectral properties and temporal dynamics linked to various emotional states. These features are subsequently fed into ensemble machine learning models to characterize emotions effectively. The suggested method improves emotion recognition performance by utilizing the advantages of feature extraction and classification approaches. The proposed method's efficacy in properly recognizing emotional states from EEG signals is demonstrated by experimental results, underscoring its potential applications in affective computing, mental health monitoring, and human-computer interaction.