Automated human emotion recognition from EEG signals using chaotic local binary pattern and ensemble learning

Key Points

• Automated emotion recognition achieved high accuracy rates through ensemble learning techniques, enhancing diagnostic potential.
• The accuracy reached 92% in distinguishing emotions, showing significant technological advancement in emotion detection.
• The approach employed chaotic local binary pattern for feature extraction from EEG signals, enhancing recognition efficiency.
• Highlighting the robust nature of EEG-based emotion analysis, the findings suggest wider applications in various fields.