Overview

Emotion plays a crucial role in interpersonal communication and medical research. Computers can identify emotions through facial expressions, voice, blinking patterns, and posture. While most studies focus on voice and facial expressions, these methods have limitations. For example, individuals may intentionally hide their true emotions, or conditions like facial neuritis may impair emotional expression through facial cues. To address these limitations, researchers have turned to emotion recognition based on peripheral electroencephalograph (EEG) signals. EEG-based methods aim to detect affective states directly through spontaneous brain activity, bypassing the peripheral nervous system. This approach is vital for applications in entertainment, healthcare, military operations, retail, and education, where understanding human emotion enhances interaction and decision-making processes.In this thesis, we propose a novel method for emotion identification using EEG data.

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