A review on the computational methods for emotional state estimation from the human EEG

Area of Science:

• Affective Computing
• Neuroscience
• Human-Computer Interaction

Background:

• Affective computing aims to develop systems recognizing human emotional states for enhanced human-computer interaction.
• Various methods exist for emotion estimation, with electroencephalography (EEG) offering direct, cost-effective insights into emotional states.
• Extracting reliable emotional information from complex, noisy EEG data necessitates advanced computational techniques.

Purpose of the Study:

• To review computational methods for EEG-based emotion recognition.
• To explore feature extraction and classification techniques for EEG signals.
• To propose sequential Bayesian inference for real-time continuous emotion estimation.

Main Methods:

• Review of existing computational methods for EEG emotion analysis.
• Exploration of techniques for deducing EEG indices of emotion.
• Proposal of sequential Bayesian inference for real-time emotion state estimation.

Main Results:

• Identified various computational approaches for EEG-based emotion recognition.
• Highlighted the need for sophisticated methods to handle complex EEG data.
• Proposed a novel approach using sequential Bayesian inference for continuous emotion estimation.

Conclusions:

• EEG offers a promising avenue for emotion recognition in affective computing.
• Advanced computational methods are crucial for accurate EEG-based emotion detection.
• Sequential Bayesian inference presents a viable direction for real-time emotion estimation systems.