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This is a Literature Review study.

September 5, 2025Applied and Computational EngineeringOpen Access

EEG-Based Affective Computing: A Review of Signal Processing Techniques

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The review reveals the significance of preprocessing and feature extraction in EEG-based emotion recognition systems, enhancing their predictive performance.
Support vector machine, long short-term memory networks, and deep belief networks are some of the models detailed, each showing promising results in classifying emotions.
The MCD_DA method addresses cross-subject adaptation challenges, aiming to improve generalization to new individuals beyond training samples.
Current emotion recognition systems struggle with individual variability in EEG signals, emphasizing the need for robust techniques for broader application.

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Implication

This review highlights machine learning models and preprocessing methods for emotion recognition from EEG, suggesting improved accuracy in real-world applications.

Cite This Study

Linlin Su (2025) studied this question.

synapsesocial.com/papers/68bb3a3d2b87ece8dc955151 https://doi.org/10.54254/2755-2721/2025.ld26298

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Implication

This review highlights machine learning models and preprocessing methods for emotion recognition from EEG, suggesting improved accuracy in real-world applications.

EEG-Based Affective Computing: A Review of Signal Processing Techniques

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Implication

Cite This Study

EEG-Based Affective Computing: A Review of Signal Processing Techniques

Key Points

Discussion

Implication

Cite This Study

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