Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.

Area of Science:

• Neuroscience and Artificial Intelligence
• Brain-Computer Interfaces (BCI)
• Machine Learning for Affective Computing

Background:

• Electroencephalogram (EEG) signal analysis is vital for human emotion identification in BCIs.
• Applications include enhanced brain-machine interaction and brain health assessment.
• Challenges include subject variability, high noise, and limited labeled data, hindering model generalizability.

Purpose of the Study:

• To review and analyze the current state of EEG-based emotion identification.
• To evaluate traditional and deep learning approaches for EEG signal analysis.
• To identify persistent challenges and future research directions in the field.

Main Methods:

• Literature review of electroencephalogram (EEG) signal analysis for emotion identification.
• Analysis of established datasets such as DEAP, SEED, and AMIGOS.
• Comparison of traditional machine learning methods (SVM, KNN, RF) with deep learning models (CNN, RNN).

Main Results:

• Traditional methods rely on handcrafted features for EEG classification.
• Deep learning models (CNNs, RNNs) offer automatic feature learning from raw EEG data.
• Despite advancements, challenges of noise, subject variability, and data scarcity remain significant.

Conclusions:

• Deep learning techniques show potential for improved EEG-based emotion recognition.
• Fundamental challenges in EEG signal analysis require further investigation.
• Future research should prioritize model robustness, scalability, and interpretability.