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Emotion detection from EEG using transfer learning.

Sidharth Sidharth, Ashish Abraham Samuel, Ranjana H

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

Summary

This study introduces a novel method for detecting emotions using electroencephalography (EEG) by combining Mean Phase Coherence (MPC) and Magnitude Squared Coherence (MSC) features. The approach achieved high accuracy in both subject-dependent and subject-independent emotion classification tasks.

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Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Electroencephalography (EEG)-based emotion detection faces challenges due to limited data.
Transfer learning offers a potential solution for data scarcity in complex machine learning tasks.

Purpose of the Study:

To develop an effective EEG-based emotion detection system using transfer learning and novel feature combinations.
To evaluate the performance of the proposed method in both subject-dependent and subject-independent scenarios.

Main Methods:

Utilized Resnet50 as the base model for transfer learning.
Developed a novel feature representation using Mean Phase Coherence (MPC) and Magnitude Squared Coherence (MSC) in image matrices.
Incorporated Differential Entropy (DE) features into the diagonal of the image matrix.
Employed 10-fold cross-validation for subject-dependent accuracy and leave-one-subject-out (LOSO) for subject-independent accuracy on the SEED EEG dataset (3 classes).

Main Results:

Achieved 93.1% accuracy in subject-dependent emotion classification.
Obtained 71.6% accuracy in subject-independent emotion classification.
Both accuracies significantly exceed chance level for 3-class classification.

Conclusions:

The combination of MPC and MSC features shows significant promise for EEG-based emotion classification.
The transfer learning approach effectively addresses data limitations in EEG emotion detection.
Future work may explore data augmentation, enhanced classifiers, and improved feature engineering.