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Semi-supervised regression with adaptive graph learning for EEG-based emotion recognition.

Tianhui Sha¹, Yikai Zhang¹, Yong Peng^1,2

¹School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.

Mathematical Biosciences and Engineering : MBE

|June 16, 2023

Summary

This summary is machine-generated.

This study introduces a novel semi-supervised regression with adaptive graph learning (SRAGL) model for more accurate cross-session electroencephalogram (EEG) emotion recognition, improving upon existing methods.

Keywords:

Electroencephalogram (EEG)adaptive graph learning emotion recognition graph label propagation semi-supervised regression

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Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Electroencephalogram (EEG) signals offer rich physiological data for emotion recognition but are challenging due to non-stationarity and low signal-to-noise ratio.
Existing methods struggle with cross-session variability and the inherent noise in EEG data.

Purpose of the Study:

To develop a robust model for cross-session EEG emotion recognition that addresses the limitations of non-stationary and noisy EEG signals.
To improve the accuracy and reliability of emotion recognition from EEG data across different sessions.

Main Methods:

Proposed a semi-supervised regression with adaptive graph learning (SRAGL) model.
SRAGL jointly estimates emotional labels of unlabeled samples and learns an adaptive graph to represent connections within EEG data.
Utilized semi-supervised regression to leverage both labeled and unlabeled data for enhanced emotion recognition.

Main Results:

SRAGL achieved superior performance in cross-session emotion recognition tasks, with average accuracies of 78.18%, 80.55%, and 81.90%.
The model demonstrated quick convergence and gradual optimization of emotion metrics, resulting in a reliable similarity matrix.
Identified critical frequency bands and brain regions for emotion recognition by analyzing the learned regression projection matrix.

Conclusions:

SRAGL offers a significant advancement in cross-session EEG emotion recognition, outperforming state-of-the-art algorithms.
The adaptive graph learning component effectively captures complex relationships in EEG data, enhancing label estimation.
The method provides insights into feature importance, enabling automatic identification of relevant brain regions and frequency bands for emotion detection.