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Related Experiment Video

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in

Dongwei Chen1, Rui Miao2, Zhaoyong Deng3,4

  • 1Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Zhuhai, China.

Frontiers in Computational Neuroscience
|August 16, 2021
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Summary

This study introduces a new model for emotion classification using electroencephalogram (EEG) data. By incorporating sensor correlation into Granger causality analysis, the SC-SGA model significantly improves emotion recognition accuracy compared to existing methods.

Keywords:
EEG sensorsL1/2-based sparse granger causality analysisL2 norm logistic regressionLASSOSC-SGAgranger causality analysis

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Area of Science:

  • Affective computing
  • Neuroscience
  • Machine learning

Background:

  • Electroencephalogram (EEG) data analysis is crucial for affective computing.
  • Granger causality analysis is a common method for EEG feature extraction in emotion classification.
  • Traditional methods are sensitive to artifacts and lack robust feature selection.

Purpose of the Study:

  • To enhance EEG feature selection and emotion classification accuracy.
  • To address limitations of conventional sparse Granger causality models.
  • To propose a novel model integrating sensor correlation as prior knowledge.

Main Methods:

  • Developed the Sparse Granger Causality analysis model based on Sensor Correlation (SC-SGA).
  • Integrated sensor correlation as prior knowledge into the L1/2 norm Granger causality framework.
  • Employed L2 norm logistic regression for emotion classification.

Main Results:

  • The SC-SGA model demonstrated superior performance on two real EEG emotion datasets.
  • Achieved emotion classification accuracy improvements ranging from 2.46% to 21.81% over existing models.
  • Validated the effectiveness of incorporating sensor correlation for enhanced feature extraction.

Conclusions:

  • Integrating sensor correlation significantly enhances sparse Granger causality models for EEG-based emotion recognition.
  • The SC-SGA model offers a more accurate and robust approach to affective computing.
  • This method provides a promising direction for future research in brain-computer interfaces and emotion analysis.