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A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion

Tongguang Ni1, Yuyao Ni2, Jing Xue3

  • 1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China.

Frontiers in Psychology
|August 16, 2021
PubMed
Summary

This study introduces a novel domain adaptation sparse representation classifier (DASRC) for more accurate emotion recognition using electroencephalogram (EEG) brain-computer interfaces (BCIs). The method enhances cross-domain classification performance, overcoming individual variability in EEG signals.

Keywords:
cross-datasetcross-subjectdomain adaptationelectroencephalogramemotion classification

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Area of Science:

  • Neuroscience
  • Computer Science
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) leverage electroencephalogram (EEG) for emotion recognition.
  • EEG signals exhibit significant individual variability and non-stationarity, hindering cross-domain classification (e.g., across subjects, sessions, or devices).
  • Domain adaptation techniques aim to transfer knowledge from a source domain (SD) to a target domain (TD) to address these challenges.

Purpose of the Study:

  • To propose a novel domain adaptation sparse representation classifier (DASRC) for robust EEG-based emotion classification across different domains.
  • To reduce domain distribution differences and establish a shared subspace for EEG data.
  • To learn a common domain-invariant dictionary that connects source and target domains.

Main Methods:

  • Utilized the local information preserved criterion to project EEG samples into a shared subspace.
  • Learned a common domain-invariant dictionary within the projection subspace.
  • Incorporated Principal Component Analysis (PCA) and Fisher criteria to enhance dictionary recognition ability.
  • Developed an iterative optimization method for simultaneous subspace and dictionary learning.

Main Results:

  • The proposed DASRC demonstrated feasibility and competitive performance for cross-subject and cross-dataset EEG emotion classification.
  • The method effectively reduced differences in domain distribution by projecting data into a shared subspace.
  • A common domain-invariant dictionary facilitated knowledge transfer between source and target EEG domains.

Conclusions:

  • The developed DASRC is a viable and effective approach for cross-domain EEG-based emotion recognition.
  • The method successfully addresses the challenges posed by individual variability and non-stationarity in EEG signals.
  • This work contributes to advancing the robustness and generalizability of BCI emotion classification systems.