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    This study introduces robust principal component analysis (RPCA)-embedded transfer learning (TL) to create personalized electroencephalogram (EEG) emotion models with less data. RPCA processing is crucial for cross-day enhancements in both subject-dependent and TL models.

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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Signal Processing

    Background:

    • Personalized electroencephalogram (EEG)-based emotion classification is limited by non-stationarity and the need for extensive labeled data.
    • Collecting multi-day labeled EEG data is impractical due to time and labor constraints.
    • Existing methods struggle to account for intra- and inter-individual differences in EEG signals.

    Purpose of the Study:

    • To propose a novel approach using robust principal component analysis (RPCA)-embedded transfer learning (TL) to generate personalized cross-day EEG emotion classification models.
    • To reduce the requirement for labeled data in personalized EEG emotion recognition.
    • To mitigate intra- and inter-individual differences in EEG data for improved model generalization.

    Main Methods:

    • Implementation of transfer learning (TL) with a focus on within-dataset (wdTL) and cross-dataset (cdTL) approaches.
    • Integration of robust principal component analysis (RPCA) preprocessing to handle EEG signal non-stationarity and noise.
    • Validation using two distinct datasets (MDME and SDMN) with add-session-in methodology.

    Main Results:

    • TL significantly improved valence and arousal classification accuracy by incorporating additional source sessions, even with limited initial data.
    • Within-dataset TL (wdTL) marginally outperformed subject-dependent (SD) models when tested on a later day's session.
    • Cross-dataset TL (cdTL) showed promise for valence but was less effective for arousal classification.
    • Crucially, cross-day performance enhancements in both SD and TL scenarios were only observed after RPCA processing.

    Conclusions:

    • RPCA-embedded TL offers a viable solution for building personalized EEG emotion classification models with reduced data requirements.
    • The proposed method effectively addresses EEG non-stationarity and individual variability, enabling robust cross-day emotion recognition.
    • This research paves the way for leveraging large, diverse EEG repositories to construct more accurate and personalized emotion detection systems.