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Jincen Wang

Biographic Data

ID10055622
NAMEJincen Wang
GIVEN NAMESJincen
FAMILY NAMEWang
SIGNATUREWANG J
AFFILIATIONSMinistry of Education
ORCID0009-0001-9144-105X
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2024
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Trend-Aware Multiscale Spatial-Temporal Graph Convolution Network for P300 Detection

    Open Access•Jincen Wang, Wenming Zheng et al.•ARTICLE•IEEE Transactions on Computational…•2026

    P300-based brain–computer interfaces (BCIs) enable direct communication between the brain and external devices by decoding P300 potentials, playing a vital role in rehabilitation and cognitive neuroscience research. Accurate detection of P300 potentials is essential for the successful implementation of P300-based BCIs. Current mainstream P300 detection algorithms are based on multichannel electroencephalogram (EEG) signals, and while promising de…

  • Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

    Open Access•Yan Zhao, Yuan Zong et al.•ARTICLE•IEEE Transactions on Computational…•2024

    In this article, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDANs) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDANs), whose key contribution lies in the introduction of a novel regularization term called implicit distribution alignment (IDA). This t…

No prominent works on this page.

  • Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

    Open Access•Yan Zhao, Yuan Zong et al.•ARTICLE•IEEE Transactions on Computational…•2024

    In this article, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDANs) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDANs), whose key contribution lies in the introduction of a novel regularization term called implicit distribution alignment (IDA). This t…

  • Trend-Aware Multiscale Spatial-Temporal Graph Convolution Network for P300 Detection

    Open Access•Jincen Wang, Wenming Zheng et al.•ARTICLE•IEEE Transactions on Computational…•2026

    P300-based brain–computer interfaces (BCIs) enable direct communication between the brain and external devices by decoding P300 potentials, playing a vital role in rehabilitation and cognitive neuroscience research. Accurate detection of P300 potentials is essential for the successful implementation of P300-based BCIs. Current mainstream P300 detection algorithms are based on multichannel electroencephalogram (EEG) signals, and while promising de…

Advanced Neural Network Applications (1 work) · Adversarial Robustness in Machine Learning (1 work) · Anomaly Detection Techniques and Applications (1 work) · Artificial Intelligence (1 work) · Computation (1 work) · Computer Science (1 work) · Emotion and Mood Recognition (1 work) · Emotion recognition (1 work) · Graph (1 work) · Graph theory (1 work)

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