Jincen Wang
Biographic Data
| ID | 10055622 |
|---|---|
| NAME | Jincen Wang |
| GIVEN NAMES | Jincen |
| FAMILY NAME | Wang |
| SIGNATURE | WANG J |
| AFFILIATIONS | Ministry of Education |
| ORCID | 0009-0001-9144-105X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Trend-Aware Multiscale Spatial-Temporal Graph Convolution Network for P300 Detection
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
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
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
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)