Search Results for author: Gan Huang

Found 6 papers, 2 papers with code

DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis

no code implementations30 Jul 2024 Yue Pan, Qile Liu, Qing Liu, Li Zhang, Gan Huang, Xin Chen, Fali Li, Peng Xu, Zhen Liang

To address this issue, we propose a Dual Attentive (DuA) transformer framework for long-term continuous EEG emotion analysis.

EEG Emotion Recognition +1

Vox-Fusion++: Voxel-based Neural Implicit Dense Tracking and Mapping with Multi-maps

no code implementations19 Mar 2024 Hongjia Zhai, Hai Li, Xingrui Yang, Gan Huang, Yuhang Ming, Hujun Bao, Guofeng Zhang

In this paper, we introduce Vox-Fusion++, a multi-maps-based robust dense tracking and mapping system that seamlessly fuses neural implicit representations with traditional volumetric fusion techniques.

RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments

1 code implementation23 Oct 2023 Jinyu Li, Xiaokun Pan, Gan Huang, Ziyang Zhang, Nan Wang, Hujun Bao, Guofeng Zhang

In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems.

EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition

1 code implementation27 Mar 2023 Rushuang Zhou, Weishan Ye, Zhiguo Zhang, Yanyang Luo, Li Zhang, Linling Li, Gan Huang, Yining Dong, Yuan-Ting Zhang, Zhen Liang

The results show the proposed EEGmatch performs better than the state-of-the-art methods under different incomplete label conditions (with 6. 89% improvement on SEED and 1. 44% improvement on SEED-IV), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals.

Data Augmentation Domain Adaptation +3

EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition

no code implementations7 Feb 2021 Zhen Liang, Rushuang Zhou, Li Zhang, Linling Li, Gan Huang, Zhiguo Zhang, Shin Ishii

The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases.

EEG Emotion Recognition +2

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