1 code implementation • 4 Mar 2024 • Xiongri Shen, Zhenxi Song, Zhiguo Zhang
The specific design can make the model focus more on the current network correlation and employ the global insight of the transformer to reconstruct FC, which both help the generation of high-quality target label FC.
no code implementations • 27 Feb 2024 • Jiaqi Wang, Zhenxi Song, Zhengyu Ma, Xipeng Qiu, Min Zhang, Zhiguo Zhang
Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs).
1 code implementation • 2 Jan 2024 • Xiongri Shen, Zhenxi Song, Linling Li, Min Zhang, Lingyan Liang Honghai Liu, Demao Deng, Zhiguo Zhang
Early diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) utilizing multi-modal magnetic resonance imaging (MRI) is a pivotal area of research.
no code implementations • 13 Aug 2023 • Weishan Ye, Zhiguo Zhang, Min Zhang, Fei Teng, Li Zhang, Linling Li, Gan Huang, Jianhong Wang, Dong Ni, Zhen Liang
In this paper, a semi-supervised Dual-stream Self-Attentive Adversarial Graph Contrastive learning framework (termed as DS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition.
1 code implementation • 27 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.
no code implementations • 7 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.
no code implementations • IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 70, 2021 2020 • Hongtao Wang, Linfeng Xu, Anastasios Bezerianos, Chuangquan Chen, Zhiguo Zhang
Finally, the critical brain regions and connections for driving fatigue detection were investigated through the dynamically learned adjacency matrix. Index Terms— Attention-based multiscale convolutional neural network (CNN), driving fatigue, dynamical graph convolution network (GCN), electroencephalography (EEG), spatiotemporalstructure.