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Weidong Dang

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4 papers

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JBHI Journal 2026 Journal Article

ML-TGNet: A Multi-Level Topology Guidance Network for Motor Imagery Decoding

Weidong Dang
Zichen Ren
Jialu Sun
Dongmei Lv
Zhangjin Xiong
Wei Guo
Zhongke Gao
Huijie Yu

Brain-computer interfaces (BCIs) based on motor imagery electroencephalogram (MI-EEG) signals have been extensively applied in various neural rehabilitation scenarios. However, existing methods primarily focus on designing complex architectures to extract spatio-temporal features from MI-EEG signals, often neglecting the brain dynamics information embedded within them. This oversight leads to the extraction of redundant information, ultimately reducing decoding performance. To address these challenges, we design a multi-level topology-guidance network (ML-TGNet) that leverages topological brain synchronization information to more effectively extract features related to MI tasks. ML-TGNet specifically comprises a multi-level topology guidance module, a feature pool module, and a multi-branch decoding module. To evaluate its performance, extensive experiments are conducted on three publicly available MI datasets: the BCI Competition IV-2a dataset, the High Gamma dataset, and the OpenBMI dataset. ML-TGNet achieves classification accuracies of 82. 33%, 96. 42%, and 85. 26% on these three datasets, respectively, outperforming current state-of-the-art models. These findings confirm the efficacy of using brain synchronization information to guide MI decoding, thereby opening a novel approach for EEG-based brain state decoding by integrating brain dynamics into deep learning.

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EAAI Journal 2024 Journal Article

A novel multiphase flow water cut modeling framework based on flow behavior-heuristic deep learning

Weidong Dang
Dongmei Lv
Feng Jing
Ping Yu
Wei Guo
Zhongke Gao

Industry 4. 0 is of great significance for the development of oil industry. One of the pivotal steps towards achieving oil Industry 4. 0 is accurately mastering the oilfield production dynamics, especially the changes of process parameters. Among many process parameters, the accurate modeling of water cut is extremely critical and difficult. In this paper, under laboratory conditions, oil–water flows with various preset water cuts are simulated. A fluid sensor, equipped with eight concave-shaped conductive electrodes, is employed to capture multi-channel measurement data, continuously recording the oil–water flow process from multiple angles. Subsequently, a novel flow behavior-heuristic deep learning model, named FBHWC model, is developed to model the relationship between the measurement data and water cut, achieving water cut measurement. The FBHWC model is guided by the complex flow behaviors of oil–water flows and consists of two key modules. Particularly, the multi-level feature fusion module focuses on the high-order feature extraction and fusion of sensor measurement data, while the multi-scale measurement module uses fully convolutional design to achieve accurate measurement of water cut. Experimental results show that the FBHWC model has excellent performance in measuring water cut, with mean square error of 0. 016%. All these open up a new venue for exploring industrial multiphase flows through combining multi-electrode sensor and deep learning.

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JBHI Journal 2021 Journal Article

Attention-Based Parallel Multiscale Convolutional Neural Network for Visual Evoked Potentials EEG Classification

Zhongke Gao
Xinlin Sun
Mingxu Liu
Weidong Dang
Chao Ma
Guanrong Chen

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

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JBHI Journal 2021 Journal Article

Rhythm-Dependent Multilayer Brain Network for the Detection of Driving Fatigue

Weidong Dang
Zhongke Gao
Dongmei Lv
Xinlin Sun
Chichao Cheng

Fatigue driving has attracted a great deal of attention for its huge influence on automobile accidents. Recognizing driving fatigue provides a primary but significant way for addressing this problem. In this paper, we first conduct the simulated driving experiments to acquire the EEG signals in alert and fatigue states. Then, for multi-channel EEG signals without pre-processing, a novel rhythm-dependent multilayer brain network (RDMB network) is developed and analyzed for driving fatigue detection. We find that there exists a significant difference between alert and fatigue states from the view of network science. Further, key sub-RDMB network based on closeness centrality are extracted. We calculate six network measures from the key sub-RDMB network and construct feature vectors to classify the alert and fatigue states. The results show that our method can respectively achieve the average accuracy of 95. 28% (with sample length of 5 s), 90. 25% (2 s), and 87. 69% (1 s), significantly higher than compared methods. All these validate the effectiveness of RDMB network for reliable driving fatigue detection via EEG.

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