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Duo Chen

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7 papers

1 author row

JBHI Journal 2026 Journal Article

Interictal Epileptiform Discharge Detection Using Dual-Domain Features and GAN

Wenhao Rao
Jiayang Guo
Chunran Zhu
Meiyan Xu
Naian Xiao
Yijie Pan
Ling Zhang
Xiaowen Ye

Interictal Epileptiform Discharge is essential for identifying epilepsy. However, the unpredictable and non-stationary nature of electroencephalogram (EEG) patterns poses considerable challenges for reliable identification. Manual interpretation of EEG is subjective and time-consuming. With advancements in machine learning and deep learning, computer-aided approaches for automated IED detection have been rapidly developed. The state-of-the-art convolutional neural network (CNN)-based methods have shown promising results but struggle to capture long-term dependencies in time-series data. In contrast, Transformer excels at modeling sequential information through self-attention mechanisms, overcoming the CNN limitations. This study proposes an IED Detector (IEDD) that integrates convolutional layers and a Transformer to detect IEDs. The IEDD initially employs convolutional layers to extract local features of IEDs, followed by a Transformer to model long-term dependencies. To further extract spatial features, EEG data are represented as a three-dimensional tensor with embedded channel topology, where a CNN captures spatial features at each sampling point and a Long Short-Term Memory (LSTM) network models their temporal evolution. Additionally, due to the scarcity of IED data, a novel Transformer-based Generative Adversarial Network (GAN) is developed to augment the IED dataset. Experimental results show the proposed approach achieves an average accuracy of 96. 11% on the augmented Dataset 1 and 95. 25% on Dataset 2 for binary classification, with an average sensitivity of 87. 26% and precision of 89. 96% for multi-label classification. These findings provide valuable insights into advancing deep learning and Transformer-based approaches for automated IED detection.

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JBHI Journal 2025 Journal Article

Acupuncture State Detection at Zusanli (ST-36) Based on Scalp EEG and Transformer

Wenhao Rao
Meiyan Xu
Haochen Wang
Weicheng Hua
Jiayang Guo
Yongheng Zhang
Haibin Zhu
Ziqiu Zhou

In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acupuncture states. Scalp EEG has been proven significantly relevant to brain activity and acupuncture stimulation. In this work, we designed an acupuncture paradigm to collect scalp EEG to study the differences in EEG changes during different acupuncture states. Since deep learning (DL) has been increasingly used in EEG analysis, we propose the Acupuncture Transformer Detector (ATD), a model based on Convolutional Neural Networks (CNN) and Transformer technology. ATD encapsulates the local and global features of EEG under the acupuncture states of Zusanli acupoint (ST-36) in an end-to-end classification framework. The experiment results from 28 healthy participants show that the proposed model can efficiently classify the EEG in different states, with an accuracy of $85. 47\pm 0. 73\%$. In this study, time-frequency analysis revealed that power changes were mainly confined to the delta frequency band under different acupuncture states. Brain topography revealed that ST-36 was activated primarily on the left frontal and parieto-occipital areas. This method provides new ideas for automatic recognition of acupuncture status from the perspective of DL, offering new solutions for standardizing acupuncture procedures.

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JBHI Journal 2025 Journal Article

An Optimization Strategy Allowing a Tactile Glove With Minimal Tactile Sensors for Soft Object Identification

Min Tang
Xiaoyu Liu
Xiaofeng Qiao
Yuanjie Zhu
Linyuan Fan
Songjun Du
Duo Chen
Jinghui Wang

Humans can easily perceive the shapes and textures of grasped objects due to high-density mechanoreceptor networks in the hand. However, replicating this capability in wearable devices with limited sensors remains challenging. Here, we designed a tactile glove equipped with easily accessible sensors, enabling accurate identification of soft objects during grasping. We propose an optimization strategy to eliminate redundant sensors and determine the minimal sensor configuration, which was then integrated into the tactile glove. The results indicate that the minimal sensor configuration (n = 7) attached to the hand achieved accurate identification comparable to that obtained using a larger number of sensors (n = 22) distributed across the hand before elimination. Furthermore, we found that various machine learning classifiers achieved recognition accuracies of up to 90% for soft objects when using the tactile glove. Correlation analyses were conducted to characterize individual contribution and mutual cooperativity of regional tactile forces on the hand during grasping, aiding in the interpretation of sensor selection or elimination in the optimization strategy. Adequate validation and analysis demonstrate that our strategy allows an easy–to–apply solution for identifying soft objects via a tactile glove with a minimal number of sensors, offering valuable insights for guiding the design of tactile sensor layouts in artificial limbs and robotic teleoperation systems.

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JBHI Journal 2025 Journal Article

REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification

Meiyan Xu
Jie Jiao
Duo Chen
Yi Ding
Qingqing Chen
Jipeng Wu
Peipei Gu
Yijie Pan

High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77. 99% on BCI-C IV-2a and an accuracy of 63. 94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.

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EAAI Journal 2024 Journal Article

A robust defect detection method with a generalization enhancer and cross-modality aggregator for cylinder bores

Xujie He
Jing Jin
Duo Chen
Cangtian Zhou

High-quality cylinder bores in automobile engines enable drivers to respond quickly to emergencies. Automated detection methods are gradually being adopted across various industries. However, uncontrollable factors and improper preservation methods lead to various types of defects on cylinder bores, thereby causing existing high-performance detectors to exhibit not only undesirable generalizability for unseen defect types but also a certain degree of missed detection for defects of seen types, thereby allowing defective cylinder bores to flow into the market. To address these issues, we propose a foundation-model-based robust defect detection method with high generalizability for cylinder bores (RHG-Detector). Specifically, to address unseen defect categories, we propose a generalization enhancer comprising a box filter, a region extractor and a defect discriminator (DeDi) based on a foundation model to extend defect detection from a closed set to an open set. To reduce missed detections, we adopt a cross-modality aggregator to aggregate the detection results from different modalities. Additionally, we collected and annotated challenging defect classification and detection datasets for cylinder bores, named HIT-EngDC (Harbin Institute of Technology Engine defect classification dataset) and HIT-EngDD2 (Engine defect detection dataset-version 2), which cover nearly all types of cylinder bore defects. Extensive experiments on HIT-EngDC and HIT-EngDD2 demonstrate the state-of-the-art performance of RHG-Detector, with a classification accuracy of 92. 0 and a mAP@50 (mean average precision under intersection over union = 0. 5) score of 45. 2, where the latter is increased by ∼6 and ∼3 compared to the corresponding FasterRCNN (faster region-based convolutional neural networks) and YOLOv7 (you only look once) scores, respectively.

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AAAI Conference 2023 Conference Paper

Bidirectional Optical Flow NeRF: High Accuracy and High Quality under Fewer Views

Shuo Chen
Binbin Yan
Xinzhu Sang
Duo Chen
Peng Wang
Xiao Guo
Chongli Zhong
Huaming Wan

Neural Radiance Fields (NeRF) can implicitly represent 3D-consistent RGB images and geometric by optimizing an underlying continuous volumetric scene function using a sparse set of input views, which has greatly benefited view synthesis tasks. However, NeRF fails to estimate correct geometry when given fewer views, resulting in failure to synthesize novel views. Existing works rely on introducing depth images or adding depth estimation networks to resolve the problem of poor synthetic view in NeRF with fewer views. However, due to the lack of spatial consistency of the single-depth image and the poor performance of depth estimation with fewer views, the existing methods still have challenges in addressing this problem. So this paper proposes Bidirectional Optical Flow NeRF(BOF-NeRF), which addresses this problem by mining optical flow information between 2D images. Our key insight is that utilizing 2D optical flow images to design a loss can effectively guide NeRF to learn the correct geometry and synthesize the right novel view. We also propose a view-enhanced fusion method based on geometry and color consistency to solve the problem of novel view details loss in NeRF. We conduct extensive experiments on the NeRF-LLFF and DTU MVS benchmarks for novel view synthesis tasks with fewer images in different complex real scenes. We further demonstrate the robustness of BOF-NeRF under different baseline distances on the Middlebury dataset. In all cases, BOF-NeRF outperforms current state-of-the-art baselines for novel view synthesis and scene geometry estimation.

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YNIMG Journal 2022 Journal Article

The role of low-frequency oscillations in three-dimensional perception with depth cues in virtual reality

Zhili Tang
Xiaoyu Liu
Hongqiang Huo
Min Tang
Tao Liu
Zhixin Wu
Xiaofeng Qiao
Duo Chen

Currently, vision-related neuroscience studies are undergoing a trend from simplified image stimuli toward more naturalistic stimuli. Virtual reality (VR), as an emerging technology for visual immersion, provides more depth cues for three-dimensional (3D) presentation than two-dimensional (2D) image. It is still unclear whether the depth cues used to create 3D visual perception modulate specific cortical activation. Here, we constructed two visual stimuli presented by stereoscopic vision in VR and graphical projection with 2D image, respectively, and used electroencephalography to examine neural oscillations and their functional connectivity during 3D perception. We find that neural oscillations are specific to delta and theta bands in stereoscopic vision and the functional connectivity in the two bands increase in cortical areas related to visual pathways. These findings indicate that low-frequency oscillations play an important role in 3D perception with depth cues.

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