生物医学工程学杂志最新文献_第7页

[Multi-lead electrocardiogram atrial fibrillation detection algorithm based on multi-scale patch-based attention]. [基于多尺度贴片关注的多导联心电图房颤检测算法]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202511015

Xiaojia Wang, Ruidong Yan, Mingxin Zhang, Chunfeng Yang, Yushun Gong

{"title":"[Multi-lead electrocardiogram atrial fibrillation detection algorithm based on multi-scale patch-based attention].","authors":"Xiaojia Wang, Ruidong Yan, Mingxin Zhang, Chunfeng Yang, Yushun Gong","doi":"10.7507/1001-5515.202511015","DOIUrl":"10.7507/1001-5515.202511015","url":null,"abstract":"Aiming at the deficiencies of insufficient cross-domain generalization and poor rhythm sensitivity in atrial fibrillation (AF) detection from multi-lead electrocardiogram (ECG) signals, this paper proposes a novel AF detection algorithm based on multi-scale patch attention fusion. The method segmented ECG signals into overlapping temporal fragments of different scales to capture local waveform details and long-range rhythm patterns respectively; it fused cross-scale feature information through a multi-scale attention mechanism to strengthen the model's ability to perceive local and global rhythm features, and introduced the self-attention mechanism of Transformer to capture long-range rhythm correlations among fragments, thus realizing in-depth mining of ECG features. The algorithm was validated on the public CinC2021 dataset and the self-constructed clinical Clin-ECG dataset. Experimental results showed that the algorithm achieved an accuracy of 94.6% and 92.7% on the two datasets, with F1 scores reaching 0.945 and 0.923, respectively. Compared with baseline models such as ECG-ResNet and CNN-BiLSTM, the proposed algorithm exhibited higher accuracy and better cross-dataset generalization ability, providing an effective method for the automatic detection of AF from multi-lead ECG signals.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"546-553"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Research on the classification method of unbalanced data for the assessment of traffic accident disability level]. [交通事故伤残程度评定中不平衡数据的分类方法研究]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202508042

Hubin Yan, Shaohua Wang, Jianlin Jia, Wenhui Qin

{"title":"[Research on the classification method of unbalanced data for the assessment of traffic accident disability level].","authors":"Hubin Yan, Shaohua Wang, Jianlin Jia, Wenhui Qin","doi":"10.7507/1001-5515.202508042","DOIUrl":"10.7507/1001-5515.202508042","url":null,"abstract":"The evaluation of disability grades in traffic accidents is a professional forensic clinical appraisal matter, and its results directly affect the fairness of judicial compensation. In the construction of automated disability grade evaluation models, the imbalanced distribution of disability cases leads to low recognition accuracy for minority categories, becoming a key bottleneck restricting the technology's implementation. In response, this paper proposes an imbalanced data classification method based on a hybrid parameter scaling weight optimization mechanism. First, a loss weight calculation model is constructed based on category proportion, category sparsity, and category diversity. Second, the loss weight calculation model is designed by integrating the focal loss function's ability to focus on hard samples with the cross-entropy loss function's global gradient stability advantage. Then, at the early stages of training, the model proposed in this paper aligns sensitivity to imbalanced categories and constructs a low-computational-demand hybrid parameter scaling weight optimization mechanism. Experimental results show that, compared with the best-performing baseline methods, the proposed method significantly improves both accuracy and macro-F1 score on the traffic accident disability grade dataset. It can effectively enhance the classification performance of minority grade categories in imbalanced data and help improve the accuracy of automated appraisal in judicial identification of traffic accident disability grades.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"607-614"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[White matter microstructural disturbance and its relationship with cognitive function in schizophrenia with comorbidity of metabolic syndrome]. [伴有代谢综合征的精神分裂症患者白质微结构紊乱及其与认知功能的关系]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202603015

Jingyu Zhou, Kai Yu, Taisheng Han, Huan Huang, Mingjun Duan, María Luisa Bringas Vega, Cheng Luo, Dezhong Yao

{"title":"[White matter microstructural disturbance and its relationship with cognitive function in schizophrenia with comorbidity of metabolic syndrome].","authors":"Jingyu Zhou, Kai Yu, Taisheng Han, Huan Huang, Mingjun Duan, María Luisa Bringas Vega, Cheng Luo, Dezhong Yao","doi":"10.7507/1001-5515.202603015","DOIUrl":"10.7507/1001-5515.202603015","url":null,"abstract":"The mechanisms underlying the impact of metabolic syndrome on cognitive dysfunction in patients with schizophrenia remain unclear. The present study employed a two-factor factorial design to investigate the effects of metabolic syndrome on white matter microstructure in schizophrenia and its association with cognitive function. A total of 187 participants were included and classified into four groups based on the diagnoses of schizophrenia and metabolic syndrome: schizophrenia patients with metabolic syndrome (SZ-wMS), schizophrenia patients without metabolic syndrome, healthy controls with metabolic syndrome, and healthy controls without metabolic syndrome. Diffusion tensor imaging data were acquired. Using diffusion tensor model, fractional anisotropy (FA) was calculated to characterize the microstructural integrity of white matter. Peripheral metabolic indices and multiple domains of cognitive function were also assessed. The SZ-wMS group showed further reduced FA in the right sagittal stratum. Within the two-factor analytical framework, an interaction effect between metabolic syndrome and schizophrenia on FA in the right sagittal stratum was identified. Correlation analyses revealed that reduced FA in the right sagittal stratum was associated with impaired language function in patients with schizophrenia. Moreover, mediation analysis indicated that body mass index might indirectly affect language function by influencing FA in the right sagittal stratum. In summary, reduced integrity of white matter fibers in sagittal stratum may represent a potential neural mechanism underlying the comorbidity of schizophrenia and metabolic syndrome and may be associated with language dysfunction in schizophrenia.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"441-449"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Novel framework based on multi-stage random sampling and differentiable rendering for automated three-dimensional orientation estimation of skull base foramina]. [基于多阶段随机采样和可微渲染的颅底孔三维方向自动估计新框架]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202512001

Yonghui Li, Han Zhang, Shuai Geng, Dongliang Tan, Weili Shi, Yu Miao, Zhengang Jiang

{"title":"[Novel framework based on multi-stage random sampling and differentiable rendering for automated three-dimensional orientation estimation of skull base foramina].","authors":"Yonghui Li, Han Zhang, Shuai Geng, Dongliang Tan, Weili Shi, Yu Miao, Zhengang Jiang","doi":"10.7507/1001-5515.202512001","DOIUrl":"10.7507/1001-5515.202512001","url":null,"abstract":"The foramen ovale and foramen rotundum of the skull base serve as critical anatomical approaches for percutaneous puncture of the trigeminal ganglion. Their three-dimensional orientation is crucial for designing surgical pathways and avoiding adjacent vital structures. However, this orientation exhibits significant individual variability, and its assessment currently lacks automated quantitative methods. To address this issue, the present study formulates the puncture foramina orientation estimation as a geometric optimization problem and proposes a coarse-to-fine solution framework: first, a coarse estimate is obtained through adaptive multi-stage random sampling to rapidly cover the feasible region, followed by a refined estimate using optimization techniques based on differentiable rendering. Experiments on 64 foraminal structures from 16 subjects demonstrated that the average angular error of the automatically estimated results was 2.21 °, which is smaller than the inter-observer variability among physicians 4.81 °. The method outperforms manual judgment in terms of accuracy, providing an objective anatomical reference for personalized puncture path planning and demonstrating clear potential for clinical translation.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"580-588"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Gait recognition based on feature-level fusion of motion posture and surface electromyography]. [基于运动姿态与肌表电特征融合的步态识别]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202509001

Chao Ma, Mu Li, Jie Yao

{"title":"[Gait recognition based on feature-level fusion of motion posture and surface electromyography].","authors":"Chao Ma, Mu Li, Jie Yao","doi":"10.7507/1001-5515.202509001","DOIUrl":"10.7507/1001-5515.202509001","url":null,"abstract":"To address the problems of misidentification of similar gaits, excessive feature dimensionality, and computational complexity in gait recognition, this paper proposes a gait recognition method based on feature-level fusion. After validating the complementarity between motion posture signals and surface electromyography (sEMG) signals, parameters in the time, frequency, and time-frequency domains of the two types of signals were extracted. Based on the energy distribution of acceleration, angular velocity, and angle signals from motion posture signals, feature-level fusion was performed. A dual constraint strategy combining Gain-based discriminability filtering and energy-ratio stability filtering was adopted to reduce feature dimensions, yielding the most discriminative feature subset, upon which the XGBoost model was applied for gait recognition. Experimental results showed that the proposed method improved the average recognition accuracy by 8.6% over the baseline model that used only motion posture signals, reaching 95.8%. Specifically, the accuracies for forward, backward, and turning gaits reached 89.8%, 95.2%, and 97.3%, respectively, effectively reducing the misidentification rates for these three similar gaits. Furthermore, the feature-level fusion strategy effectively improved computational efficiency, and the energy distribution-based feature selection strategy reduced the impact of background noise on feature parameter perturbations, thereby enhancing model stability. This method provides strong technical support and engineering application value for gait feature parameter identification and real-time intelligent gait recognition control of exoskeletons.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"598-606"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Research on deep learning-based pathological image classification of pilonidal sinus]. 基于深度学习的脊髓窦病理图像分类研究

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202512031

Qi Wang, Siqi Ben, Xiuyan Li, Qun Ma, Shunli Qi, Shifei Liu, Aidong Liu

{"title":"[Research on deep learning-based pathological image classification of pilonidal sinus].","authors":"Qi Wang, Siqi Ben, Xiuyan Li, Qun Ma, Shunli Qi, Shifei Liu, Aidong Liu","doi":"10.7507/1001-5515.202512031","DOIUrl":"10.7507/1001-5515.202512031","url":null,"abstract":"Pilonidal sinus disease (PSD) is a suppurative inflammatory condition characterized by a sinus tract in the sacrococcygeal gluteal cleft and surrounding skin. Surgical resection is an important treatment for PSD; however, postoperative recurrence occurs in some patients, severely affecting their quality of life. Due to the lack of objective clinical indicators for predicting recurrence, developing an artificial intelligence-based prediction model is an effective approach. In this study, postoperative hematoxylin and eosin (H&E) stained pathological sections from PSD patients were used. The horizontal and vertical distance network (HoVer-Net) deep learning model for automated nuclei segmentation and classification was employed to extract nuclear features from lesion regions. These features were then integrated with multidimensional clinical characteristics to select key features. And then a machine learning classifier was incorporated to construct a recursive feature elimination-balanced random forest (RFE-BalancedRF) ensemble model to predict PSD recurrence. Experimental results demonstrate that the proposed PSD recurrence prediction model achieves an area under the curve of receiver operating characteristic (ROC_AUC) of 0.751 ± 0.048 and an accuracy (Acc) of 0.715 ± 0.066. Furthermore, a correlation exists between multimodal features and the risk of PSD recurrence, confirming the effectiveness of the proposed model in predicting PSD recurrence. This study innovatively integrates radiomics features with clinical characteristics from the dual perspectives of pathological image analysis and multimodal feature fusion to construct a PSD recurrence risk prediction model, potentially providing a new pathway with considerable clinical translation potential for artificial intelligence assisted medicine.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"562-570"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Finite-element simulation and experimental investigation of nanosecond transcranial pulsed electric field propagation and distribution in a three-dimensional brain model]. [纳秒级经颅脉冲电场在三维脑模型中的传播与分布的有限元模拟与实验研究]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202512068

Yue Chen, Chenguo Yao, Fanping Yan, Pangxin Xiao, Xiaoyu Lu

{"title":"[Finite-element simulation and experimental investigation of nanosecond transcranial pulsed electric field propagation and distribution in a three-dimensional brain model].","authors":"Yue Chen, Chenguo Yao, Fanping Yan, Pangxin Xiao, Xiaoyu Lu","doi":"10.7507/1001-5515.202512068","DOIUrl":"10.7507/1001-5515.202512068","url":null,"abstract":"Nanosecond pulsed electric field (nsPEF) exposure can disrupt and disaggregate amyloid-β, indicating its potential to improve symptoms of Alzheimer's disease. However, the propagation and distribution patterns of nsPEF within brain tissue remain insufficiently understood, making related simulation analysis necessary. In this study, a high-resolution three-dimensional human head model incorporating the scalp, skull, cerebrospinal fluid, gray matter, white matter, and hippocampus was constructed. Based on the spectral characteristics of nsPEF, the dielectric properties of human tissues at different frequency ranges were assigned, and a transient finite-element model of nsPEF exposure in the human brain was established. The simulation analysis identified two optimal electrode-pair positions and characterized the spatial distributions of intracranial electric field strength as well as current density. It further elucidated the dependence of the hippocampal electric field response and current density on pulse parameters. In addition, a physical human brain model was constructed to experimentally validate the finite-element simulation results. The results showed that transcranial nsPEF can reach deep brain regions with extremely narrow pulse widths, and pulsed electric fields with kilovolt-level amplitudes and nanosecond-scale pulse widths can generate electric field strengths of approximately 10 3 V/m in the hippocampus. In summary, this work provides a theoretical basis and experimental support for optimizing the electrode configuration and stimulation parameters of transcranial nsPEF, thereby laying a foundation for future research on its application in non-invasive physical interventions for Alzheimer's disease.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"504-512"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Research on surgical instrument segmentation algorithm based on frequency-domain adaptive feature decomposition and visual Mamba]. 基于频域自适应特征分解和视觉曼巴的手术器械分割算法研究

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202510015

Zili Tan, Wenxuan He, Yujie Cao, Ronghua Chi

{"title":"[Research on surgical instrument segmentation algorithm based on frequency-domain adaptive feature decomposition and visual Mamba].","authors":"Zili Tan, Wenxuan He, Yujie Cao, Ronghua Chi","doi":"10.7507/1001-5515.202510015","DOIUrl":"10.7507/1001-5515.202510015","url":null,"abstract":"For the endoscopic surgical instrument segmentation task, existing methods have failed to address the semantic gap caused by the mismatch between high-frequency spatial details and low-frequency semantic features in the U-shaped network (U-Net) architecture. This study proposes a U-Net algorithm based on frequency-domain adaptive feature decomposition and visual Mamba, namely frequency-domain decoupling Mamba U-Net (FDMUNet), for surgical instrument segmentation. This algorithm embeds a frequency-domain adaptive feature enhancement module into the skip connections, and decomposes features into high-frequency and low-frequency components through the Fourier transform and a learnable filter, followed by channel weighting and fusion, so as to enhance surgical instrument edge information and bridge the semantic gap between encoder and decoder features. On the Endoscopic Vision Challenge 2017 public dataset, FDMUNet achieved Intersection over Union, mean Intersection over Union, and mean class Intersection over Union scores of 70.79%, 74.25%, and 69.50%, respectively. In addition, the ablation experiment further verified the effectiveness of the proposed module. This method not only provides a new solution for instrument segmentation in complex scenes, but also provides a new research idea for the application of frequency-domain information in medical image segmentation.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"554-561"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Graph convolution-based brain network analysis of visual mismatch negativity in adults with attention-deficit/hyperactivity disorder]. [基于图卷积的脑网络分析成人注意缺陷/多动障碍的视觉错配消极性]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202507079

Min Zheng, Chen Dang, Li Sun, Ling Zou

{"title":"[Graph convolution-based brain network analysis of visual mismatch negativity in adults with attention-deficit/hyperactivity disorder].","authors":"Min Zheng, Chen Dang, Li Sun, Ling Zou","doi":"10.7507/1001-5515.202507079","DOIUrl":"10.7507/1001-5515.202507079","url":null,"abstract":"Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Adults with ADHD continue to exhibit deficits in attention and executive function, whereas the neural mechanisms underlying visual mismatch negativity (vMMN)-related processing remain unclear. This study investigated the functional brain connectivity characteristics of adults with ADHD by combining standardized low-resolution brain electromagnetic tomography (sLORETA), phase-locking value (PLV) analysis, and a graph convolutional network (GCN) based on a visual Oddball paradigm that elicited vMMN responses. Electroencephalography (EEG) data were collected from 10 adults with ADHD and 10 healthy controls using a 128-channel recording system. Source signals from 68 brain regions defined by the Desikan-Killiany atlas were reconstructed using sLORETA. Functional connectivity networks were constructed using PLV and subsequently classified by the GCN model. The results showed that the accuracy, precision, recall, and F1-score of the GCN model under five-fold cross-validation were (85.13 ± 1.94)%, (80.58 ± 2.08)%, (86.04 ± 1.76)%, and (83.21 ± 1.89)%, respectively. Node feature weights and classification contribution analyses identified the lingual gyrus, calcarine fissure and surrounding cortex, parahippocampal gyrus, and precuneus as highly discriminative brain regions. These findings indicate that adults with ADHD exhibit abnormal functional connectivity patterns during vMMN-related processing and provide evidence for the auxiliary identification and neural mechanism investigation of ADHD.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"521-529"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer]. [基于多分支卷积神经网络和Transformer的脑电图情感识别方法]。

生物医学工程学杂志 Pub Date : 2026-06-25 DOI: 10.7507/1001-5515.202602031

Jialin Chen, Dongsheng Liao, Pengli Liu, Yabo Wang

{"title":"[An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer].","authors":"Jialin Chen, Dongsheng Liao, Pengli Liu, Yabo Wang","doi":"10.7507/1001-5515.202602031","DOIUrl":"10.7507/1001-5515.202602031","url":null,"abstract":"Electroencephalogram (EEG)-based emotion recognition is an important research area in affective computing and mental health assessment. To address the insufficient modeling of long-term dependencies in EEG signals, this paper proposes an EEG emotion recognition method based on multi-branch convolutional neural networks (CNN) and Transformer (MCT). The proposed method employs a multi-scale CNN to extract local temporal features from EEG signals and constructs a parallel CNN-Transformer architecture to capture both short-term variations and long-term dependencies, thereby enabling temporal feature modeling at different time scales. Furthermore, a dual-branch convolutional structure is utilized to learn both global and local spatial channel features of EEG signals. A convolutional block attention module (CBAM) is then introduced to fuse the spatio-temporal features of EEG signals. Experimental results show that the proposed MCT model achieves a classification accuracy of 83.83% on the Shanghai Jiao Tong University emotion EEG dataset (SEED). On the music emotion EEG dataset (MEEG), it attains accuracies of 90.00% and 92.62% for the arousal and valence dimensions, respectively. On the database for emotion analysis using physiological signals (DEAP), it attains accuracies of 61.30% and 61.04% for the arousal and valence dimensions. The accuracy results on all three datasets outperform those of the best-performing baseline models. These findings indicate that MCT can effectively learn discriminative features associated with emotional states, providing a new perspective for EEG-based emotion recognition research.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"513-520"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0