Frontiers in Neurorobotics最新文献

{"title":"AMANet: a data-augmented multi-scale temporal attention convolutional network for motor imagery classification.","authors":"Shu Wang, Raofen Wang, Liang Chang, Jianzhen Wu, Lingyan Hu","doi":"10.3389/fnbot.2025.1704111","DOIUrl":"10.3389/fnbot.2025.1704111","url":null,"abstract":"<p><p>Motor imagery brain-computer interface (MI-BCI) has garnered considerable attention due to its potential for neural plasticity. However, the limited number of MI-EEG samples per subject and the susceptibility of features to noise and artifacts posed significant challenges for achieving high decoding performance. To address this problem, a Data-Augmented Multi-Scale Temporal Attention Convolutional Network (AMANet) was proposed. The network mainly consisted of four modules. First, the data augmentation module comprises three steps: sliding-window segmentation to increase sample size, Common Spatial Pattern (CSP) extraction of discriminative spatial features, and linear scaling to enhance network robustness. Then, multi-scale temporal convolution was incorporated to dynamically extract temporal and spatial features. Subsequently, the ECA attention mechanism was integrated to realize the adaptive adjustment of the weights of different channels. Finally, depthwise separable convolution was utilized to fully integrate and classify the deep extraction of temporal and spatial features. In 10-fold cross-validation, the results show that AMANet achieves classification accuracies of 84.06 and 85.09% on the BCI Competition IV Datasets 2a and 2b, respectively, significantly outperforming baseline models such as Incep-EEGNet. On the High-Gamma dataset, AMANet attains a classification accuracy of 95.48%. These results demonstrate the excellent performance of AMANet in motor imagery decoding tasks.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1704111"},"PeriodicalIF":2.8,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12827673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146051670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subdomain adaptation method based on transferable semantic alignment and class correlation.","authors":"Qian Han, Jinfu Lao, Jinyong Zhang","doi":"10.3389/fnbot.2025.1665528","DOIUrl":"10.3389/fnbot.2025.1665528","url":null,"abstract":"<p><p>To address these challenges, we propose a subdomain adaptation framework driven by transferable semantic alignment and class correlation. First, source and target domains are divided into subdomains according to class labels, and a joint subdomain distribution alignment mechanism is introduced to reduce intra-class distribution divergence while enlarging inter-class disparities. Second, a domain-adaptive semantic consistency loss is employed to cluster semantically similar samples and separate dissimilar ones in a unified representation space, enabling precise cross-domain semantic alignment. Third, pseudo-label quality in the target domain is improved via temperature-based label smoothing, complemented by a class correlation matrix and a loss function capturing inter-class relationships to exploit intrinsic intra-class coherence and inter-class distinction. Extensive experiments on multiple public datasets demonstrate that the proposed method achieves superior average classification accuracy compared to existing approaches, validating the effectiveness of semantic alignment and class correlation modeling. By explicitly modeling intra-class coherence and inter-class distinction without additional architectural complexity, the framework effectively mitigates domain shift, enhances semantic alignment, and improves recognition performance on the target domain, offering a robust solution for deep unsupervised domain adaptation.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1665528"},"PeriodicalIF":2.8,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12812969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146010095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Path planning of industrial robots based on the adaptive field cooperative sampling algorithm.","authors":"Yongbo Zhuang, Sha Luo, Qingdang Li, Dianming Chu, Wenjuan Bai, Xintao Liu, Mingyuan Fan, Lv Wei","doi":"10.3389/fnbot.2025.1754834","DOIUrl":"10.3389/fnbot.2025.1754834","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fnbot.2025.1574044.].</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1754834"},"PeriodicalIF":2.8,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12757013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145900306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IAP-TransUNet: integration of the attention mechanism and pyramid pooling for medical image segmentation.","authors":"Yuxuan Shi, Fang Li, Shuting Zhao, Hongmeng Yu, Xinrong Chen, Quan Liu","doi":"10.3389/fnbot.2025.1706626","DOIUrl":"10.3389/fnbot.2025.1706626","url":null,"abstract":"<p><strong>Introduction: </strong>The combination of CNN and Transformer has attracted much attention for medical image segmentation due to its superior performance at present. However, the segmentation performance is affected by limitations such as the local receptive field and static weights of CNN convolution operations, as well as insufficient information exchange between Transformer local regions.</p><p><strong>Methods: </strong>To address these issues, an integrated attention mechanism and pyramid pooling network is proposed in this paper. Firstly, an efficient channel attention mechanism is embedded into CNN to extract more comprehensive image features. Then, CBAM_ASPP module is introduced into the bottleneck layer to obtain multi-scale context information. Finally, in order to address the limitations of traditional convolution, depthwise separable convolution is used to achieve a lightweight network.</p><p><strong>Results: </strong>The experiments based on the Synapse multi organ segmentation dataset and ACDC dataset showed that the proposed IAP-TransUNet achieved Dice similarity coefficients (DSCs) of 78.85% and 90.46%, respectively. Compared with the state-of-the-art method, for the Synapse multi organ segmentation dataset, the Hausdorff distance was reduced by 2.92%. For the ACDC dataset, the segmentation accuracy of the left ventricle, myocardium, and right ventricle was improved by 0.14%, 1.89%, and 0.23%, respectively.</p><p><strong>Discussion: </strong>The experimental results demonstrate that the proposed network has improved the effectiveness and shows strong performance on both CT and MRI data, which suggests its potential for generalization across different medical imaging modalities.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1706626"},"PeriodicalIF":2.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12702935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145767822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel intelligent physiotherapy robot based on dynamic acupoint recognition method.","authors":"Yuhan Zhang, Shiyang Sun, Donghui Zhao, Junyou Yang, Shuoyu Wang","doi":"10.3389/fnbot.2025.1696824","DOIUrl":"10.3389/fnbot.2025.1696824","url":null,"abstract":"<p><strong>Background: </strong>Physiotherapy robots offer a feasible and promising solution for achieving safe and efficient treatment. Among these, acupoint recognition is the core component that ensures the precision of physiotherapy robots. Although the research on the acupoint recognition such as hand and ear has been extensive, the accurate location of acupoints on the back of the human body still faces great challenges due to the lack of significant external features.</p><p><strong>Methods: </strong>This paper designs a two-stage acupoint recognition method, which is achieved through the cooperation of two detection networks. First, a lightweight RTMDet network is used to extract the effective back range from the image, and then the acupoint coordinates are inferred from the extracted back range, reducing the inference consumption caused by invalid information. In addition, the RTMPose network based on the SimCC framework converts the acupoint coordinate regression problem into a classification problem of sub-pixel block subregions on the X and Y axes by performing sub-pixel-level segmentation of images, significantly improving detection speed and accuracy. Meanwhile, the multi-layer feature fusion of CSPNeXt enhances feature extraction capabilities. Then, we designed a physiotherapy interaction interface. Through the three-dimensional coordinates of the acupoints, we independently planned the physiotherapy task path of the physiotherapy robot.</p><p><strong>Results: </strong>We conducted performance tests on the acupoint recognition system and physiotherapy task planning in the physiotherapy robot system. The experiments have proven our effectiveness, achieving a recall of 90.17% on human datasets, with a detection error of around 5.78 mm. At the same time, it can accurately identify different back postures and achieve an inference speed of 30 FPS on a 4070Ti GPU. Finally, we conducted continuous physiotherapy tasks on multiple acupoints for the user.</p><p><strong>Conclusion: </strong>The experimental results demonstrate the significant advantages and broad application potential of this method in improving the accuracy and reliability of autonomous acupoint recognition by physiotherapy robots.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1696824"},"PeriodicalIF":2.8,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12682758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145714114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On collective behavior in <i>C. elegans</i>.","authors":"Nemanja Antonic, Aymeric Vellinger, Elio Tuci","doi":"10.3389/fnbot.2025.1689332","DOIUrl":"10.3389/fnbot.2025.1689332","url":null,"abstract":"<p><p><i>C. elegans</i> is a model organism in many biological domains, such as genetics, neurophysiology, and behavioral ecology. Despite our relatively deep knowledge of the neuronal, genetic and molecular mechanisms underlying <i>C. elegans</i> communication, we still lack a comprehensive understanding of emergent group-level dynamics. We review the literature on collective behavior of <i>C. elegans</i> by categorizing works in this relatively small research field along three main axes corresponding to primary collective responses: aggregation, swarming, and collective decision-making. Through an analysis of the methods and scientific contributions of these works, we develop a critical perspective that points to important gaps in our understanding of the mechanisms underlaying the emergence of collective responses. We discuss the consequences of the lack of evidence concerning the effect of population density on the emergence of specific group dynamics, and the relatively limited knowledge related to how self-generated pheromones regulate local interactions and contribute to the emergence of group responses. We elaborate on the methodological problems of developing experimental scenarios to disentangle causal relationships between population density, pheromone-based interactions and collective responses. We propose to overcome these limitations with an interdisciplinary approach based on the use of <i>in vivo</i> experiments, mathematical and computer-based models.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1689332"},"PeriodicalIF":2.8,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12665682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145660934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path planning of industrial robots based on the adaptive field cooperative sampling algorithm.","authors":"Yongbo Zhuang, Sha Luo, Qingdang Li, Dianming Chu, Wenjuan Bai, Xintao Liu, Mingyuan Fan, Lv Wei","doi":"10.3389/fnbot.2025.1574044","DOIUrl":"10.3389/fnbot.2025.1574044","url":null,"abstract":"<p><p>For the low efficiency and poor generalization ability of path planning algorithm of industrial robots, this work proposes an adaptive field co-sampling algorithm (AFCS). Firstly, the environment complexity function is proposed to make full use of environment information and improve its generalization ability of the traditional rapidly random search tree algorithm (RRT) algorithm. Then an optimal sampling strategy is proposed to make the improvement of the efficiency and optimal direction of RRT algorithm. Finally, this article designs a collaborative extension strategy, which introduces the improved artificial potential field algorithm (APF) into the traditional RRT algorithm to determine the new nodes, so as to improve the orientation and expansion efficiency of the algorithm. The proposed AFCS algorithm completes simulation experiments in two environments with different complexity. Compared with the traditional RRT, RRT* and tRRT algorithm, the results show that the AFCS algorithm has achieved great improvement in environmental adaptability, stability and efficiency. At last, ROKAE industrial robot is taken as the object to build a simulation environment for the path planning, which further verifies the practicability of the algorithm.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1574044"},"PeriodicalIF":3.5,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12657479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145648374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: RSA-TransUNet: a robust structure-adaptive TransUNet for enhanced road crack segmentation.","authors":"Liling Hou, Fei Yu, Yaowen Hu, Yang Hu, Ruoli Yang","doi":"10.3389/fnbot.2025.1711642","DOIUrl":"https://doi.org/10.3389/fnbot.2025.1711642","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fnbot.2025.1633697.].</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1711642"},"PeriodicalIF":2.8,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12641505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145603799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective and efficient self-supervised masked model based on mixed feature training.","authors":"Qingjiu Kang, Feng Liu, Chunliu Cai","doi":"10.3389/fnbot.2025.1705970","DOIUrl":"https://doi.org/10.3389/fnbot.2025.1705970","url":null,"abstract":"<p><p>Under the influence of Masked Language Modeling (MLM), Masked Image Modeling (MIM) employs an attention mechanism to perform masked training on images. However, processing a single image requires numerous iterations and substantial computational resources to reconstruct the masked regions, resulting in high computational complexity and significant time costs. To address this issue, we propose an Effective and Efficient self-supervised Masked model based on Mixed feature training (EESMM). First, we stack two images for encoding and input the fused features into the network, which not only reduces computational complexity but also enables the learning of more features. Second, during decoding, we obtain the decoding features corresponding to the original images based on the decoding features of the two input original images and the mixed images, and then construct a corresponding loss function to enhance feature representation. EESMM significantly reduces pre-training time without sacrificing accuracy, achieving 83% accuracy on ImageNet in just 363 h using four V100 GPUs-only one-tenth of the training time required by SimMIM. This validates that the method can substantially accelerate the pre-training process without noticeable performance degradation.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1705970"},"PeriodicalIF":2.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12611909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145540224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simple robot suggests trunk rotation is essential for emergence of inside leading limb during quadruped galloping turns.","authors":"Tomoe Maeta, Shoei Hattori, Takeshi Kano, Akira Fukuhara, Akio Ishiguro","doi":"10.3389/fnbot.2025.1628368","DOIUrl":"10.3389/fnbot.2025.1628368","url":null,"abstract":"<p><p>During turning maneuvers in the galloping gait of quadruped animals, a strong relationship exists between the turning direction and the sequence in which the forelimbs make ground contact: the outer forelimb acts as the \"trailing limb\" while the inner forelimb serves as the \"leading limb.\" However, the control mechanisms underlying this behavior remain largely unclear. Understanding these mechanisms could deepen biological knowledge and assist in developing more agile robots. To address this issue, we hypothesized that decentralized interlimb coordination mechanism and trunk movement are essential for the emergence of an inside leading limb in a galloping turn. To test the hypothesis, we developed a quasi-quadruped robot with simplified wheeled hind limbs and variable trunk roll and yaw angles. For forelimb coordination, we implemented a simple decentralized control based on local load-dependent sensory feedback, utilizing trunk roll inclination and yaw bending as turning methods. Our experimental results confirmed that in addition to the decentralized control from previous studies which reproduces animal locomotion in a straight line, adjusting the trunk roll angle spontaneously generates a ground contact sequence similar to gallop turning in quadruped animals. Furthermore, roll inclination showed a greater influence than yaw bending on differentiating the leading and trailing limbs. This study suggests that physical interactions serve as a universal mechanism of locomotor control in both forward and turning movements of quadrupedal animals.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 ","pages":"1628368"},"PeriodicalIF":2.8,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12590251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145481800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}