Frontiers in Neurorobotics最新文献

{"title":"EEG-driven automatic generation of emotive music based on transformer.","authors":"Hui Jiang, Yu Chen, Di Wu, Jinlin Yan","doi":"10.3389/fnbot.2024.1437737","DOIUrl":"10.3389/fnbot.2024.1437737","url":null,"abstract":"<p><p>Utilizing deep features from electroencephalography (EEG) data for emotional music composition provides a novel approach for creating personalized and emotionally rich music. Compared to textual data, converting continuous EEG and music data into discrete units presents significant challenges, particularly the lack of a clear and fixed vocabulary for standardizing EEG and audio data. The lack of this standard makes the mapping relationship between EEG signals and musical elements (such as rhythm, melody, and emotion) blurry and complex. Therefore, we propose a method of using clustering to create discrete representations and using the Transformer model to reverse mapping relationships. Specifically, the model uses clustering labels to segment signals and independently encodes EEG and emotional music data to construct a vocabulary, thereby achieving discrete representation. A time series dictionary was developed using clustering algorithms, which more effectively captures and utilizes the temporal and structural relationships between EEG and audio data. In response to the insensitivity to temporal information in heterogeneous data, we adopted a multi head attention mechanism and positional encoding technology to enable the model to focus on information in different subspaces, thereby enhancing the understanding of the complex internal structure of EEG and audio data. In addition, to address the mismatch between local and global information in emotion driven music generation, we introduce an audio masking prediction loss learning method. Our method generates music that <i>Hits@</i>20 On the indicator, a performance of 68.19% was achieved, which improved the score by 4.9% compared to other methods, indicating the effectiveness of this method.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1437737"},"PeriodicalIF":2.6,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11366740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119502","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":"Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix.","authors":"Jian Li, Junming Su, Weilin Yu, Xuping Mao, Zipeng Liu, Haitao Fu","doi":"10.3389/fnbot.2024.1451924","DOIUrl":"10.3389/fnbot.2024.1451924","url":null,"abstract":"<p><p>Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1451924"},"PeriodicalIF":2.6,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11366699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119503","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":"Brain-inspired biomimetic robot control: a review.","authors":"Adrià Mompó Alepuz, Dimitrios Papageorgiou, Silvia Tolu","doi":"10.3389/fnbot.2024.1395617","DOIUrl":"10.3389/fnbot.2024.1395617","url":null,"abstract":"<p><p>Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these <i>Brain-Inspired</i> control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1395617"},"PeriodicalIF":2.6,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11366706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119501","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":"The super-resolution reconstruction algorithm of multi-scale dilated convolution residual network.","authors":"Shanqin Wang, Miao Zhang, Mengjun Miao","doi":"10.3389/fnbot.2024.1436052","DOIUrl":"10.3389/fnbot.2024.1436052","url":null,"abstract":"<p><p>Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated convolution network based on dilated convolution is proposed in this paper. First, the algorithm extracts features from the same input image through the dilated convolution kernels of different receptive fields to obtain feature maps with different scales; then, through the residual attention dense block, further obtain the features of the original low resolution images, local residual connections are added to fuse multi-scale feature information between multiple channels, and residual nested networks and jump connections are used at the same time to speed up deep network convergence and avoid network degradation problems. Finally, deep network extraction features, and it is fused with input features to increase the nonlinear expression ability of the network to enhance the super-resolution reconstruction effect. Experimental results show that compared with Bicubic, SRCNN, ESPCN, VDSR, DRCN, LapSRN, MemNet, and DSRNet algorithms on the Set5, Set14, BSDS100, and Urban100 test sets, the proposed algorithm has improved peak signal-to-noise ratio and structural similarity, and reconstructed images. The visual effect is better.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1436052"},"PeriodicalIF":2.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11363189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142106541","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":"Vehicle recognition pipeline via DeepSort on aerial image datasets.","authors":"Muhammad Hanzla, Muhammad Ovais Yusuf, Naif Al Mudawi, Touseef Sadiq, Nouf Abdullah Almujally, Hameedur Rahman, Abdulwahab Alazeb, Asaad Algarni","doi":"10.3389/fnbot.2024.1430155","DOIUrl":"10.3389/fnbot.2024.1430155","url":null,"abstract":"<p><strong>Introduction: </strong>Unmanned aerial vehicles (UAVs) are widely used in various computer vision applications, especially in intelligent traffic monitoring, as they are agile and simplify operations while boosting efficiency. However, automating these procedures is still a significant challenge due to the difficulty of extracting foreground (vehicle) information from complex traffic scenes.</p><p><strong>Methods: </strong>This paper presents a unique method for autonomous vehicle surveillance that uses FCM to segment aerial images. YOLOv8, which is known for its ability to detect tiny objects, is then used to detect vehicles. Additionally, a system that utilizes ORB features is employed to support vehicle recognition, assignment, and recovery across picture frames. Vehicle tracking is accomplished using DeepSORT, which elegantly combines Kalman filtering with deep learning to achieve precise results.</p><p><strong>Results: </strong>Our proposed model demonstrates remarkable performance in vehicle identification and tracking with precision of 0.86 and 0.84 on the VEDAI and SRTID datasets, respectively, for vehicle detection.</p><p><strong>Discussion: </strong>For vehicle tracking, the model achieves accuracies of 0.89 and 0.85 on the VEDAI and SRTID datasets, respectively.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1430155"},"PeriodicalIF":2.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142106542","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":"Feature Interaction Dual Self-attention network for sequential recommendation.","authors":"Yunfeng Zhu, Shuchun Yao, Xun Sun","doi":"10.3389/fnbot.2024.1456192","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1456192","url":null,"abstract":"<p><p>Combining item feature information helps extract comprehensive sequential patterns, thereby improving the accuracy of sequential recommendations. However, existing methods usually combine features of each item using a vanilla attention mechanism. We argue that such a combination ignores the interactions between features and does not model integrated feature representations. In this study, we propose a novel Feature Interaction Dual Self-attention network (FIDS) model for sequential recommendation, which utilizes dual self-attention to capture both feature interactions and sequential transition patterns. Specifically, we first model the feature interactions for each item to form meaningful higher-order feature representations using a multi-head attention mechanism. Then, we adopt two independent self-attention networks to capture the transition patterns in both the item sequence and the integrated feature sequence, respectively. Moreover, we stack multiple self-attention blocks and add residual connections at each block for all self-attention networks. Finally, we combine the feature-wise and item-wise sequential patterns into a fully connected layer for the next item recommendation. We conduct experiments on two real-world datasets, and our experimental results show that the proposed FIDS method outperforms state-of-the-art recommendation models.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1456192"},"PeriodicalIF":2.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117065","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":"RL-CWtrans Net: multimodal swimming coaching driven via robot vision.","authors":"Guanlin Wang","doi":"10.3389/fnbot.2024.1439188","DOIUrl":"10.3389/fnbot.2024.1439188","url":null,"abstract":"<p><p>In swimming, the posture and technique of athletes are crucial for improving performance. However, traditional swimming coaches often struggle to capture and analyze athletes' movements in real-time, which limits the effectiveness of coaching. Therefore, this paper proposes RL-CWtrans Net: a robot vision-driven multimodal swimming training system that provides precise and real-time guidance and feedback to swimmers. The system utilizes the Swin-Transformer as a computer vision model to effectively extract the motion and posture features of swimmers. Additionally, with the help of the CLIP model, the system can understand natural language instructions and descriptions related to swimming. By integrating visual and textual features, the system achieves a more comprehensive and accurate information representation. Finally, by employing reinforcement learning to train an intelligent agent, the system can provide personalized guidance and feedback based on multimodal inputs. Experimental results demonstrate significant advancements in accuracy and practicality for this multimodal robot swimming coaching system. The system is capable of capturing real-time movements and providing immediate feedback, thereby enhancing the effectiveness of swimming instruction. This technology holds promise.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1439188"},"PeriodicalIF":2.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142106540","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 discrete zeroing neural network for online solving time-varying nonlinear optimization problems","authors":"Feifan Song, Yanpeng Zhou, Changxian Xu, Zhongbo Sun","doi":"10.3389/fnbot.2024.1446508","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1446508","url":null,"abstract":"To reduce transportation time, a discrete zeroing neural network (DZNN) method is proposed to solve the shortest path planning problem with a single starting point and a single target point. The shortest path planning problem is reformulated as an optimization problem, and a discrete nonlinear function related to the energy function is established so that the lowest-energy state corresponds to the optimal path solution. Theoretical analyzes demonstrate that the discrete ZNN model (DZNNM) exhibits zero stability, effectiveness, and real-time performance in handling time-varying nonlinear optimization problems (TVNOPs). Simulations with various parameters confirm the efficiency and real-time performance of the developed DZNNM for TVNOPs, indicating its suitability and superiority for solving the shortest path planning problem in real time.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"89 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLIB: Contrastive learning of ignoring background for underwater fish image classification","authors":"Qiankun Yan, Xiujuan Du, Chong Li, Xiaojing Tian","doi":"10.3389/fnbot.2024.1423848","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1423848","url":null,"abstract":"Aiming at the problem that the existing methods are insufficient in dealing with the background noise anti-interference of underwater fish images, a contrastive learning method of ignoring background called CLIB for underwater fish image classification is proposed to improve the accuracy and robustness of underwater fish image classification. First, CLIB effectively separates the subject from the background in the image through the extraction module and applies it to contrastive learning by composing three complementary views with the original image. To further improve the adaptive ability of CLIB in complex underwater images, we propose a multi-view-based contrastive loss function, whose core idea is to enhance the similarity between the original image and the subject and maximize the difference between the subject and the background, making CLIB focus more on learning the core features of the subject during the training process, and effectively ignoring the interference of background noise. Experiments on the Fish4Knowledge, Fish-gres, WildFish-30, and QUTFish-89 public datasets show that our method performs well, with improvements of 1.43–6.75%, 8.16–8.95%, 13.1–14.82%, and 3.92–6.19%, respectively, compared with the baseline model, further validating the effectiveness of CLIB.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"13 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based gait adaptation dysfunction identification using CMill-based gait data","authors":"Hang Yang, Zhenyi Liao, Hailei Zou, Kuncheng Li, Ye Zhou, Zhenzhen Gao, Yajun Mao, Caiping Song","doi":"10.3389/fnbot.2024.1421401","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1421401","url":null,"abstract":"BackgroundCombining machine learning (ML) with gait analysis is widely applicable for diagnosing abnormal gait patterns.ObjectiveTo analyze gait adaptability characteristics in stroke patients, develop ML models to identify individuals with GAD, and select optimal diagnostic models and key classification features.MethodsThis study was investigated with 30 stroke patients (mean age 42.69 years, 60% male) and 50 healthy adults (mean age 41.34 years, 58% male). Gait adaptability was assessed using a CMill treadmill on gait adaptation tasks: target stepping, slalom walking, obstacle avoidance, and speed adaptation. The preliminary analysis of variables in both groups was conducted using t-tests and Pearson correlation. Features were extracted from demographics, gait kinematics, and gait adaptability datasets. ML models based on Support Vector Machine, Decision Tree, Multi-layer Perceptron, K-Nearest Neighbors, and AdaCost algorithm were trained to classify individuals with and without GAD. Model performance was evaluated using accuracy (ACC), sensitivity (SEN), F1-score and the area under the receiver operating characteristic (ROC) curve (AUC).ResultsThe stroke group showed a significantly decreased gait speed (<jats:italic>p</jats:italic> = 0.000) and step length (SL) (<jats:italic>p</jats:italic> = 0.000), while the asymmetry of SL (<jats:italic>p</jats:italic> = 0.000) and ST (<jats:italic>p</jats:italic> = 0.000) was higher compared to the healthy group. The gait adaptation tasks significantly decreased in slalom walking (<jats:italic>p</jats:italic> = 0.000), obstacle avoidance (<jats:italic>p</jats:italic> = 0.000), and speed adaptation (<jats:italic>p</jats:italic> = 0.000). Gait speed (<jats:italic>p</jats:italic> = 0.000) and obstacle avoidance (<jats:italic>p</jats:italic> = 0.000) were significantly correlated with global F-A score in stroke patients. The AdaCost demonstrated better classification performance with an ACC of 0.85, SEN of 0.80, F1-score of 0.77, and ROC-AUC of 0.75. Obstacle avoidance and gait speed were identified as critical features in this model.ConclusionStroke patients walk slower with shorter SL and more asymmetry of SL and ST. Their gait adaptability was decreased, particularly in obstacle avoidance and speed adaptation. The faster gait speed and better obstacle avoidance were correlated with better functional mobility. The AdaCost identifies individuals with GAD and facilitates clinical decision-making. This advances the future development of user-friendly interfaces and computer-aided diagnosis systems.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"78 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}