Search Results (271)

Functional near-infrared spectroscopy (fNIRS) offers a safe and portable signal source for brain–computer interface (BCI) applications, particularly in motor imagery (MI) decoding. However, its low sampling rate and hemodynamic delay pose challenges for temporal modeling and dynamic brain network analysis. To address these limitations in temporal dynamics, static graph modeling, and feature fusion interpretability, we propose TopoTempNet, an innovative topology-enhanced temporal network for biomedical signal decoding. TopoTempNet integrates multi-level graph features with temporal modeling through three key innovations: (1) multi-level topological feature construction using local and global functional connectivity metrics (e.g., connection strength, density, global efficiency); (2) a graph-modulated attention mechanism combining Transformer and Bi-LSTM to dynamically model key connections; and (3) a multimodal fusion strategy uniting raw signals, graph structures, and temporal representations into a high-dimensional discriminative space. Evaluated on three public fNIRS datasets (MA, WG, UFFT), TopoTempNet achieves superior accuracy (up to 90.04% ± 3.53%) and Kappa scores compared to state-of-the-art models. The ROC curves and t-SNE visualizations confirm its excellent feature discrimination and structural clarity. Furthermore, the statistical analysis of graph features reveals the model’s ability to capture task-specific functional connectivity patterns, enhancing the interpretability of decoding outcomes. TopoTempNet provides a novel pathway for building interpretable and high-performance BCI systems based on fNIRS. Full article

Induction motors are the dominant choice in industrial applications due to their robustness, structural simplicity, and high reliability. However, extended operation under extreme conditions, such as high temperatures, overload, and contamination, accelerates the degradation of internal components and increases the likelihood of faults. These faults are challenging to detect, as they typically develop gradually without clear external indicators. To address this issue, the present study proposes a cost-effective fault diagnosis system utilizing low-cost MEMS acoustic sensors in conjunction with a lightweight multilayer neural network (MNN). The same MNN architecture is employed to systematically compare three types of input feature representations: raw time-domain waveforms, FFT-based statistical features, and PCA-compressed FFT features. A total of 5040 samples were used to train, validate, and test the model for classifying three conditions: normal, rotor fault, and bearing fault. The time-domain approach achieved 90.6% accuracy, misclassifying 102 samples. In comparison, FFT-based statistical features yielded 99.8% accuracy with only two misclassifications. The FFT + PCA method produced similar performance while reducing dimensionality, making it more suitable for resource-constrained environments. These results demonstrate that acoustic-based fault diagnosis provides a practical and economical solution for industrial applications. Full article

Background: This study examined the influence of creative thinking, shaped by different forms of episodic mental representations, on human movement. The primary objective was to investigate how creativity, elicited through distinct cognitive stimuli, affects movement variability. Methods: Twenty-four professional dancers developed two original dance phrases, each inspired by either a visual or a narrative mental representation. Movement data were collected via inertial sensor technology and subsequently analysed to determine differences in motor expression. Results: The results indicated that movements performed under narrative representation conditions exhibited significantly increased risk-taking behaviour, greater movement amplitude, and a higher overall movement volume compared to those guided by visual stimuli. Conclusions: These findings underscore the role of creativity in modulating both the expressive and physical dimensions of human movement. Moreover, this research demonstrates the potential of inertial sensor technology not only to capture kinematic patterns but also to provide insight into the deeper layers of human artistic and cognitive processes. Full article

Metaphorical instructions are widely used in motor skill learning, yet their impact on learning and memory processes in children remains underexplored. This study examined whether metaphor-based language could enhance children’s acquisition and recall of body posture-related motor skills in an online learning environment. Forty-eight children aged 7 to 9 were randomly assigned to receive either metaphorical or explicit verbal instructions while learning 15 gymnastic postures demonstrated through static images. Following the learning phase, participants completed a free recall task, in which they reproduced the learned postures without cues, and a recognition task involving the identification of previously learned postures. Results indicated that children in the metaphor group recalled significantly more postures than those in the explicit group, with no reduction in movement quality. However, no group differences were observed in recognition accuracy or discrimination sensitivity. These findings suggest that metaphorical instructions may enhance children’s ability to retrieve self-generated motor representations but offer limited advantage when external cues are available. The study provides evidence for the value of metaphor-based strategies in supporting immediate motor memory in digital, child-focused learning settings and highlights the potential task-dependency of instructional language effects on memory outcomes. Full article

Standard Operating Procedures (SOPs) serve a critical role in complex systems operations, guiding operator response during normal and emergency scenarios. This study compares 29 SOPs (517 steps) across three domains with varying operator selection rigor: airline operations, Habitable Airlock (HAL) operations, and semi-autonomous vehicles. Using the extended Procedure Representation Language (e-PRL) framework, each step was decomposed into perceptual, cognitive, and motor components, enabling quantitative analysis of step types, memory demands, and training requirements. Monte Carlo simulations compared Time on Procedure against the Allowable Operational Time Window to predict failure rates. The analysis revealed three universal vulnerabilities: verification steps missing following waiting requirements (70% in airline operations, 58% in HAL operations, and 25% in autonomous vehicle procedures), ambiguous perceptual cues (15–48% of steps), and excessive memory demands (highest in HAL procedures at 71% average recall score). Procedure failure probabilities varied significantly (5.72% to 63.47% across domains), with autonomous vehicle procedures showing the greatest variability despite minimal operator selection. Counterintuitively, Habitable Airlock procedures requiring the most selective operators had the highest memory demands, suggesting that rigorous operator selection may compensate for procedure design deficiencies. These findings establish that procedure design approaches vary by domain based on assumptions about operator capabilities rather than universal human factors principles. Full article

Classical social cognitive conceptions suppose that the existence of common representations between agents constitutes the basis that represents the world from others’ perspectives. Alternatively, recent contributions support that the ability to distinguish self- from other’s representation would rather be at the origins of social inferences abilities. In the present study we compared the effects of two types of imitation training: mirror imitation (for which gesture could be represented in common referential) and anatomically congruent imitation (which requires not only a representation of the gesture of the model but also distinguishing between one’s own and others’ representations). We observed that a 4 min training of anatomically congruent imitation, but not of mirror imitation, improved performance on a visual perspective-taking test. This short training did not significantly impact self-reported measures of social cognitive skills. These results suggest that a unique transversal cognitive mechanism of co-representing and switching between self-related and other-related representations could be involved at both the motor and the mental-state levels. Opportunities for innovative social cognitive interventions at the motor level are discussed. Full article

Background/Objectives: The study aimed to investigate how cognitive variables (performance IQ, verbal short-term memory, working memory, and ADHD symptomatology) impact lexical representations in children with high-functioning autism spectrum disorder (HF-ASD). Methods: Participants were two groups (n1 = n2 = 20) of monolingual Greek-speaking children, aged 7 to 12 years, with and without HF-ASD matched in age, gender, and cognitive skills. Results: Overall, the HF-ASD group had more immature lexical representations than the control group, even though the two groups were similar in naming. In both groups, naming was correlated moderately with verbal short-term memory but only age predicted significantly semantic knowledge. In the ASD group, a bilateral predictive relationship was revealed between output motor programming skills and stored phonological knowledge, supporting theoretical assumptions of the psycholinguistic model of speech. Finally, a different pattern of interrelations was observed between cognitive and lexical variables in the two groups. Conclusions: The findings of the current study indicate that ASD children may map and process new vocabulary differently compared to typically developing peers. Full article

Rolling bearing failures in rotating machinery essential to energy systems (e.g., motors, generators, or turbines) can cause downtime, energy inefficiency, and safety hazards—especially under variable speed conditions common in traction drives. Traditional diagnosis methods struggle with nonstationary signals from speed variations. In response, there is a growing trend toward unsupervised and adaptive signal processing techniques, which offer better generalization in complex operating scenarios. This paper proposes an intelligent fault diagnosis framework combining Adaptive Chirp Mode Decomposition (ACMD)-based order tracking with a novel Shortest Paths Density Peak Search (SP-DPS) clustering algorithm. ACMD is chosen for its proven ability to extract instantaneous speed profiles from nonstationary signals, enabling angular domain resampling and quasi-stationary signal representation. SP-DPS enhances clustering robustness by incorporating global structure awareness into the analysis of statistical features in both the time and frequency domains. The method is validated using both a public bearing dataset and a custom-built metro traction motor test bench, representative of electric traction systems. The results show over 96% diagnostic accuracy under significant speed fluctuations, outperforming several state-of-the-art clustering approaches. This study presents a scalable and accurate unsupervised solution for bearing fault diagnosis, with strong potential to improve reliability, reduce maintenance costs, and prevent energy losses in critical energy conversion machinery. Full article

Background/Objectives: According to Embodied Cognition theories, motor skills in early childhood are closely interconnected with various cognitive abilities, including working memory, cognitive flexibility, and theory of mind. These processes are integral components of the multicomponent model of narrative comprehension, which posits that higher-order cognitive functions support the construction of coherent mental representations of narrative meaning. This study aimed to examine whether motor skills directly contribute to narrative comprehension in preschool children or whether this relationship is mediated by cognitive skills. Methods: Seventy-four typically developing children aged 3 to 6 years (47.2% female) participated in this study. Motor skills were assessed using standardized measures, and cognitive abilities were evaluated through tasks targeting working memory, cognitive flexibility, and theory of mind. Narrative comprehension was measured with age-appropriate tasks requiring the understanding and retelling of stories. A structural equation model (SEM) was conducted to test the direct and indirect effects of motor skills on narrative comprehension via cognitive skills. Results: The SEM results indicated a significant direct effect of motor skills on cognitive skills and an indirect effect on narrative comprehension mediated by cognitive abilities. No evidence was found for a direct pathway from motor skills to narrative comprehension independent of cognitive processes. Conclusions: These findings underscore the complex interplay between motor, cognitive, and language development in early childhood. The results suggest that motor skills contribute to narrative comprehension indirectly by enhancing core cognitive abilities, offering novel insights into the developmental mechanisms that support language acquisition and understanding. Full article

This study proposes a novel approach to input data configuration for the fault diagnosis of three-phase induction motors. Conventional neural network (CNN)-based diagnostic methods often employ three-phase current signals and apply various image transformation techniques, such as RGB mapping, wavelet transforms, and short-time Fourier transform (STFT), to construct multi-channel input data. While such approaches outperform 1D-CNNs or grayscale-based 2D-CNNs due to their rich informational content, they require multi-channel data and involve an increased computational complexity. Accordingly, this study transforms the three-phase currents into the D-Q synchronous reference frame and utilizes the D-axis current (Id) for image transformation. The Id is used to generate input data using the same image processing techniques, allowing for a direct performance comparison under identical CNN architectures. Experiments were conducted under consistent conditions using both three-phase-based and Id-based methods, each applied to RGB mapping, DWT, and STFT. The classification accuracy was evaluated using a ResNet50-based CNN. Results showed that the Id-STFT achieved the highest performance, with a validation accuracy of 99.6% and a test accuracy of 99.0%. While the RGB representation of three-phase signals has traditionally been favored for its information richness and diagnostic performance, this study demonstrates that a high-performance CNN-based fault diagnosis is achievable even with grayscale representations of a single current. Full article

Infants discriminate faces early in the first year, but research on infants’ discrimination of bodies is plagued by mixed findings. Using a familiarisation novelty preference method, we investigated 7- and 9-month-old infants’ discrimination of body postures presented in upright and inverted orientations, and with and without heads, along with relationships with gross and fine motor development. In our initial studies, 7-month-old infants discriminated upright headless postures with forward-facing and about-facing images. Eye tracking revealed that infants looked at the bodies of the upright headless postures the longest and at the heads of upright whole figures for 60–70% of the time regardless of the presence of faces, suggesting that heads detract attention from bodies. In a more stringent test, with similarly complex limb positions between test items, infants could not discriminate postures. With longer trials, the 7-month-olds demonstrated a familiarity preference for the upright whole figures, and the 9-month-olds demonstrated a novelty preference, albeit with a less robust effect. Unlike previous studies, we found that better gross motor skills were related to the 7-month-olds’ better discrimination of upright headless postures compared to inverted postures. The 9-month-old infants’ lower gross and fine motor skills were associated with a stronger preference for inverted compared to upright whole figures. This is further evidence of a configural representation of bodies in infancy, but it is constrained by an upper bias (heads in upright figures, feet in inverted), the test item similarity, and the trial duration. The measure and type of motor development reveals differential relationships with infants’ representations of bodies. Full article

To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. The main contributions are twofold: (1) A DCBA module is introduced to extract multi-scale features—including prominent, local, and average information—from grayscale images reconstructed from vibration signals across different domains; and (2) a two-stream network architecture is designed to learn complementary feature representations from time-domain and time–frequency-domain signals, which are fused through fully connected layers to improve diagnostic accuracy. Experimental results demonstrate that the proposed method achieves high recognition accuracy under various working speeds, loads, and road surfaces. Comparative studies with SENet, ECANet, CBAM, and single-stream 2DCNN models confirm its superior performance and robustness. The integration of DCBA with dual-domain feature learning effectively enhances fault feature extraction under complex operating conditions. Full article

The decoding of motor imagery (MI) electroencephalogram (EEG) signals is crucial for motor control and rehabilitation. However, as feature extraction is the core component of the decoding process, traditional methods, often limited to single-feature domains or shallow time-frequency fusion, struggle to comprehensively capture the spatio-temporal-frequency characteristics of the signals, thereby limiting decoding accuracy. To address these limitations, this paper proposes a dual-branch neural network architecture with multi-domain feature fusion, the dual-branch spatio-temporal-frequency fusion convolutional network with Transformer (DB-STFFCNet). The DB-STFFCNet model consists of three modules: the spatiotemporal feature extraction module (STFE), the frequency feature extraction module (FFE), and the feature fusion and classification module. The STFE module employs a lightweight multi-dimensional attention network combined with a temporal Transformer encoder, capable of simultaneously modeling local fine-grained features and global spatiotemporal dependencies, effectively integrating spatiotemporal information and enhancing feature representation. The FFE module constructs a hierarchical feature refinement structure by leveraging the fast Fourier transform (FFT) and multi-scale frequency convolutions, while a frequency-domain Transformer encoder captures the global dependencies among frequency domain features, thus improving the model’s ability to represent key frequency information. Finally, the fusion module effectively consolidates the spatiotemporal and frequency features to achieve accurate classification. To evaluate the feasibility of the proposed method, experiments were conducted on the BCI Competition IV-2a and IV-2b public datasets, achieving accuracies of 83.13% and 89.54%, respectively, outperforming existing methods. This study provides a novel solution for joint time-frequency representation learning in EEG analysis. Full article

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) have promising applications in neurorehabilitation for individuals who have lost mobility and control over parts of their body due to brain injuries, such as stroke patients. Accurately classifying MI tasks is essential for effective BCI performance, but this task remains challenging due to the complex and non-stationary nature of EEG signals. This study aims to improve the classification of left and right-hand MI tasks by utilizing high-resolution time-frequency features extracted from EEG signals, enhanced with deep learning-based data augmentation techniques. We propose a novel deep learning framework named the Generalized Wavelet Transform-based Deep Convolutional Network (GDC-Net), which integrates multiple components. First, EEG signals recorded from the C3, C4, and Cz channels are transformed into detailed time-frequency representations using the Generalized Morse Wavelet Transform (GMWT). The selected features are then expanded using a Deep Convolutional Generative Adversarial Network (DCGAN) to generate additional synthetic data and address data scarcity. Finally, the augmented feature maps data are subsequently fed into a hybrid CNN-LSTM architecture, enabling both spatial and temporal feature learning for improved classification. The proposed approach is evaluated on the BCI Competition IV dataset 2b. Experimental results showed that the mean classification accuracy and Kappa value are 89.24% and 0.784, respectively, making them the highest compared to the state-of-the-art algorithms. The integration of GMWT and DCGAN significantly enhances feature quality and model generalization, thereby improving classification performance. These findings demonstrate that GDC-Net delivers superior MI classification performance by effectively capturing high-resolution time-frequency dynamics and enhancing data diversity. This approach holds strong potential for advancing MI-based BCI applications, especially in assistive and rehabilitation technologies. Full article

This paper proposes a disturbance observer (DOB)-based robust force control framework for tendon-sheath mechanisms (TSMs) that transmit tension forces from the proximal to the distal end. A detailed physical model of the TSM system, where a motor actuates the tendon and the output corresponds to the contact force at the robot end-effector, is developed. However, the resulting nominal model adopts a simplified representation of friction and involves significant parametric uncertainties due to the inherently complex dynamics of the tendon-sheath structure. By rigorously verifying the well-established robust stability conditions associated with DOB-based control frameworks, it is confirmed that the tendon-sheath transmission system satisfies all required assumptions and stability criteria. Furthermore, the necessary additional conditions can be readily met by appropriately designing the Q-filter, which is comparatively straightforward in practice. This validation supports the theoretical soundness and practical suitability of employing a DOB to effectively estimate and compensate for the system’s inherent parametric uncertainties and external disturbances. Numerical simulations incorporating a discrete-segment tendon model with an advanced friction dynamics formulation demonstrate significant improvements in force tracking accuracy at the tendon’s distal end compared to conventional control schemes without DOB compensation. The results highlight the robustness and effectiveness of the proposed control scheme for tendon-driven robotic systems. Full article