Search Results (72)

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article

Electroencephalography (EEG) is a powerful tool for investigating gustatory perception, offering high temporal resolution and non-invasive brain activity recording. This review highlights the ability of EEG to reveal the complex interactions between sensory input, emotional responses, and cognitive evaluation in the process of taste perception. This review examines the physiological basis of taste, focusing on key brain regions and how environmental and psychological factors influence taste perception. It also discusses the methods and applications of EEG technology, including its principles, signal features, and measurement methods. Notably, EEG markers like event-related potentials (ERPs), frequency band power analysis, and brain network connectivity are essential for understanding the neural dynamics of taste processing. This review concludes with potential future research directions, including the integration of EEG with other neuroimaging techniques, cross-cultural studies on gustatory perception, and the use of EEG biomarkers in early neurological disease diagnosis. Full article

This study investigated the impact of thawing methods on the roasting quality and flavor of reduced-salt marinated large yellow croaker to optimize processing protocols for frozen products. Three thawing methods, low-temperature thawing (LTT), room-temperature thawing (RTT), and flowing-water thawing (FWT), were systematically evaluated. Freshly marinated (FM) and non-thawed (WT) samples served as controls. Key parameters, including thawing efficiency, physicochemical properties, texture, color, sensory attributes, and volatile organic compounds (VOCs), were analyzed. The results showed that FWT achieved the fastest thawing (14.67 min), significantly outperforming RTT (32.57 min) and LTT (591 min) (p < 0.05). Moisture content and springiness remained stable across treatments (p > 0.05). For color parameters, lightness (L*), yellowness (b*), and browning index (BI) showed no significant variations (p > 0.05), while the total color difference (ΔE) was significantly affected by thawing methods (p < 0.05). FWT exhibited the lowest salt retention (3.49 g/100 g), a 18.8% reduction compared to WT (4.30 g/100 g). Texture analysis revealed that FWT samples maintained optimal hardness and chewiness, with sensory scores second only to WT. Volatile profiling identified distinct “thermal–oxygen–temporal” effects, referring to the respective influences of heating conditions, oxidative environments, and processing time on flavor compound formation. RTT and WT treatments significantly increased the relative 1-propanethiol and 5-methyl-2-furanmethanol (>10% increase) contents, respectively, and markedly reduced the 2-butanol levels (<0.3%) due to volatilization losses. GC-IMS and electronic nose analysis established a robust correlation network among three major VOC clusters (aldehydes/alcohols, esters/acid/sulfides, and ketones), with sensory scores showing strong positive correlations with the alkane- and aromatic-sensitive sensors (W5C/W1C) of the electronic nose (r > 0.90) and negative correlations with other sensors (r < −0.70). These findings demonstrate that FWT offers the best balance of efficiency, salt reduction, and sensory quality, making it a superior method for reduced-salt marinated large yellow croaker industrial applications. Full article

Introduction: Speech perception relies on integrating auditory and visual information, shaped by both perceptual and cognitive factors. Musical training has been shown to affect multisensory processing, whereas cognitive processes, such as recalibration derived from a perceptual history, influence neural responses to upcoming sensory inputs. To investigate these influences, we evaluated cortical activity associated with the McGurk illusion focusing specifically on how musical training and perceptual history affect multisensory speech perception. Methods: Musicians and age-matched nonmusicians participated in electroencephalogram experiments using a McGurk task. We analyzed five conditions on the basis of stimulus type and participants’ responses and quantified the rate of illusory percepts and cortical alpha power between groups using dynamic imaging of coherent sources. Results: No differences in McGurk susceptibility were detected between musicians and nonmusicians. Source-localized alpha, however, revealed group-specific patterns: musical training was associated with frontal alpha modulation during integration, a finding consistent with enhanced top-down control, whereas nonmusicians relied more on sensory-driven processing. Additionally, illusory responses occurred in auditory-only trials. Follow-up analyses revealed no significant alpha modulation clusters in musicians, but temporal alpha modulations in nonmusicians depending on preceding audiovisual congruency. Conclusions: These findings suggest that musical training may influence the neural mechanisms of audiovisual integration during speech perception. Specifically, musicians appear to employ enhanced top-down control involving frontal regions, whereas nonmusicians rely more on sensory-driven processing mediated by parietal and temporal regions. Furthermore, perceptual recalibration may be more prominent in nonmusicians, whereas musicians appear to focus more on current sensory input, reducing their reliance on perceptual history. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

The temporal dominance of sensations (TDS) and temporal liking (TL) methods offer complementary insights into the evolution of sensory and hedonic responses during food consumption. This study investigates the feasibility of predicting TL curves for food pairings from their TDS profiles using reservoir computing, a type of recurrent neural network. Participants evaluated eight samples—two crackers (plain, sesame), two spreads (peanut butter, strawberry jam), and their four binary combinations—performing both TDS and TL evaluations. This process yielded paired time-series data of TDS and TL curves. We trained various reservoir models under different conditions, including varying reservoir sizes (64, 128, 192, or 256 neurons) and the inclusion of auxiliary input dimensions, such as flags indicating the types of foods tasted. Our results show that models with minimal auxiliary inputs achieved the lowest root mean squared errors (RMSEs), with the best performance being an RMSE of 0.44 points on a 9-point liking scale between the observed and predicted TL curves. The ability to predict TL curves for food pairings holds some promise for reducing the need for extensive sensory evaluation, especially when a large number of food combinations are targeted. Full article

The reuse of by-products from plant processing as feed for animals aligns with the principles of a circular economy. Feeding dairy ruminants agro-industrial by-products often alters the chemical composition and sensory characteristics of dairy items. A dual approach—classic with neuro-sensory techniques—was utilized to evaluate the effect of prickly pear by-products on the diets of dairy ewes. Fresh Pecorino cheeses made from the milk of two groups of sheep fed with and without prickly pear by-product silage were analyzed for chemical composition and volatile organic compounds (VOCs). Furthermore, a neurosensory approach with consumers was used, combining electroencephalography (EEG) and temporal dominance of sensations techniques (TDS). Prickly pear silage in sheep diets did not alter the chemical composition of fresh cheese, but it did modify its fatty acids, with a significant increase in SFA (+2.60%) and PUFA (+0.33%), with a better n-6/n-3 ratio (−0.35%) due to higher omega-3 fatty acid content (+0.23%). The identification of VOCs revealed an increase in caproic acid (+27.27%) and n-caprylic acid (+6.47%) and a greater presence of sweet notes in the prickly pear-based cheeses, which exhibited a different aromatic complexity compared with the control cheeses. Even with a neuromarketing approach, sweetness remained the predominant sensation. Full article

The objective of this research was to apply static and dynamic sensometric techniques to determine the impact of processing factors (dehydration time, frying exposure time) and storage duration on the sensory and cognitive characteristics, as well as consumer preference, of chayote chips. A total of 18 types of chips were prepared (using a combination of three frying temperatures [140, 150, 160 °C], two exposure times [5 and 10 s], and three periods of storage [0, 30, and 60 days]). A panel of 100 consumers was formed to evaluate sensory and cognitive attributes (emotions and memories) as well as overall liking, using static techniques such as Rate-All-That-Apply (RATA), Check-All-That-Apply (CATA), and a hedonic scale. Finally, the temporal dominance of sensations (TDS) dynamic technique was used to study the behavior of chips with higher levels of preference. The results of the sensory techniques indicated that the storage day factor influenced the sensory results. The samples prepared on the same day were perceived with high intensities of typical attributes of this type of food (bitter-BT, Fried-A, Sweet-A, Potato-A, Toasted-A, Chayote-A, Potato-F, Crunchy, Chayote-F, and Sweet-BT) while evoking positive emotions and memories in consumers (active, enthusiastic, free, good, good nature, happy, interested, satisfied, traditional food, family, summer, party, and mild weather). In terms of preference, consumers selected the chip samples with 0 days of storage. The TDS curves determined that the dominant attributes of the chayote chips with 0 days of storage were chayote flavor, sweet, and fried (with a dominance t = 5–20 s). Regarding the cognitive aspect, these chayote chips evoke positive dominant emotions (good, satisfied, and happy from t = 8–20 s) as well as dominant positive memories of childhood (t = 9–20 s), traditional food (t = 11–20 s), and friendship (t = 11–20 s). Full article

This study investigates the integration of biologically inspired noise injection with a time-distributed adaptation of the AlexNet architecture to enhance the performance and robustness of human activity recognition (HAR) systems. It is a critical field in computer vision which involves identifying and interpreting human actions from video sequences and has applications in healthcare, security and smart environments. The proposed model is based on an adaptation of AlexNet, originally developed for static image classification and not inherently suited for modelling temporal sequences for video action classification tasks. While our time-distributed AlexNet efficiently captures spatial and temporal features and suitable for video classification. However, its performance can be limited by overfitting and poor generalisation to unseen scenarios, to address these challenges, Gaussian noise was introduced at the input level during training, inspired by neural mechanisms observed in biological sensory processing to handle variability and uncertainty. Experiments were conducted on the EduNet, UCF50 and UCF101 datasets. The EduNet dataset was specifically designed for educational environments and we evaluate the impact of noise injection on model accuracy, stability and overall performance. The proposed bio-inspired noise-injected time-distributed AlexNet achieved an overall accuracy of 91.40% and an F1 score of 92.77%, outperforming other state-of-the-art models. Hyperparameter tuning, particularly optimising the learning rate, further enhanced model stability, reflected in lower standard deviation values across multiple experimental runs. These findings demonstrate that the strategic combination of noise injection with time-distributed architectures improves generalisation and robustness in HAR, paving the way for resource-efficient and real-world-deployable deep learning systems. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

This study aimed to clarify the sensory characteristics of Koshu wine, which is the most popular white wine produced in Japan, by identifying descriptive terms for sour taste, a primary aspect of wine flavor. A sensory evaluation generated 56 terms related to sour taste quality. Some terms were categorized on the basis of the timing of perception— immediately after sipping, holding in the mouth, and after swallowing—while others were classified as expressing “temporal change”, “overall impression terms”, or “metaphorical terms”. From these, 12 terms—“fresh”, “stand out”, “sharp”, “soft”, “round”, “gentle”, “bright”, “duration”, “crisp”, “intensity”, “mild”, and “calm”—were selected, with definitions and reference standards (materials or examples that represent each characteristic) established. A trained sensory panel evaluated 16 Koshu wines, revealing significant differences in all sour taste quality terms except “duration”. The evaluation of “duration” may require improvement. Correlation analysis indicated that pH was strongly associated with “sharp” sour taste immediately after sipping, while titratable acidity and pH correlated with “round” and “gentle” sour taste when the wine was held in the mouth. Total acidity was linked to the duration of sour taste. Applying the sour taste quality terms determined from this study will enable the quantification of the sour taste quality of wines. Full article

Seaweed is a sustainable ingredient that has been suggested to improve the nutritional aspects as well as the sensory properties of different food products. The objective of this study was to evaluate the flavor properties of extracts from brown seaweed (Ascophyllum nodosum) and sugar kelp (Saccharina latissimi) obtained at different temperatures. These varieties commonly grow in the Atlantic Ocean. The seaweed samples were extracted using water at three different temperatures (50 °C, 70 °C, and 90 °C). The volatile fraction of the extracts was extracted with headspace solid-phase microextraction and analyzed by gas chromatography–mass spectrometry. The headspace chemical composition varies significantly among seaweed extracts and at different extraction temperatures. Major classes of identified compounds were aldehydes, ketones, alcohols, hydrocarbons, and halogenated compounds. Extracts were also evaluated using temporal check-all-that-apply (with 84 untrained participants). The different temperatures had minimal impact on the flavour properties of the brown seaweed samples, but the extraction temperature did influence the properties of the sugar kelp samples. Increasing the extraction temperature seemed to lead to an increase in bitterness, savouriness, and earthy flavor, but future studies are needed to confirm this finding. This study continues the exploration of the flavor properties of seaweeds and identifies the dynamic flavor profile of brown seaweed and sugar kelp under different extraction conditions. Full article

Sweetness is a key dimension of sensory experience in food, and variations in the type and concentration of sweeteners can elicit distinct brain responses. In this study, electroencephalography (EEG) was employed to systematically evaluate neural activity elicited by different concentrations of sucrose solutions (1%, 3%, 5%, and 7%) and by non-nutritive sweeteners matched in perceived sweetness to a 7% sucrose solution (10% erythritol, 0.0133% sucralose, and 0.0368% stevioside). The results revealed that an increased sucrose concentration was associated with progressively weaker EEG signal intensity, suggesting that the brain can effectively distinguish sweetness intensity. Under iso-sweet conditions, different types of sweeteners induced significantly distinct EEG patterns, indicating that the nature of the sweetener modulates flavor perception at the neural level. Further analysis showed increases in both δ- and α-band power following sweet taste stimulation, with prominent activations observed in the frontal, parietal, and right temporal regions. These findings demonstrate the utility of EEG in detecting subtle differences in brain responses to sweeteners, offering new insights into the neural mechanisms underlying sweet taste perception. Full article

The perceived quality of extra virgin olive oil (EVOO) arises from the multisensory integration of multimodal stimuli, primarily driven by non-volatile and volatile organic compounds (VOCs). Given that EVOO is frequently consumed in combination with other foods, cross-modal interactions, encompassing both internal and external elements, play a crucial role in shaping its sensory perception. A more realistic representation of EVOO perception can be achieved by considering these cross-modal effects and their temporal dynamics. This study employed dynamic sensory and instrumental techniques to investigate the product-related mechanisms that influence EVOO flavor perception. Ten trained panelists (mean age = 41.5 years; 50% female) evaluated two EVOO samples under two consumption conditions: alone and accompanied by a solid carrier (bread or chickpeas). Temporal Check-All-That-Apply (TCATA) and nose-space analysis using Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS) were conducted simultaneously. Sensory descriptors and mass spectral peaks were analyzed through temporal curve indices (Area Under the Curve, Maximum Citation/Concentration, Time to Maximum), which were then used to construct multi-dimensional sensory and VOC release maps. Findings revealed that the composition and texture of the food carriers had a greater influence on temporal flavor perception than the variability in VOCs released by the different EVOO samples. These results underscore the importance of considering cross-modal sensory interactions when predicting EVOO flavor perception. The carriers modulated both the perception and VOC release, with effects dependent on their specific composition and texture. This methodological approach enabled a deeper understanding of the dynamic relationship between VOC release and EVOO sensory experience. Full article

This study presents a novel investigation into immersive teleoperation systems using collaborative, device-agnostic interfaces for advancing smart haptics in industrial assistive applications. The research focuses on evaluating the quality of experience (QoE) of users interacting with a teleoperation system comprising a local robotic arm, a robot gripper, and heterogeneous remote tracking and haptic feedback devices. By employing a modular device-agnostic framework, the system supports flexible configurations, including one-user-one-equipment (1U-1E), one-user-multiple-equipment (1U-ME), and multiple-users-multiple-equipment (MU-ME) scenarios. The experimental set-up involves participants manipulating predefined objects and placing them into designated baskets by following specified 3D trajectories. Performance is measured using objective QoE metrics, including temporal efficiency (time required to complete the task) and spatial accuracy (trajectory similarity to the predefined path). In addition, subjective QoE metrics are assessed through detailed surveys, capturing user perceptions of presence, engagement, control, sensory integration, and cognitive load. To ensure flexibility and scalability, the system integrates various haptic configurations, including (1) a Touch kinaesthetic device for precision tracking and grounded haptic feedback, (2) a DualSense tactile joystick as both a tracker and mobile haptic device, (3) a bHaptics DK2 vibrotactile glove with a camera tracker, and (4) a SenseGlove Nova force-feedback glove with VIVE trackers. The modular approach enables comparative analysis of how different device configurations influence user performance and experience. The results indicate that the objective QoE metrics varied significantly across device configurations, with the Touch and SenseGlove Nova set-ups providing the highest trajectory similarity and temporal efficiency. Subjective assessments revealed a strong correlation between presence and sensory integration, with users reporting higher engagement and control in scenarios utilizing force feedback mechanisms. Cognitive load varied across the set-ups, with more complex configurations (e.g., 1U-ME) requiring longer adaptation periods. This study contributes to the field by demonstrating the feasibility of a device-agnostic teleoperation framework for immersive industrial applications. It underscores the critical interplay between objective task performance and subjective user experience, providing actionable insights into the design of next-generation teleoperation systems. Full article