Tactile stimulation designs adapted to clinical settings result in reliable fMRI-based somatosensory digit maps.

Movement constraints in stroke survivors are often accompanied by additional impairments in related somatosensory perception. A complex interplay between the primary somatosensory and motor cortices is essential for adequate and precise movements. This necessitates investigating the role of the primary somatosensory cortex in movement deficits of stroke survivors. The first step towards this goal could be a fast and reliable functional Magnetic Resonance Imaging (fMRI)-based mapping of the somatosensory cortex applicable for clinical settings. Here, we compare two 3 T fMRI-based somatosensory digit mapping techniques adapted for clinical usage in seven neurotypical volunteers and two sessions, to assess their validity and retest-reliability. Both, the traveling wave and the blocked design approach resulted in complete digit maps in both sessions of all participants, showing the expected layout. Similarly, no evidence for differences in the volume of activation, nor the activation overlap between neighboring activations could be detected, indicating the general feasibility of the clinical adaptation and their validity. Retest-reliability, indicated by the Dice coefficient, exhibited reasonable values for the spatial correspondence of single digit activations across sessions, but low values for the spatial correspondence of the area of overlap between neighboring digits across sessions. Parameters describing the location of the single digit activations exhibited very high correlations across sessions, while activation volume and overlap only exhibited medium to low correlations. The feasibility and high retest-reliabilities for the parameters describing the location of the single digit activations are promising concerning the implementation into a clinical context to supplement diagnosis and treatment stratification in upper limb stroke patients.

Simultaneous Estimation of Digit Tip Forces and Hand Postures in a Simulated Real-Life Condition With High-Density Electromyography and Deep Learning.

In myoelectric control, continuous estimation of multiple degrees of freedom has an important role. Most studies have focused on estimating discrete postures or forces of the human hand but for a practical prosthetic system, both should be considered. In daily life activities, hand postures vary for grasping different objects and the amount of force exerted on each fingertip depends on the shape and weight of the object. This study aims to investigate the feasibility of continuous estimation of multiple degrees of freedom. We proposed a reach and grasp framework to study both absolute fingertip forces and hand movement types using deep learning techniques applied to high-density surface electromyography (HD-sEMG). Four daily life grasp types were examined and absolute fingertip forces were simultaneously estimated while grasping various objects, along with the grasp types. We showed that combining a 3-dimensional Convolutional Neural Network (3DCNN) with a Long Short-term Memory (LSTM) can reliably and continuously estimate the digit tip forces and classify different hand postures in human individuals. The mean absolute error (MAE) and Pearson correlation coefficient (PCC) results of the force estimation problem across all fingers and subjects were 0.46 ± 0.23 and 0.90 ± 0.03% respectively and for the classification problem, they were 0.04 ± 0.01 and 0.97 ± 0.02%. The results demonstrated that both absolute digit tip forces and hand postures can be successfully estimated through deep learning and HD-sEMG.

Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition.

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed 'VF-DCN'), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of 0.17%, 0.19%, 2.11%, and 0.65%, respectively, and Accuracy Rates (ACC) of 100%, 99.97%, 98.92%, and 99.36%, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases.

Bioinspired adaptable multiplanar mechano-vibrotactile haptic system.

Several gaps persist in haptic device development due to the multifaceted nature of the sense of touch. Existing gaps include challenges enhancing touch feedback fidelity, providing diverse haptic sensations, and ensuring wearability for delivering tactile stimuli to the fingertips. Here, we introduce the Bioinspired Adaptable Multiplanar Haptic system, offering mechanotactile/steady and vibrotactile pulse stimuli with adjustable intensity (up to 298.1 mN) and frequencies (up to 130 Hz). This system can deliver simultaneous stimuli across multiple fingertip areas. The paper includes a full characterisation of our system. As the device can play an important role in further understanding human touch, we performed human stimuli sensitivity and differentiation experiments to evaluate the capability of delivering mechano-vibrotactile, variable intensity, simultaneous, multiplanar and operator agnostic stimuli. Our system promises to accelerate the development of touch perception devices, providing painless, operator-independent data crucial for researching and diagnosing touch-related disorders.

Brain Encoding of Naturalistic, Continuous, and Unpredictable Tactile Events.

Studies employing EEG to measure somatosensory responses have been typically optimized to compute event-related potentials in response to discrete events. However, tactile interactions involve continuous processing of nonstationary inputs that change in location, duration, and intensity. To fill this gap, this study aims to demonstrate the possibility of measuring the neural tracking of continuous and unpredictable tactile information. Twenty-seven young adults (females, 15) were continuously and passively stimulated with a random series of gentle brushes on single fingers of each hand, which were covered from view. Thus, tactile stimulations were unique for each participant and stimulated fingers. An encoding model measured the degree of synchronization between brain activity and continuous tactile input, generating a temporal response function (TRF). Brain topographies associated with the encoding of each finger stimulation showed a contralateral response at central sensors starting at 50 ms and peaking at ∼140 ms of lag, followed by a bilateral response at ∼240 ms. A series of analyses highlighted that reliable tactile TRF emerged after just 3 min of stimulation. Strikingly, topographical patterns of the TRF allowed discriminating digit lateralization across hands and digit representation within each hand. Our results demonstrated for the first time the possibility of using EEG to measure the neural tracking of a naturalistic, continuous, and unpredictable stimulation in the somatosensory domain. Crucially, this approach allows the study of brain activity following individualized, idiosyncratic tactile events to the fingers.

Inter-Digit Low Level Force Coordination in a Complex Isometric Pinch Tracking Task.

This study examined whether target pursuit tracking by a performer-controlled computer cursor around a square diamond-shaped circuit, using isometric pinch grip force production, would show a significant difference in performance metrics dependent on the clockwise sense of the target movement along the trajectory path. The target template incorporated path segments requiring all four possible combinations of directional force modulation patterns (increasing and decreasing isometric pinch forces of the thumb and index finger). Overall, it was found that cursor positional accuracy was greater during counterclockwise pursuit, that steadiness was greater during clockwise pursuit, and that the cursor bearing angle with respect to target movement was biased toward cursor positioning being within the interior of the trajectory circuit regardless of clockwise sense.

Artificial embodiment displaces cortical neuromagnetic somatosensory responses.

Integrating artificial limbs as part of one's body involves complex neuroplastic changes resulting from various sensory inputs. While somatosensory feedback is crucial, plastic processes that enable embodiment remain unknown. We investigated this using somatosensory evoked fields (SEFs) in the primary somatosensory cortex (S1) following the Rubber Hand Illusion (RHI), known to quickly induce artificial limb embodiment. During electrical stimulation of the little finger and thumb, 19 adults underwent neuromagnetic recordings before and after the RHI. We found early SEF displacement, including an illusion-brain correlation between extent of embodiment and specific changes to the first cortical response at 20 ms in Area 3b, within S1. Furthermore, we observed a posteriorly directed displacement at 35 ms towards Area 1, known to be important for visual integration during touch perception. That this second displacement was unrelated to extent of embodiment implies a functional distinction between neuroplastic changes of these components and areas. The earlier shift in Area 3b may shape extent of limb ownership, while subsequent displacement into Area 1 may relate to early visual-tactile integration that initiates embodiment. Here we provide evidence for multiple neuroplastic processes in S1-lasting beyond the illusion-supporting integration of artificial limbs like prostheses within the body representation.

Validity and reliability of a finger training tool for assessing metacarpal phalangeal joint ranges of motion in asymptomatic participants.

This pilot study aims to evaluate concurrent validity using the goniometer as a reference tool and test-retest reliability of flexion of metacarpal phalangeal joint (MCP) measurements taken from a finger training device (air-guitar system) in healthy participants. There were ten self -reported asymptomatic participants recruited to test the devices. The measurements of all metacarpophangeal joints of the dominant hands were conducted using a finger goniometer and the air-guitar system. Two measuring sessions were conducted on the same day. The concurrent validity of the air-guitar indicated by strong concordance correlation coefficient (0.62-0.90) with the goniometer and mean difference (approximately 1°) between the two instruments are well below the limit of 5°. The test-retest reliability of MCP measurements from the air-guitar glove (0.82-0.99) was acceptable as a clinically meaningful measurement tool as the intraclass correlation coefficients were higher than 0.7. The standard error of measurement and minimal detectable change of the air-guitar are similar to those of the goniometer. The air-guitar tracking features, when used as a home-based therapy tool, may assist in monitoring change of MCP flexion over a time course with good reliability and strongly associated with the measurements from the goniometer.

Towards higher load capacity: innovative design of a robotic hand with soft jointed structure.

In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor coordinated drive mechanism, a thumb coupling transmission mechanism and a thumb steering mechanism are proposed. These innovative designs allow for more effective actuation in each finger, enhancing the load capacity of the robotic hand while maintaining key performance indicators such as dexterity and adaptability. A mechanical model of the robotic finger was made to determine the application limitations and load capacity. The robotic hand was then prototyped for a set of experiments. The experimental results showed that the proposed theoretical model were reliable. Also, the fingertip force of the robotic finger could reach up to 10.3 N, and the load force could reach up to 72.8 N. When grasping target objects of different sizes and shapes, the robotic hand was able to perform the various power grasping and precision grasping in the Cutkosky taxonomy. Moreover, the robotic hand had good flexibility and adaptability by means of adjusting the envelope state autonomously.

Human movement strategies in uncertain environments: A synergy-based approach to the stability-agility tradeoff.

Humans frequently prepare for agile movements by decreasing stability. This facilitates transitions between movements but increases vulnerability to external disruptions. Therefore, humans might weigh the risk of disruption against the gain in agility and scale their stability to the likelihood of having to perform an agility-demanding action. We used the theory of motor synergies to investigate how humans manage this stability-agility tradeoff under uncertainty. This theory has long quantified stability using the synergy index, and reduction in stability before movement transitions using anticipatory synergy adjustment (ASA). However, the impact of uncertainty - whether a quick action should be executed or inhibited - on ASA is unknown. Furthermore, the impact of ASA on execution and inhibition of the action is unclear. We combined multi-finger, isometric force production with the go/no-go paradigm. Thirty participants performed constant force (no-go task), rapid force pulse (go task), and randomized go and no-go trials (go/no-go task) in response to visual cues. We measured the pre-cue finger forces and computed ASA using the uncontrolled manifold method and quantified the spatio-temporal features of the force after the visual cue. We expected ASA in both go/no-go and go tasks, but larger ASA for the latter. Surprisingly, we observed ASA only for the go task. For the go/no-go task, 53% of participants increased stability before the cue. The high stability hindered performance, leading to increased errors in no-go trials and lower peak forces in go trials. These results align with the stability-agility tradeoff. It is puzzling why some participants increased stability even though 80% of the trials demanded agility. This study indicates that individual differences in the effect of task uncertainty and motor inhibition on ASA is unexplored in motor synergy theory and presents a method for further development.

Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.

BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing machine learning models requiring manual feature extraction. NEW METHOD: This study introduces a 3D convolutional neural network (3D-CNN) model to decode finger movements using ECoG data. The model employs adaptive, explainable AI (xAI) techniques to interpret the physiological relevance of brain signals. ECoG signals from epilepsy patients during awake craniotomy were processed to extract power spectral density across multiple frequency bands. These data formed a 3D matrix used to train the 3D-CNN to predict finger trajectories. RESULTS: The 3D-CNN model showed significant accuracy in predicting finger movements, with root-mean-square error (RMSE) values of 0.26-0.38 for single finger movements and 0.20-0.24 for combined movements. Explainable AI techniques, Grad-CAM and SHAP, identified the high gamma (HG) band as crucial for movement prediction, showing specific cortical regions involved in different finger movements. These findings highlighted the physiological significance of the HG band in motor control. COMPARISON WITH EXISTING METHODS: The 3D-CNN model outperformed traditional machine learning approaches by effectively capturing spatial and temporal patterns in ECoG data. The use of xAI techniques provided clearer insights into the model's decision-making process, unlike the "black box" nature of standard deep learning models. CONCLUSIONS: The proposed 3D-CNN model, combined with xAI methods, enhances the decoding accuracy of finger movements from ECoG data. This approach offers a more efficient and interpretable solution for brain-computer interface (BCI) applications, emphasizing the HG band's role in motor control.

Zero-Biased Bionic Fingertip E-Skin with Multimodal Tactile Perception and Artificial Intelligence for Augmented Touch Awareness.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600 µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80 °C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Menstrual Cycle Variations in Wearable-Detected Finger Temperature and Heart Rate, But Not in Sleep Metrics, in Young and Midlife Individuals.

Most studies about the menstrual cycle are laboratory-based, in small samples, with infrequent sampling, and limited to young individuals. Here, we use wearable and diary-based data to investigate menstrual phase and age effects on finger temperature, sleep, heart rate (HR), physical activity, physical symptoms, and mood. A total of 116 healthy females, without menstrual disorders, were enrolled: 67 young (18-35 years, reproductive stage) and 53 midlife (42-55 years, late reproductive to menopause transition). Over one menstrual cycle, participants wore Oura ring Gen2 to detect finger temperature, HR, heart rate variability (root mean square of successive differences between normal heartbeats [RMSSD]), steps, and sleep. They used luteinizing hormone (LH) kits and daily rated sleep, mood, and physical symptoms. A cosinor rhythm analysis was applied to detect menstrual oscillations in temperature. The effect of menstrual cycle phase and group on all other variables was assessed using hierarchical linear models. Finger temperature followed an oscillatory trend indicative of ovulatory cycles in 96 participants. In the midlife group, the temperature rhythm's mesor was higher, but period, amplitude, and number of days between menses and acrophase were similar in both groups. In those with oscillatory temperatures, HR was lowest during menses in both groups. In the young group only, RMSSD was lower in the late-luteal phase than during menses. Overall, RMSSD was lower, and number of daily steps was higher, in the midlife group. No significant menstrual cycle changes were detected in wearable-derived or self-reported measures of sleep efficiency, duration, wake-after-sleep onset, sleep onset latency, or sleep quality. Mood positivity was higher around ovulation, and physical symptoms manifested during menses. Temperature and HR changed across the menstrual cycle; however, sleep measures remained stable in these healthy young and midlife individuals. Further work should investigate over longer periods whether individual- or cluster-specific sleep changes exist, and if a buffering mechanism protects sleep from physiological changes across the menstrual cycle.

Exploring the bias: how skin color influences oxygen saturation readings via Monte Carlo simulations.

Significance: Our goal is to understand the root cause of reported oxygen saturation ( SpO 2 ) overestimation in heavily pigmented skin types to devise solutions toward enabling equity in pulse oximeter designs. Aim: We aim to gain theoretical insights into the effect of skin tone on SpO 2 - R curves using a three-dimensional, four-layer tissue model representing a finger. Approach: A finger tissue model, comprising the epidermis, dermis, two arteries, and a bone, was developed using a Monte Carlo-based approach in the MCmatlab software. Two skin tones-light and dark-were simulated by adjusting the absorption and scattering properties within the epidermal layer. Following this, SpO 2 - R curves were generated in various tissue configurations, including transmission and reflection modes using red and infrared wavelengths. In addition, the influence of source-detector (SD) separation distances on both light and dark skin tissue models was studied. Results: In transmission mode, SpO 2 - R curves did not deviate with changes in skin tones because both pulsatile and non-pulsatile terms experienced equal attenuation at red and infrared wavelengths. However, in reflection mode, measurable variations in SpO 2 - R curves were evident. This was due to differential attenuation of the red components, which resulted in a lower perfusion index at the red wavelength in darker skin. As the SD separation increased, the effect of skin tone on SpO 2 - R curves in reflection mode became less pronounced, with the largest SD separation exhibiting effects similar to those observed in transmission mode. Conclusions: Monte Carlo simulations have demonstrated that different light pathlengths within the tissue contribute to the overestimation of SpO 2 in people with darker skin in reflection mode pulse oximetry. Increasing the SD separation may mitigate the effect of skin tone on SpO 2 readings. These trends were not observed in transmission mode; however, further planned research using more complex models of the tissue is essential.

Monte Carlo simulation of the effect of melanin concentration on light-tissue interactions for transmittance pulse oximetry measurement.

Significance: Questions about the accuracy of pulse oximeters in measuring arterial oxygen saturation ( SpO 2 ) in individuals with darker skin pigmentation have resurfaced since the COVID-19 pandemic. This requires investigation to improve patient safety, clinical decision making, and research. Aim: We aim to use computational modeling to identify the potential causes of inaccuracy in SpO 2 measurement in individuals with dark skin and suggest practical solutions to minimize bias. Approach: An in silico model of the human finger was developed to explore how changing melanin concentration and arterial oxygen saturation ( SaO 2 ) affect pulse oximeter calibration algorithms using the Monte Carlo (MC) technique. The model generates calibration curves for Fitzpatrick skin types I, IV, and VI and an SaO 2 range between 70% and 100% in transmittance mode. SpO 2 was derived by inputting the computed ratio of ratios for light and dark skin into a widely used calibration algorithm equation to calculate bias ( SpO 2 - SaO 2 ). These were validated against an experimental study to suggest the validity of the Monte Carlo model. Further work included applying different multiplication factors to adjust the moderate and dark skin calibration curves relative to light skin. Results: Moderate and dark skin calibration curve equations were different from light skin, suggesting that a single algorithm may not be suitable for all skin types due to the varying behavior of light in different epidermal melanin concentrations, especially at 660 nm. The ratio between the mean bias in White and Black subjects in the cohort study was 6.6 and 5.47 for light and dark skin, respectively, from the Monte Carlo model. A linear multiplication factor of 1.23 and exponential factor of 1.8 were applied to moderate and dark skin calibration curves, resulting in similar alignment. Conclusions: This study underpins the careful re-assessment of pulse oximeter designs to minimize bias in SpO 2 measurements across diverse populations.

Time-independent relationship between digits closure velocity and hand transport acceleration during reach-to-grasp movements.

Prehension movements in primates have been extensively studied for decades, and hand transport and hand grip adjustment are usually considered as the main components of any object reach-to-grasp action. Evident temporal patterns were found for the velocity of the hand during the transport phase and for the digits kinematics during pre-shaping and enclosing phases. However, such kinematics were always analysed separately in regard to time, and never studied in terms of dependence one from another. Nevertheless, if a reliable one-to-one relationship is proven, it would allow reconstructing the digit velocity (and position) simply by knowing the hand acceleration during reaching motions towards the target object, ceasing the usual dependence seen in literature from time of movement and distance from the target. In this study, the aim was precisely to analyse reach-to-grasp motions to explore if such relationship exists and how it can be formulated. Offline and real-time results not only seem to suggest the existence of a time-independent, one-to-one relationship between hand transport and hand grip adjustment, but also that such relationship is quite resilient to the different intrinsic and extrinsic properties of the target objects such as size, shape and position.

Interference haptic stimulation and consistent quantitative tactility in transparent electrotactile screen with pressure-sensitive transistors.

Integrating tactile feedback through haptic interfaces enhances experiences in virtual and augmented reality. However, electrotactile systems, which stimulate mechanoreceptors directly, often yield inconsistent tactile results due to variations in pressure between the device and the finger. In this study, we present the integration of a transparent electrotactile screen with pressure-sensitive transistors, ensuring highly consistent quantitative haptic sensations. These transistors effectively calibrate tactile variations caused by touch pressure. Additionally, we explore remote-distance tactile stimulations achieved through the interference of electromagnetic waves. We validated tactile perception using somatosensory evoked potentials, monitoring the somatosensory cortex response. Our haptic screen can stimulate diverse electrotactile sensations and demonstrate various tactile patterns, including Morse code and Braille, when integrated with portable smart devices, delivering a more immersive experience. Furthermore, interference of electric fields allows haptic stimulation to facilitate diverse stimulus positioning at lower current densities, extending the reach beyond direct contact with electrodes of our screen.

Effects of Different Test Positions on Quantitative Muscle Strength of Wrist and Finger Flexor Muscle Groups and Its Standardization.

OBJECTIVES: To explore the effects of different test positions on quantitative muscle strength of wrist and finger flexor muscle groups and to establish a standardized muscle strength test protocol for each muscle group. METHODS: Forty healthy subjects (12 males and 28 females) were recruited. A portable digital quantitative muscle strength tester, Micro FET2TM, was used to measure the flexor muscle strength of each finger and the wrist joint at the 30° extension, 0° neutral, and 30° flexion, respectively. Palmar abduction strength of the thumb was measured at 30° and 60°, respectively. Ten subjects were randomly selected from the 40 subjects, and the quantitative muscle strength of each muscle group was tested again by the same operator after an interval of 10 to 15 days. RESULTS: Except for the fact that in males, there was no significant difference in flexor muscle strength of thumb and wrist joint between 30° of wrist extension and neutral 0° position, the muscle strength of the other fingers flexion and wrist palmar flexor showed the following characteristics：30° of wrist extension > neutral 0° position > 30° of flexion, and the PAST was 30°>60°; The flexor muscle strength of all the subjects was thumb > index finger > middle finger > ring finger > little finger; All muscle strength values of male were greater than those of female, and the difference was statistically significant (P<0.05); There was no significant difference between the left and right side muscle strength values of all subjects (P>0.05). The reliability of muscle strength values measured at different times in 10 subjects was good. CONCLUSIONS: The quantitative muscle strength of each muscle group of the hand and wrist is affected by the test position, and a standardized and uniformed test position should be adopted in the actual identification. Micro FET2TM has good reliability for hand and wrist quantitative muscle strength testing. The 30° extension of the wrist can be used as the best standardized test position for the flexion muscle strength of each finger and wrist joint. The 30° position can be used as the best standardized test position for PAST.

Force reflections of auditory and tactile action-effect weighting in motor planning.

Most voluntary actions have only few goals, which provides considerable freedom in the selection of action parameters. Recent studies showed that task-irrelevant aspects of the task context influence the motor parameters of the actions in a way which seems to reflect the relative importance of these aspects within the underlying action representation. The present study investigated how the intensity of auditory action-effects affected force exertion patterns in a self-paced action production task. Participants applied force impulses with their index finger on a force-sensitive resistor every three seconds. In four separate conditions, force impulses elicited no sound, or elicited tones with 69, 59 or 49 dB intensity. The results showed that participants applied more force when tone intensity was lower, and when tones were absent. These force differences were also present in the first 60 ms following tone onset, implying that these reflected differences in motor planning. The results are compatible with the notion that actions are represented in terms of their sensory effects, which are weighted differently-presumably to maintain an optimal level of overall auditory and tactile stimulation in the present case. These results hint at the potential usefulness of motor parameters as readouts of action intentions.

DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition.

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.