Finger Movements Research Articles

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs

Functional magnetic resonance imaging (fMRI) studies have documented cerebellar activity across a wide array of tasks. However, the functional contribution of the cerebellum within these task domains remains unclear because cerebellar activity is often studied in isolation. This is problematic, as cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections. Here, we present a new approach that addresses this problem. Rather than focus on task-dependent activity changes in the cerebellum alone, we ask if neocortical inputs to the cerebellum are gated in a task-dependent manner. We hypothesize that input is upregulated when the cerebellum functionally contributes to a task. We first validated this approach using a finger movement task, where the integrity of the cerebellum has been shown to be essential for the coordination of rapid alternating movements but not for force generation. While both neocortical and cerebellar activity increased with increasing speed and force, the speed-related changes in the cerebellum were larger than predicted by an optimized cortico-cerebellar connectivity model. We then applied the same approach in a cognitive domain, assessing how the cerebellum supports working memory. Enhanced gating was associated with the encoding of items in working memory, but not with the manipulation or retrieval of the items. Focusing on task-dependent gating of neocortical inputs to the cerebellum offers a promising approach for using fMRI to understand the specific contributions of the cerebellum to cognitive function.

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs.

Functional magnetic resonance imaging (fMRI) studies have documented cerebellar activity across a wide array of tasks. However, the functional contribution of the cerebellum within these task domains remains unclear because cerebellar activity is often studied in isolation. This is problematic, as cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections. Here, we present a new approach that addresses this problem. Rather than focus on task-dependent activity changes in the cerebellum alone, we ask if neocortical inputs to the cerebellum are gated in a task-dependent manner. We hypothesize that input is upregulated when the cerebellum functionally contributes to a task. We first validated this approach using a finger movement task, where the integrity of the cerebellum has been shown to be essential for the coordination of rapid alternating movements but not for force generation. While both neocortical and cerebellar activity increased with increasing speed and force, the speed-related changes in the cerebellum were larger than predicted by an optimized cortico-cerebellar connectivity model. We then applied the same approach in a cognitive domain, assessing how the cerebellum supports working memory. Enhanced gating was associated with the encoding of items in working memory, but not with the manipulation or retrieval of the items. Focusing on task-dependent gating of neocortical inputs to the cerebellum offers a promising approach for using fMRI to understand the specific contributions of the cerebellum to cognitive function.

Hemodynamics of short-duration light-intensity physical exercise in the prefrontal cortex of children: a functional near-infrared spectroscopy study

Identifying the types of exercise that enhance cerebral blood flow is crucial for developing exercise programs that enhance cognitive function. Nevertheless, few studies have explored the amount of light-intensity, short-duration exercises that individuals can easily perform on cerebral blood flow, particularly in children. We examined the effects of these exercises on the hemodynamics of the prefrontal cortex (PFC) using functional near-infrared spectroscopy. Participants comprised 41 children (aged 12.1 ± 1.5 years, 37% female) who engaged in seven light-intensity exercises, with each movement performed in two patterns lasting 10 or 20 s. Changes in oxygenated hemoglobin (oxy-Hb) levels at rest and during exercise were compared using analysis of covariance, with sex and age as covariates. Significant increases in oxy-Hb were observed in multiple regions of the PFC during all forms of exercise (including dynamic and twist stretching [66.6%, 8/12 regions, η2 = 0.07–0.27], hand and finger movements [75.0%, 9/12 regions, η2 = 0.07–0.16], and balance exercises (100.0%, 6/6 regions, η2 = 0.13–0.25]), except for static stretching with monotonic movements. This study implies that short-duration, light-intensity exercises, provided that they entail a certain degree of cognitive and/or physical demands, can activate the PFC and increase blood flow.

Top-down and bottom-up oscillatory dynamics regulate implicit visuomotor sequence learning.

Implicit visuomotor sequence learning is crucial for acquiring skills that result in automated behaviors. The oscillatory dynamics underpinning this learning process are not well understood. To address this gap, the current study employed electroencephalography with a medium-density array (64 electrodes) to investigate oscillatory activity associated with implicit visuomotor sequence learning in the Serial Reaction Time task. In the task, participants unknowingly learn a series of finger movements. Eighty-five healthy adults participated in the study. Analyses revealed that theta activity at the vertex and alpha/beta activity over the motor areas decreased over the course of learning. No associations between alpha/beta and theta power were observed. These findings are interpreted within a dual-process framework: midline theta activity is posited to regulate top-down attentional processes, whereas beta activity from motor areas underlies the bottom-up encoding of sensory information from movement. From this model, we suggest that during implicit visuomotor sequence learning, top-down processes become disengaged (indicated by a reduction in theta activity), and modality specific bottom-up processes encode the motor sequence (indicated by a reduction in alpha/beta activity).

Bio-Skin-Inspired Flexible Pressure Sensor Based on Carbonized Cotton Fabric for Human Activity Monitoring.

With the development of technology, people's demand for pressure sensors with high sensitivity and a wide working range is increasing. An effective way to achieve this goal is simulating human skin. Herein, we propose a facile, low-cost, and reproducible method for preparing a skin-like multi-layer flexible pressure sensor (MFPS) device with high sensitivity (5.51 kPa-1 from 0 to 30 kPa) and wide working pressure range (0-200 kPa) by assembling carbonized fabrics and micro-wrinkle-structured Ag@rGO electrodes layer by layer. In addition, the highly imitated skin structure also provides the device with an extremely short response time (60/90 ms) and stable durability (over 3000 cycles). Importantly, we integrated multiple sensor devices into gloves to monitor finger movements and behaviors. In summary, the skin-like MFPS device has significant potential for real-time monitoring of human activities in the field of flexible wearable electronics and human-machine interaction.

Ventrointermediate thalamic stimulation improves motor learning in humans

Ventrointermediate thalamic stimulation (VIM-DBS) modulates oscillatory activity in a cortical network including primary motor cortex, premotor cortex, and parietal cortex. Here we show that, beyond the beneficial effects of VIM-DBS on motor execution, this form of invasive stimulation facilitates production of sequential finger movements that follow a repeated sequence. These results highlight the role of thalamo–cortical activity in motor learning.

Single Finger Trajectory Prediction From Intracranial Brain Activity Using Block-Term Tensor Regression With Fast and Automatic Component Extraction.

Multiway- or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector- or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally efficient manner, leading to a significant performance gain over conventional vector- or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.

Low-Cost Flexible Graphene Oxide Humidity Sensor Fabricated Using Inkjet Printing and Aerosol Deposition

Flexible humidity sensors play a critical role in medical diagnostics and industrial control systems. In this study, a low-cost flexible humidity sensor is presented. The humidity sensor is developed by printing silver interdigitated electrodes (IDE) on a polyethylene terephthalate (PET) substrate using an Epson Stylus C88+ inkjet printer. The sensing layer of the humidity sensor was fabricated using graphene oxide (GO) ink, which is deposited onto the electrodes using an aerosol deposition technique. The GO humidity sensor achieves excellent sensing performance over a wide range of humidity levels from 11% to 97% RH range, with a fast response time of 2 s and recovery time of 17 s. The sensor also exhibits ultra-high sensitivity (243 kΩ/%RH), low hysteresis (2.16%), excellent repeatability, long-term stability, and high flexibility (tested at bending radiuses of 4 cm, 3.5 cm, 3 cm, and 2.5 cm). The humidity sensing mechanism of the proposed GO humidity sensor was also discussed. Furthermore, the sensor exhibited excellent capabilities in monitoring human respiration, distinguishing between nose and mouth breathing, detecting finger movements without physical contact, and even recognising basic spoken words. These features of the sensor possess significant potential for various applications in human healthcare.

ROBUSTNESS EVALUATION OF MACHINE LEARNING ALGORITHMS FOR NEUROCOMPUTER INTERFACE SOFTWARE USING DISTRIBUTED AND PARALLEL COMPUTING

This article highlights the field of information technology for brain-computer interaction, and the main goal is to use it to determine patterns of human brain activity using electroencephalography (EEG) data. During the execution of the article, machine learning methods were used, namely such classifiers as Random Forest, Multi-Layer Perceptron (MLP), and Logistic Regression. The investigation begins with real-world experiments recording EEG signals during finger movement tasks, providing valuable insight into the complex dynamics of brain operation and interaction. Utilizing 10-fold cross-validation, the performance of each classifier is rigorously evaluated across various metrics, including accuracy, f1_weighted та roc_auc_ovr_weighted. Through this process, the robustness and consistency of classifier performance are assessed, with dispersion values computed to gauge variability across iterations. The findings reveal nuanced differences among the classifiers, with MLP demonstrating the highest robustness, followed by Logistic Regression and Random Forest. The main goal of the article was to find out the importance of such a classifier performance parameter as robustness. Software robustness is a key characteristic, especially in medical applications, where consistent and reliable performance of information technology is paramount. Neural interfaces offer many avenues for solving various limb problems, spinal cord injuries, and neurological diseases in humans. These devices contribute to improving the quality of life by minimizing these problems in people, which leads to increased mobility and functional capabilities of people. The article also emphasizes the potential associated with the transformation of neurointerface technologies in expanding human capabilities and revolutionizing human-machine interaction. In conclusion, the research contributes to advancing the field of brain-computer interaction by leveraging machine learning algorithms to decode neural signals and uncover hidden patterns within EEG data. By identifying the most stable classifier, the study lays the groundwork for the development of robust neurointerface technologies with practical applications in healthcare, rehabilitation, and beyond. Through interdisciplinary collaboration and innovative methodologies, the journey towards unlocking the full potential of brain-computer interaction continues, promising new horizons in human augmentation and technological innovation.

Design And Develop Robotic Arm For People With Disable Hands Using EMG And Gyroscope Sensor Control

Robotics technology is currently developing rapidly, including in the medical field. In this world there are many people with hand disabilities or patients who have amputations on their wrists, so they experience difficulties in carrying out their daily activities using their hands, such as to grasp objects. Then the thought arose to conduct research, with the aim of creating a Robotic Arm that could recognize 2 movements, namely the opening and clenching of the fingers like a hand in general. The Electromyogram sensor is used as a Robotic Arm control system, while the Gyroscope with Arduino as the control center. Based on the overall test results, the Robotic Arm can be moved with muscle contractions and user gestures to open and clench finger movements.

Interpretable video-based tracking and quantification of parkinsonism clinical motor states

Quantification of motor symptom progression in Parkinson’s disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical “black-box” ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive kinematic dataset and developing an interpretable video-based framework that predicts high versus low PD motor symptom severity according to MDS-UPDRS Part III metrics. This data driven approach validated and robustly quantified canonical movement features and identified new clinical insights, not previously appreciated as related to clinical severity, including pinkie finger movements and lower limb and axial features of gait. Our framework is enabled by retrospective, single-view, seconds-long videos recorded on consumer-grade devices such as smartphones, tablets, and digital cameras, thereby eliminating the requirement for specialized equipment. Following interpretable ML principles, our framework enforces robustness and interpretability by integrating (1) automatic, data-driven kinematic metric evaluation guided by pre-defined digital features of movement, (2) combination of bi-domain (body and hand) kinematic features, and (3) sparsity-inducing and stability-driven ML analysis with simple-to-interpret models. These elements ensure that the proposed framework quantifies clinically meaningful motor features useful for both ML predictions and clinical analysis.

Electromechanical Performance and Figure of Merit Optimization for Flexible Lead-Free PDMS-BaTiO 3 Piezocomposites.

In the modern era of the Internet of Things, the potential role of flexible piezoelectric generators (PEG) reflects the rapid increase in self-powered devices and wearable technologies. In this study, a casting process to elaborate the polydimethylsiloxane (PDMS)/barium titanate (BaTiO 3) composite has been presented. The addition of 15 wt % BaTiO 3 microparticles into the PDMS polymer greatly enhances the piezoelectric coefficient (d 31 = 24 pC N-1), leading to an increased output voltage of approximately 4 V under finger tapping force. The proposed flexible microgenerator yielded an excellent piezoelectric figure of merit (FoM 31 = 13.1 × 10-12 m2 N-1), significantly enhancing successfully the energy-harvesting performance (power density of 35 nW/cm2). Furthermore, the fabricated lead-free PEG exhibited an excellent flexibility figure of merit (fFoM) due to the low young modulus values (Maximum E = 3.4 MPa). These results indicate efficient energy conversion and demonstrate a favorable balance between the flexibility and piezoelectric properties of the composite, highlighting its potential for a wide range of applications in self-powered wearable sensors able to collect different human motions in applications such as gesture tracking and finger motion detection.

Needle fasciotomy versus limited fasciectomy for the treatment of Dupuytren’s contractures of the fingers (Hand-2): study protocol for a randomised controlled trial

BackgroundDupuytren’s contractures (DC) are fibrous cords under the skin of the hand that cause one or more fingers to curl gradually and irreversibly towards the palm. These contractures are usually painless but can cause a loss of hand function. Two treatments for Dupuytren’s contractures are widely used within the National Health Service (NHS) in the UK: removal of the contractures via surgery (limited fasciectomy) and division of the contractures via a needle inserted through the skin (needle fasciotomy).This study aims to establish the clinical and cost-effectiveness of needle fasciotomy (NF) versus limited fasciectomy (LF) for the treatment of DC in the NHS, in terms of patient-reported hand function and resource utilisation.Methods/designHand-2 is a national multi-centre, two-arm, parallel-group randomised, non-inferiority trial. Patients will be eligible to join the trial if they are aged 18 years or older, have at least one previously untreated finger with a well-defined Dupuytren’s contracture of 30° or greater that causes functional problems and is suitable for treatment with either LF or NF. Patients with a contracture of the distal interphalangeal joint only are ineligible. Eligible consenting patients will be randomised 1:1 to receive either NF or LF and will be followed up for 24 months post-treatment. A QuinteT Recruitment Intervention will be used to optimise recruitment. The primary outcome measure is the participant-reported assessment of hand function, assessed by the Hand Health Profile of the Patient Evaluation Measure (PEM) questionnaire at 12 months post-treatment. Secondary outcomes include other patient-reported measures, loss of finger movement, and cost-effectiveness, reported over the 24-month post-treatment. Embedded qualitative research will explore patient experiences and acceptability of treatment at 2 years post-surgery.DiscussionThis study will determine whether treatment with needle fasciotomy is non-inferior to limited fasciectomy in terms of patient-reported hand function at 12 months post-treatment.Trial registrationInternational Standard Registered Clinical/soCial sTudy ISRCTN12525655. Registered on 18th September 2020.

Design and Control of a Tendon-Driven Robotic Finger Based on Grasping Task Analysis.

To analyze the structural characteristics of a human hand, data collection gloves were worn for typical grasping tasks. The hand manipulation characteristics, finger end pressure, and finger joint bending angle were obtained via an experiment based on the Feix grasping spectrum. Twelve types of tendon rope transmission paths were designed under the N + 1 type tendon drive mode, and the motion performance of these 12 types of paths applied to tendon-driven fingers was evaluated based on the evaluation metric. The experiment shows that the designed tendon path (d) has a good control effect on the fluctuations of tendon tension (within 0.25 N), the tendon path (e) has the best control effect on the joint angle of the tendon-driven finger, and the tendon path (l) has the best effect on reducing the friction between the tendon and the pulley. The obtained tendon-driven finger motion performance model based on 12 types of tendon paths is a good reference value for subsequent tendon-driven finger structure design and control strategies.

Hebbian priming of human motor learning

Motor learning relies on experience-dependent plasticity in relevant neural circuits. In four experiments, we provide initial evidence and a double-blinded, sham-controlled replication (Experiment I-II) demonstrating that motor learning involving ballistic index finger movements is improved by preceding paired corticospinal-motoneuronal stimulation (PCMS), a human model for exogenous induction of spike-timing-dependent plasticity. Behavioral effects of PCMS targeting corticomotoneuronal (CM) synapses are order- and timing-specific and partially bidirectional (Experiment III). PCMS with a 2 ms inter-arrival interval at CM-synapses enhances learning and increases corticospinal excitability compared to control protocols. Unpaired stimulations did not increase corticospinal excitability (Experiment IV). Our findings demonstrate that non-invasively induced plasticity interacts positively with experience-dependent plasticity to promote motor learning. The effects of PCMS on motor learning approximate Hebbian learning rules, while the effects on corticospinal excitability demonstrate timing-specificity but not bidirectionality. These findings offer a mechanistic rationale to enhance motor practice effects by priming sensorimotor training with individualized PCMS.

Motor imagery of finger movements: Effects on cortical and muscle activities

PurposeThe purpose of the current study was to explore the immediate effect of motor imagery (MI) involving finger movement of a given limb on cortical response and muscle activity in healthy subjects. MethodsTwenty healthy right-handed adults (7 females and 13 males) with a mean + SD age of 22.05 + 6.08 years participated in the study. The beta-band event-related desynchronization (ERD) at the sensorimotor cortex and muscle activity during finger movement tasks using either the index, middle, or thumb digits on the non-dominant left hand were compared before and after an MI training session. Subjects underwent a pre-MI, MI training, and finally a post-MI session where they either performed or imagined performing a button-pushing action 50 times per session with each of the three digits. ResultsThe ERD power in the beta frequency band was lower in pre-MI compared to post-MI and was significantly different between the pre- and post-MI sessions for both the index and middle fingers, but not the thumb. A significant decrease was seen in the mean muscle activity during post-MI compared to pre-MI for all the digits except the thumb. ConclusionsThe results from the current study suggest that complex MI can result in motor learning and improvement in motor performance, thereby requiring less effort during motion.

A Review of Wearable Optical Fiber Sensors for Rehabilitation Monitoring.

As the global aging population increases, the demand for rehabilitation of elderly hand conditions has attracted increased attention in the field of wearable sensors. Owing to their distinctive anti-electromagnetic interference properties, high sensitivity, and excellent biocompatibility, optical fiber sensors exhibit substantial potential for applications in monitoring finger movements, physiological parameters, and tactile responses during rehabilitation. This review provides a brief introduction to the principles and technologies of various fiber sensors, including the Fiber Bragg Grating sensor, self-luminescent stretchable optical fiber sensor, and optic fiber Fabry-Perot sensor. In addition, specific applications are discussed within the rehabilitation field. Furthermore, challenges inherent to current optical fiber sensing technology, such as enhancing the sensitivity and flexibility of the sensors, reducing their cost, and refining system integration, are also addressed. Due to technological developments and greater efforts by researchers, it is likely that wearable optical fiber sensors will become commercially available and extensively utilized for rehabilitation.

3D Printed Carbon Nanotubes Reinforced Polydimethylsiloxane Flexible Sensors for Tactile Sensing

Technology is constantly evolving, and chronic health issues are on the rise. It is essential to have affordable and easy access to remote biomedical measurements. This makes flexible sensors a more attractive choice owing to their high sensitivity and flexibility along with low cost and ease of use. As an additional advantage, 3D printing has become increasingly popular in areas such as biomedicine, environment, and industry. This study demonstrates 3D-printed flexible sensors for tactile sensing. A biocompatible silicone elastomer such as polydimethylsiloxane (PDMS) with low elastic modulus and high stretchability makes an excellent wearable sensor material. Incorporating CNTs at varying concentrations (0.5, 1, 2)wt% enhances the sensor’s mechanical strength, conductivity, and responsiveness to mechanical strain. In addition to enhancing the thermal stability of the composite by 44%, multi-walled carbon nanotubes (MWCNTs) also enhanced the breaking strength by 57% with a 2 wt% CNT loading. Moreover, the contact angle values improved by 15%, making it a biomedical-grade hydrophobic surface. The electrical characteristics of these sensors reveal excellent strain sensitivity, making them perfect for monitoring finger movements and biomedical measurements. Overall, 2 wt% CNT-PDMS sensors exhibit optimal performance, paving the way for advanced tactile sensing in biomedical and industrial settings.

Towards Efficient Neural Decoder for Dexterous Finger Force Predictions.

Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations. We developed a deep forest-based neural decoder to concurrently predict the extension and flexion forces of three fingers (index, middle, and ring-pinky). We trained the model using varying amounts of high-density EMG data in a limited condition (i.e., single-finger data). We showed that the deep forest decoder could achieve consistently commendable performance with 7.0% of force prediction errors and R2 value of 0.874, significantly surpassing the conventional EMG amplitude method and convolutional neural network approach. However, the deep forest decoder accuracy degraded when a smaller amount of data was used for training and when the testing data became noisy. The deep forest decoder shows accurate performance in multi-finger force prediction tasks. The efficiency aspect of the deep forest lies in the short training time and small volume of training data, which are two critical factors in current neural decoding applications. This study offers insights into efficient and accurate neural decoder training for advanced robotic hand control, which has the potential for real-life applications during human-machine interactions.

An optimized EEGNet decoder for decoding motor image of four class fingers flexion

As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.