首页期刊导航|IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society
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IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society
IEEE
IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society

IEEE

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1534-4320

IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society/Journal IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology SocietyEIISTPSCI
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    Visual Angles and Emotional Valence Affect Temporal Dynamics of Neural Representations of Facial Expression: An MEG Study

    Sanjeev NaraDheeraj RatheeNicola MolinaroNaomi Du Bois...
    1-9页
    查看更多>>摘要：Emotion processing has been a focus of research in Cognitive Neuroscience for decades. While the evoked neural markers as brain activations in response to different emotions have been reported, the temporal dynamics of emotion processing have received little attention. Furthermore, behavioral studies have found that the right side of the human face expresses emotions more accurately than the left side. Therefore, accounting for both the content of the emotion and the visual angle of the presentation from the viewer’s perspective, we have investigated temporal dynamics and variability in the processing of happy and sad emotions using magnetoencephalography (MEG), when the visual angle of presentation was either Positive (right side of the face) or Negative (left side of the face). Our results showed that decodable processing of happy emotions emerged earlier than that for sad emotions, irrespective of visual angle. However, the amplitude of the evoked response to sad emotions was higher than that to happy emotions, when faces were presented at Positive visual angles only. Source reconstructed Event Related Fields (ERFs) showed localized activities in ventral and dorsal streams including fusiform gyrus, lingual gyrus, putamen and pre and post central gyrus. Multivariate pattern analysis also demonstrated successful decoding of happy and sad emotions only when the facial expression was viewed from a positive visual angle.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Stimulation-Induced Muscle Deformation Measured With A-Mode Ultrasound Correlates With Muscle Fatigue

    Jonathan T. AlvarezYichu JinDabin K. ChoeElizabeth L. Suitor...
    10-21页
    查看更多>>摘要：Muscle fatigue is a common physiological phenomenon whose onset can impair physical performance and increase the risk of injury. Traditional assessments of muscle fatigue are primarily constrained by their dependence on maximum voluntary contractions (MVCs), which not only rely heavily on participant motivation, reducing measurement accuracy, but also require large, stationary equipment such as isokinetic dynamometers, limiting their application to discrete assessments in lab-based environments. In this work, we introduce a wearable muscle fatigue tracking strategy that employs low-profile single-element ultrasound and electrical stimulation. This integrated approach demonstrates that muscle deformation from electrically-induced muscle contractions correlates with muscle fatigue, thus circumventing the need for bulky hardware and eliminating the variability associated with human volition. We define a deformation index, which fuses stimulation-induced changes in muscle thickness with baseline muscle swelling to track muscle fatigue. Our results demonstrate that the deformation index reliably tracks muscle fatigue ( ${r} = 0.85~\pm ~0.15$ ), under specific conditions, namely extended joint angles and increased stimulation, as measured by changes in knee extension torque during a series of dynamic, volitional fatiguing contractions on 8 subjects on an isokinetic dynamometer. This approach has the potential to enable real-time, semi-continuous muscle fatigue monitoring in unconstrained environments.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Expanding Functional Workspace for People With C5-C7 Spinal Cord Injury With Supernumerary Dorsal Grasping

    Jungpyo LeeAndrew I. W. McPhersonHaoxiang HuangLicheng Yu...
    22-33页
    查看更多>>摘要：Spinal cord injuries (SCI) substantially affect sensory, motor, and autonomous functions below the level of injury, reducing the independence and quality of life for affected individuals. Specifically, people with SCI between C5 and C7 cervical levels encounter limitations in voluntary finger and wrist flexion, reducing grasp capability. Compensatory strategies like tenodesis grasp, whereby wrist extension passively closes the fingers, remain; this is effective for small and light objects but insufficient for heavier ones. Typically, wearable assistive exoskeletons are designed to actuate a person’s fingers, however, such devices are sensitive to anatomical variability, such as hand size and joint contractures. The Dorsal Grasper is a wearable device designed to address this challenge by leveraging voluntary wrist extension and providing human-robot collaborative grasping capabilities with underactuated supernumerary fingers on the back of the hand. In this study, we introduce kinematic assessment methods that we use to show how the Dorsal Grasper expands the graspable workspace and reduces trunk motion, especially in situations where the use of a wheelchair restricts the individual’s posture. Our functionally relevant experiments with multiple SCI participants demonstrate the Dorsal Grasper’s potential as a versatile assistive solution for enhancing grasping capability in individuals with distinct SCI profiles.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Intracranial Disease-Region Composite-Interpretation Technology for Enhanced Source Localization in Pediatric Epilepsy Surgery

    Jeongyoon ShinWonsik YangJungmin SeoWon Seok Chang...
    34-45页
    查看更多>>摘要：Electroencephalography (EEG) based source localization (ESL) is a useful method to localize the epileptogenic zone in epilepsy surgery. However, previous techniques only perform 3-dimensional (3D) reconstruction, and do not conduct delineation on the cortex surface as a resection guidance, and there is very little data on intracranial EEG and pediatric cases. This study proposes an Intracranial Disease-region Composite-interpretation (IDC) EEG-based source localization (ESL) scheme that uses 3D extended reality (XR) edge computing to enhance visualization and comprehensive interpretation of intracranial EEG-based source localization (iESL) for patients with pediatric epilepsy. The proposed IDC-ESL method was effective in predicting the surgical outcome in patients with focal epilepsy, which can be effectively used for epilepsy surgery. Seizure freedom was clearly associated with complete resection of combined EEG features of interictal spike, high-frequency oscillation (HFO), and seizure onset zone (SOZ), and it had the highest significance in localizing the epileptogenic zone. However, for patients with Lennox-Gastaut syndrome (LGS), IDC-ESL was not performed effectively because of a deeply seated lesion and multifocal abnormalities. It could only roughly estimate the affected area, mainly because of insular involvement. Cautious interpretation based on intraoperative electrocorticography (ECoG) is required for accurate insular resection, particularly for LGS cases.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Transformer-Based Approach for Predicting Transactive Energy in Neurorehabilitation

    Naveed Ahmad KhanTanishka GoyalFahad HussainPrashant K. Jamwal...
    46-57页
    查看更多>>摘要：Advancements in robotic neurorehabilitation have made it imperative to enhance the safety and personalization of physical human-robot interactions (pHRI). Estimation and management of energy transfer between humans and robots is essential for enhancing safety during the rehabilitation. Traditional control methods, which rely on coordinate-based monitoring of robot velocity and external forces, often fail in unstructured environments due to their susceptibility to sensor noise and limited adaptability to individual patient needs. This paper introduces the concept of transactive energy, a coordinate-invariant entity that captures the energy dynamics between the human and the robot during robot-assisted rehabilitation and can be used for personalized robot control. However, estimation of such energy transfer is a complex process and therefore, we have developed a transformer-based model to predict the transactive potential energy. The proposed model is implemented on an ankle rehabilitation robot which is a compliant parallel robot and provides the required three rotational degrees of freedom (DOF). The model learns from the data obtained from the experiments carried out using the ankle robot with five stroke patients on two types of controllers: an impedance controller operated in zero impedance control mode and a trajectory tracking controller. This study provides a baseline, for future research on energy-based control mechanisms in pHRI applications, by utilizing the advanced deep learning models.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Continuous Estimation of Hand Kinematics From Electromyographic Signals Based on Power-and Time-Efficient Transformer Deep Learning Network

    Chuang LinChunxiao ZhaoJianhua ZhangChen Chen...
    58-67页
    查看更多>>摘要：Surface Electromyographic (sEMG) signals contain motor-related information and therefore can be used for human-machine interaction (HMI). Deep learning plays an important role in extracting motor-related information from sEMG signals. However, most studies prioritize model accuracy without sufficient consideration of model efficiency, including the model size, power consumption, and the computational speed of the model. This leads to impractical power consumption, heat dissipation levels and processing time in wearable computation scenarios. Here, we propose an efficient Transformer method that employs the EMSA (Efficient Multiple Self-Attention) and pruning mechanism to improve efficiency and accuracy concurrently, when estimating finger joint angles from sEMG signals. The proposed method does not only achieve state-of-the-art accuracy but can also be deployed on wearable devices to satisfy real-time applications. We applied the proposed model on the Ninapro DB2-dataset to estimate finger joint angles during grasping tasks. RNN series models, Convolution series models, and Transformer series models were used as reference models for comparison. In addition to common model accuracy, the deployment performance of the models was tested on microprocessors, such as Intel CPU i5, Apple M1, and Raspberry Pi 4B. When tested on 38 subjects of the Ninapro DB2, the proposed model resulted in a correlation coefficient of $0.82~\pm ~0.04$ , root mean squared error (RMSE) of $10.77~\pm ~1.48$ , and normalized RMSE of $0.11~\pm ~0.01$ , which were all similar to the results achieved by the state-of-the-art (SOTA) reference methods. Further, the computational time of the proposed methods was 65.99 ms on the Raspberry Pi 4B, which outperformed all the RNN series models and the Transformer series models. The model size and the power (the minimum size and power are 0.39 MB and 2.28 w) consumption of the proposed model also outperformed that of all reference Transformer methods. These experimental results indicate that our model can maintain the accuracy of the SOTA methods while significantly improving efficiency, thus being a promising approach for real-life applications in wearable devices.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    The Multi-Frequency Decomposition Entropy Learning for Nonlinear fMRI Data Analysis

    Di HanYuhu ShiLei WangYueyang Li...
    68-80页
    查看更多>>摘要：Functional magnetic resonance imaging (fMRI) have been widely adopted to explore the underlying neural mechanisms between psychiatric disorders which share common neurobiology and clinical manifestations. However, the existing studies mainly focus on linear relationships and ignore nonlinear contributions. To address the above issues, we propose a new method named multi-frequency decomposition entropy (MDE) learning for inferring nonlinear functional connectivity between brain regions. Firstly, the variational mode decomposition was used to divide fMRI data into five groups of frequency. Next, the copula entropy was used to calculate the nonlinear relationship between brain regions in each frequency group, and then the best important nonlinear relationships were screen out by using statistical t-test. Lastly, a gyrus importance index was proposed to reflect the distribution trend of gyri in different frequency groups. The results of applying MDE for the fMRI data analysis of schizophrenia, bipolar disorder, and attention-deficit hyperactivity disorder showed that the difference between the three groups of patient and healthy control is large at the hub nodes, and the nonlinear relationship between the patient groups is weak when they are at the same hub node. In addition, each disease exhibits unique characteristics compared with other diseases and healthy control. In a word, the nonlinear functional connectivity of different frequency groups reflect the differences and commonalities between diseases and reveal possible discriminating biomarkers among mental diseases.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    An Online Estimating Framework for Ankle Actively Exerted Torque Under Multi-DOF Coupled Dynamic Motions via sEMG

    Yu ZhouJianfeng LiShiping ZuoJie Zhang...
    81-91页
    查看更多>>摘要：Ankle rehabilitation robots can offer tailored rehabilitation training, and facilitate the functional recovery of patients. Accurate estimation of the actively exerted torque from the ankle joint complex (AJC) can increase the engagement of patients during rehabilitation training. Given the three degrees of freedom (DOFs) of AJC and its coupled motion, it becomes essential to accurately estimate the actively exerted torque under multi-DOF. This work introduces an estimation framework that includes the Hill-based sEMG-force model, the ankle musculoskeletal dynamic decoupling model, and the parameter identification-calibration strategy. The Hill-based sEMG-force model estimates the force generated by individual muscles involved in AJC; The parameter identification-calibration strategy combined with pre-experiment identifies unknown variables in the ankle musculoskeletal dynamic decoupling model; Finally, the musculoskeletal dynamic decoupling model relates the muscle forces to the AJC’s actively exerted torque. The musculoskeletal dynamic decoupling model combines anatomical and biomechanical features, enabling parameters derived from a single DOF pre-experiment through identification-calibration strategy to be applicable in multi-DOF dynamic motion. To evaluate the estimation performance of the framework, experiments were conducted in various directions involving both single and multiple DOFs. The results show that the proposed framework can estimate the actively exerted torque with a normalized root mean square error (NRMSE) of ${10}.{29}\% \pm {2}.{86}\%$ (mean ± SD) for torque estimation under a single DOF, and NRMSE of ${11}.{35}\% \pm {4}.{51}\%$ under multiple DOFs, compared to the actual measured values. This framework can improve human-robot interaction training and improve the effectiveness of robot-assisted ankle rehabilitation training. It can also provide accurate neuro-information and joint torque data for medical teams, which can lead to early diagnosis of diseases and patient-specific treatment protocols.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Feasibility Assessment of an Optically Powered Digital Retinal Prosthesis Architecture for Retinal Ganglion Cell Stimulation

    William LemaireMaher BenhouriaKonin KouaWei Tong...
    92-102页
    查看更多>>摘要：Clinical trials previously demonstrated the notable capacity to elicit visual percepts in individuals with visual impairments caused by retinal diseases by electrically stimulating the remaining neurons on the retina. However, these implants restored very limited visual acuity and required transcutaneous cables traversing the eyeball, leading to reduced reliability and complex surgery with high postoperative infection risks. To overcome the limitations imposed by cables, a retinal implant architecture in which near-infrared illumination carries both power and data through the pupil to a digital stimulation controller is presented. A high efficiency multi-junction photovoltaic cell transduces the optical power to a CMOS stimulator capable of delivering flexible interleaved sequential stimulation through a diamond microelectrode array. To demonstrate the capacity to elicit a neural response with this approach while complying with the optical irradiance limit at the pupil, fluorescence imaging with a calcium indicator is used on a degenerate rat retina. The power delivered by the laser at the permissible irradiance of 4 mW/mm2 at 850 nm is shown to be sufficient to both power the stimulator ASIC and elicit a response in retinal ganglion cells (RGCs), with the ability to generate of up to 35 000 pulses per second at the average stimulation threshold. This confirms the feasibility of generating a response in RGCs with an infrared-powered digital architecture capable of delivering complex sequential stimulation patterns at high repetition rates, albeit with some limitations.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Decoding Motor Excitability in TMS Using EEG-Features: An Exploratory Machine Learning Approach

    Lisa HaxelOskari AholaPaolo BelardinelliMaria Ermolova...
    103-112页
    查看更多>>摘要：Brain state-dependent transcranial magnetic stimulation (TMS) holds promise for enhancing neuromodulatory effects by synchronizing stimulation with specific features of cortical oscillations derived from real-time electroencephalography (EEG). However, conventional approaches rely on open-loop systems with static stimulation parameters, assuming that pre-determined EEG features universally indicate high or low excitability states. This one-size-fits-all approach overlooks individual neurophysiological differences and the dynamic nature of brain states, potentially compromising therapeutic efficacy. We present a supervised machine learning framework that predicts individual motor excitability states from pre-stimulus EEG features. Our approach combines established biomarkers with a comprehensive set of spectral and connectivity measures, implementing multi-scale feature selection within a nested cross-validation scheme. Validation across multiple classifiers, feature sets, and experimental protocols in 50 healthy participants demonstrated a mean prediction accuracy of $71 \; \pm \; 7$ %. Hierarchical clustering of top predictive EEG features revealed two distinct participant subgroups. The first subgroup, comprising approximately 50% of participants, showed predictive features predominantly in alpha and low-beta bands in sensorimotor regions of the stimulated hemisphere, aligning with traditional associations of motor excitability and the sensorimotor $\mu $ -rhythm. The second subgroup exhibited predictive features primarily in low and high gamma bands in parietal regions, suggesting that motor excitability is influenced by broader neural dynamics for these individuals. Our data-driven framework effectively identifies personalized motor excitability biomarkers, holding promise to optimize TMS interventions in clinical and research settings. Additionally, our approach provides a versatile platform for biomarker discovery and validation across diverse neuromodulation paradigms and brain signal classification tasks.
      原文链接:
    • NETL
    • NSTL
    • IEEE