IEE Transactions on Neural Systems and Rehabilitation Engineering This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Subject-Specific Modeling of EEG-fNIRS Neurovascular Coupling by Task-Related Tensor Decomposition
In this study, we proposed a novel framework to enhance the subject-specific parametric modeling of NVC from simultaneous EEG-fNIRS measurement. Specifically, task-related tensor decomposition of high-order EEG data was performed to extract the underlying connections in the temporal-spectral-spatial structures of EEG activities and identify the most relevant temporal signature within multiple trials. Subject-specific HRFs were estimated by parameters optimization of a double gamma function model. A canonical motor task experiment was designed to induce neural activity and validate the effectiveness of the proposed framewor...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 17, 2024 Category: Neuroscience Source Type: research

Development and Validation of a Self-Aligning Knee Exoskeleton With Hip Rotation Capability
The self-aligning capability of an exoskeleton is important to ensure wearing comfort, and the delicate motion ability of the exoskeleton is essential for motion assistance. Designing a self-aligning exoskeleton that offers improved wearing comfort and enhanced motion-assistance functions remains a challenge. This paper proposes a novel spatial self-aligning mechanism for a knee exoskeleton to enable simultaneous assistance in the flexion and extension (FE) of the knee joint and the internal and external rotation (IER) of the hip joint. Additionally, considering the misalignment of the human-robot joint axes, a kinematic m...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 16, 2024 Category: Neuroscience Source Type: research

Human Gait Entrainment to Soft Robotic Hip Perturbation During Simulated Overground Walking
In this study, we hypothesized that the use of a variable-speed treadmill (VST), which enables the participants to continuously adjust their speed, can improve the success rate of gait entrainment and preserve natural gait biomechanics. To test this hypothesis, we recruited 15 young and healthy adults and let them walk on a conventional FST and a self-paced VST while wearing a soft robotic hip exosuit, which applied hip flexion perturbations at various frequencies, ranging from the preferred walking frequency to a 30% increased value. Kinematics and kinetics of the participants’ walking under the two treadmill conditions...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 16, 2024 Category: Neuroscience Source Type: research

CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Sleep abnormalities can have severe health consequences. Automated sleep staging, i.e. labelling the sequence of sleep stages from the patient’s physiological recordings, could simplify the diagnostic process. Previous work on automated sleep staging has achieved great results, mainly relying on the EEG signal. However, often multiple sources of information are available beyond EEG. This can be particularly beneficial when the EEG recordings are noisy or even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated Representation multimodal fusion network that is particularly focused on improving the robus...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 15, 2024 Category: Neuroscience Source Type: research

Gait Adaptation to Asymmetric Hip Stiffness Applied by a Robotic Exoskeleton
This study aimed to characterize how individuals adapt to bilateral asymmetric joint stiffness applied by a hip exoskeleton, similar to split-belt treadmill training. Thirteen unimpaired individuals performed a walking trial on the treadmill while wearing the exoskeleton. The right side of the exoskeleton acted as a positive stiffness torsional spring, pulling the thigh towards the neutral standing position, while the left acted as a negative stiffness spring pulling the thigh away from the neutral standing position. The results showed that this intervention applied by a hip exoskeleton elicited adaptation in spatiotempora...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 15, 2024 Category: Neuroscience Source Type: research

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke Through EMG-Driven Robot Hand Training: Insights From Dynamic Causal Modeling
This study investigated the effective connectivity (EC) between the ventral premotor cortex (PMv), supplementary motor area (SMA), and primary motor cortex (M1) using Dynamic Causal Modeling (DCM) during motor tasks with the paretic hand. Nineteen chronic stroke subjects underwent 20 sessions of EMG-driven robot hand training, and their Action Reach Arm Test (ARAT) showed significant improvement ( $\beta $ =3.56, $\text{p} (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 15, 2024 Category: Neuroscience Source Type: research

Learning to Walk With Deep Reinforcement Learning: Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model of an Osseointegrated Transfemoral Amputee
This paper leverages the OpenSim physics-based simulation environment for the forward dynamic simulation of an osseointegrated transfemoral amputee musculoskeletal model, wearing a generic prosthesis. A deep reinforcement learning architecture, which combines the proximal policy optimization algorithm with imitation learning, is designed to enable the model to walk by using three different observation states. The first is a complete state that includes the agent’s kinematics, ground reaction forces, and muscle data; the second is a reduced state that only includes the kinematics and ground reaction forces; the third is a...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 10, 2024 Category: Neuroscience Source Type: research

Exploring Inter-Brain Electroencephalogram Patterns for Social Cognitive Assessment During Jigsaw Puzzle Solving
In this study, we developed a jigsaw puzzle-solving game with hyperscanning electroencephalography (EEG) signals recorded to investigate inter-brain activities during social interactions involving cooperation and competition. Participants were recruited and paired into dyads to participate in the multiplayer jigsaw puzzle game with 32-channel EEG signals recorded. The corresponding event-related potentials (ERPs), brain oscillations, and inter-brain functional connectivity were analyzed. The results showed different ERP morphologies of P3 patterns in competitive and cooperative contexts, and brain oscillations in the low-f...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 10, 2024 Category: Neuroscience Source Type: research

Cross-Spatiotemporal Graph Convolution Networks for Skeleton-Based Parkinsonian Gait MDS-UPDRS Score Estimation
Gait impairment in Parkinson’s Disease (PD) is quantitatively assessed using the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a well-established clinical tool. Objective and efficient PD gait assessment is crucial for developing interventions to slow or halt its advancement. Skeleton-based PD gait MDS-UPDRS score estimation has attracted increasing interest in improving diagnostic efficiency and objectivity. However, previous works ignore the important cross-spacetime dependencies between joints in PD gait. Moreover, existing PD gait skeleton datasets are very small, which is a big is...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 10, 2024 Category: Neuroscience Source Type: research

M-FANet: Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding
Motor imagery (MI) decoding methods are pivotal in advancing rehabilitation and motor control research. Effective extraction of spectral-spatial-temporal features is crucial for MI decoding from limited and low signal-to-noise ratio electroencephalogram (EEG) signal samples based on brain-computer interface (BCI). In this paper, we propose a lightweight Multi-Feature Attention Neural Network (M-FANet) for feature extraction and selection of multi-feature data. M-FANet employs several unique attention modules to eliminate redundant information in the frequency domain, enhance local spatial feature extraction and calibrate f...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2024 Category: Neuroscience Source Type: research

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions
In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users’ data, and then adapting to the end-user using a small amount of new data (only ${10}...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2024 Category: Neuroscience Source Type: research

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG
Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2024 Category: Neuroscience Source Type: research

Ultrasound Deep Brain Stimulation Regulates Food Intake and Body Weight in Mice
Given the widespread occurrence of obesity, new strategies are urgently needed to prevent, halt and reverse this condition. We proposed a noninvasive neurostimulation tool, ultrasound deep brain stimulation (UDBS), which can specifically modulate the hypothalamus and effectively regulate food intake and body weight in mice. Fifteen-min UDBS of hypothalamus decreased 41.4% food intake within 2 hours. Prolonged 1-hour UDBS significantly decreased daily food intake lasting 4 days. UDBS also effectively restrained body weight gain in leptin-receptor knockout mice (Sham: 96.19%, UDBS: 58.61%). High-fat diet (HFD) mice treated w...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2024 Category: Neuroscience Source Type: research

Quantification of Hypsarrhythmia in Infantile Spasmatic EEG: A Large Cohort Study
This study aims to identify potential biomarkers for automatic IS diagnosis by quantitative analysis of the EEG signals. A large cohort of 101 IS patients and 155 healthy controls (HC) were involved. Typical hypsarrhythmia and non-hypsarrhythmia EEG signals were annotated, and normal EEG were randomly picked from the HC. Root mean square (RMS), teager energy (TE), mean frequency, sample entropy (SamEn), multi-channel SamEn, multi-scale SamEn, and nonlinear correlation coefficient were computed in each sub-band of the three EEG signals, and then compared using either a one-way ANOVA or a Kruskal-Wallis test (based on their ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2024 Category: Neuroscience Source Type: research

Micro-Expression Recognition Based on Nodal Efficiency in the EEG Functional Networks
This study provides a new neuroscientific indicator for recognizing micro-expressions based on EEG signals, thereby broadening the potential applications for micro-expression recognition. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 5, 2024 Category: Neuroscience Source Type: research