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.

OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation
In conclusion, the results demonstrate the ability of OPM-MEG in measuring phase pattern and functional connectivity on source-level, and may also prove beneficial for the study on the mechanism of rhythmic stimulation therapy for rehabilitation. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 26, 2024 Category: Neuroscience Source Type: research

Assessment of Sensorized Insoles in Balance and Gait in Individuals With Parkinson’s Disease
In this study, we verified the capability of a pair of sensorized shoes (including pressure-sensitive insoles and one inertial measurement unit) in assessing ground-level walking and body weight shift exercises. The shoes can potentially be combined with a sensory biofeedback module that provides vibrotactile cues to individuals. Sensorized shoes have been assessed in terms of the capability of detecting relevant gait events (heel strike, flat foot, toe off), estimating spatiotemporal parameters of gait (stance, swing, and double support duration, stride length), estimating gait variables (vertical ground-reaction force, v...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 25, 2024 Category: Neuroscience Source Type: research

TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding
The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifo...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 25, 2024 Category: Neuroscience Source Type: research

Identification of Neural and Non-Neural Origins of Joint Hyper-Resistance Based on a Novel Neuromechanical Model
Joint hyper-resistance is a common symptom in neurological disorders. It has both neural and non-neural origins, but it has been challenging to distinguish different origins based on clinical tests alone. Combining instrumented tests with parameter identification based on a neuromechanical model may allow us to dissociate the different origins of joint hyper-resistance in individual patients. However, this requires that the model captures the underlying mechanisms. Here, we propose a neuromechanical model that, in contrast to previously proposed models, accounts for muscle short-range stiffness (SRS) and its interaction wi...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 25, 2024 Category: Neuroscience Source Type: research

Tracking the Immediate and Short-Term Effects of Continuous Theta Burst Stimulation on Dynamic Brain States
Continuous Theta Burst Stimulation (cTBS) has been shown to modulate cortical oscillations and induce cortical inhibitory effects. Electroencephalography (EEG) studies have shown some immediate effects of cTBS on brain activity. To investigate both immediate effects and short-term effects of cTBS on dynamic brain changes, cTBS was applied to 22 healthy participants over their left motor cortex. We recorded eyes-open, resting-state EEG and performance in the Nine-Hole Peg Test (NHPT) before cTBS, immediately after cTBS, and 80 minutes after cTBS. We identified nine states using a Hidden Markov Model (HMM)-based approach to ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 25, 2024 Category: Neuroscience Source Type: research

Dynamics of Center of Pressure Trajectory in Gait: Unilateral Transfemoral Amputees Versus Non-Disabled Individuals
In conclusion, this study provides valuable insights into the unique gait dynamics and balance strategies of uTFA patients, highlighting the importance of optimizing prosthetic design, alignment procedures, and rehabilitation programs to enhance gait patterns and reduce the risk of injuries due to compensatory movements. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 22, 2024 Category: Neuroscience Source Type: research

Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy. Two 16-target SSVEP-BCIs were employed, one using low-frequency and the other using high-freq...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 22, 2024 Category: Neuroscience Source Type: research

MRI Compatible Lumbopelvic Movement Measurement System to Validate and Capture Task Performance During Neuroimaging
This study examined the concurrent validity of air pressure changes between the digital and analog sensors. The digital and analog data were compared in 23 participants during the performance of modified bilateral and unilateral right and left hip bridges. Spearman’s correlations were calculated for each sensor during the three bridging tasks and showed high positive correlations, indicating that over 87% of pressure change from the analog gauge can be explained by the pressure from the digital sensor. Bland-Altman plots showed no bias and mean differences were under three mmHg. This pressure system improves the rigor of...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 21, 2024 Category: Neuroscience Source Type: research

Performance of the Action Observation-Based Brain–Computer Interface in Stroke Patients and Gaze Metrics Analysis
Brain-computer interfaces (BCIs) are anticipated to improve the efficacy of rehabilitation for people with motor disabilities. However, applying BCI in clinical practice is still a challenge due to the great diversity of patients. In the current study, a novel action observation (AO) based BCI was proposed and tested on stroke patients. Ten non-hemineglect patients and ten hemineglect patients were recruited. Four AO stimuli were designed, each presenting a decomposed action to complete the reach-and-grasp task. EEG data and eye movement data were collected. Eye movement data was utilized to analyze the reasons for individ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 21, 2024 Category: Neuroscience Source Type: research

Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study
In this study, we proposed a few-shot learning framework that integrates multi-modal MRI data based on contrastive learning. In the upstream task, it is designed to extract knowledge from heterogeneous data. Subsequently, the downstream task is dedicated to transferring the acquired knowledge to the target dataset, where a hierarchical fusion paradigm is also designed to integrate features across inter- and intra-modalities. Lastly, the proposed model was evaluated on a set of multi-modal clinical data, achieving average scores of 73.52% and 73.09% for accuracy and AUC, respectively. Our findings also reveal that the brain...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 21, 2024 Category: Neuroscience Source Type: research

Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation
In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 21, 2024 Category: Neuroscience Source Type: research

Improving Walking Energy Efficiency in Transtibial Amputees Through the Integration of a Low-Power Actuator in an ESAR Foot
Reducing energy consumption during walking is a critical goal for transtibial amputees. The study presents the evaluation of a semi-active prosthesis with five transtibial amputees. The prosthesis has a low-power actuator integrated in parallel into an energy-storing-and-releasing foot. The actuator is controlled to compress the foot during the stance phase, supplementing the natural compression due to the user’s dynamic interaction with the ground, particularly during the ankle dorsiflexion phase, and to release the energy stored in the foot during the push-off phase, to enhance propulsion. The control strategy is adapt...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 20, 2024 Category: Neuroscience Source Type: research

Robust Decoding of Upper-Limb Movement Direction Under Cognitive Distraction With Invariant Patterns in Embedding Manifold
In this study, we propose a novel decoding model with invariant patterns in embedding manifold on a mixed dataset pooled from electroencephalograph (EEG) signals under different attentional states. We reconstruct an embedding low-dimensional manifold that intrinsically characterizes movements of the upper limb and transfer patterns of neural activities decomposed from brain functional connectivity (FC) to the manifold subspace to further preserve movement-related information. Experimental results showed that the proposed decoding model had higher robustness on the mixed dataset of attentive and distracted states compared t...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 20, 2024 Category: Neuroscience Source Type: research

M2M-InvNet: Human Motor Cortex Mapping From Multi-Muscle Response Using TMS and Generative 3D Convolutional Network
We present and evaluate five different inverse imaging CNN architectures, both conventional and generative, in terms of several measures of reconstruction accuracy. We found that one architecture, which we propose as M2M-InvNet, consistently achieved the best performance. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 19, 2024 Category: Neuroscience Source Type: research

A Review of Intelligent Walking Support Robots: Aiding Sit-to-Stand Transition and Walking
In conclusion, it summarizes the key findings, current challenges and discusses potential future research directions in this field. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 19, 2024 Category: Neuroscience Source Type: research