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.

Multi-Level Graph Neural Network With Sparsity Pooling for Recognizing Parkinson’s Disease
Parkinson’s disease (PD) is a neurodegenerative disease of the brain associated with motor symptoms. With the maturation of machine learning (ML), especially deep learning, ML has been used to assist in the diagnosis of PD. In this paper, we explore graph neural networks (GNNs) to implement PD prediction using MRI data. However, most existing GNN models suffer from the efficiency of graph construction on MRI data and the problem of overfitting on small data. This paper proposes a novel multi-layer GNN model that incorporates a fast graph construction method and a sparsity-based pooling layer with an attention mechanism. ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 6, 2023 Category: Neuroscience Source Type: research

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights
Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications....
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 6, 2023 Category: Neuroscience Source Type: research

Differentiating Between Alzheimer’s Disease and Frontotemporal Dementia Based on the Resting-State Multilayer EEG Network
In this study, we constructed frequency-based multilayer resting-state electroencephalogram (EEG) networks and extracted representative network features to improve the differentiation between AD and FTD. When compared with healthy controls (HC), AD showed primarily stronger delta-alpha cross-couplings and weaker theta-sigma cross-couplings. Notably, when comparing the AD and FTD groups, we found that the AD exhibited stronger delta-alpha and delta-beta connectivity than the FTD. Thereafter, by extracting the representative network features and then applying these features in the classification between AD and FTD, an accura...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 3, 2023 Category: Neuroscience Source Type: research

Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics
Neurodegenerative disease often affects speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to delineate healthy speech from speech of people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We subjec...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 3, 2023 Category: Neuroscience Source Type: research

Dynamic Multi-Graph Convolution-Based Channel-Weighted Transformer Feature Fusion Network for Epileptic Seizure Prediction
Electroencephalogram (EEG) based seizure prediction plays an important role in the closed-loop neuromodulation system. However, most existing seizure prediction methods based on graph convolution network only focused on constructing the static graph, ignoring multi-domain dynamic changes in deep graph structure. Moreover, the existing feature fusion strategies generally concatenated coarse-grained epileptic EEG features directly, leading to the suboptimal seizure prediction performance. To address these issues, we propose a novel multi-branch dynamic multi-graph convolution based channel-weighted transformer feature fusion...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 3, 2023 Category: Neuroscience Source Type: research

Learning Skill Training Schedules From Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym
A robotic gym with multiple rehabilitation robots allows multiple patients to exercise simultaneously under the supervision of a single therapist. The multi-patient training outcome can potentially be improved by dynamically assigning patients to robots based on monitored patient data. In this paper, we present an approach to learn dynamic patient-robot assignment from a domain expert via supervised learning. The dynamic assignment algorithm uses a neural network model to predict assignment priorities between patients. This neural network was trained using a synthetic dataset created in a simulated rehabilitation gym to im...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 3, 2023 Category: Neuroscience Source Type: research

The Sensitivity of Bipolar Electromyograms to Muscle Excitation Scales With the Inter-Electrode Distance
The value of surface electromyograms (EMGs) lies in their potential to non-invasively probe the neuromuscular system. Whether muscle excitation may be accurately inferred from bipolar EMGs depends on how much the detected signal is both sensitive and specific to the excitation of the target muscle. While both are known to be a function of the inter-electrode distance (IED), specificity has been of long concern in the physiological literature. In contrast, sensitivity, at best, has been implicitly assumed. Here we provide evidence that the IED imposes a biophysical constraint on the sensitivity of surface EMG. From 20 healt...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 3, 2023 Category: Neuroscience Source Type: research

EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization
This study developed a novel method for personalizing OpenSim cylindrical wrapping surfaces during EMG-driven model calibration. To avoid the high computational cost of repeated OpenSim muscle analyses, the method uses two-level polynomial surrogate models. Outer-level models specify time-varying muscle-tendon lengths and moment arms as functions of joint angles, while inner-level models specify time-invariant outer-level polynomial coefficients as functions of wrapping surface parameters. To evaluate the method, we used walking data collected from two individuals post-stroke and performed four variations of EMG-driven low...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 3, 2023 Category: Neuroscience Source Type: research

Neural Stimulation Hardware for the Selective Intrafascicular Modulation of the Vagus Nerve
The neural stimulation of the vagus nerve is able to modulate various functions of the parasympathetic response in different organs. The stimulation of the vagus nerve is a promising approach to treating inflammatory diseases, obesity, diabetes, heart failure, and hypertension. The complexity of the vagus nerve requires highly selective stimulation, allowing the modulation of target-specific organs without side effects. Here, we address this issue by adapting a neural stimulator and developing an intraneural electrode for the particular modulation of the vagus nerve. The neurostimulator parameters such as amplitude, pulse ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 2, 2023 Category: Neuroscience Source Type: research

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold
As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract sp...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 2, 2023 Category: Neuroscience Source Type: research

Age-Related Changes in Inter-Joint Interactions for Global and Local Kinematics While Standing
Inter-joint interactions are involved in human standing. These interactions work not only for global kinematics that control the center of mass (COM) of the entire body, but also for local kinematics that control joint angular movements. Age-related changes in these interactions are thought to cause unstable standing postures in older people. Interactions of global kinematics are known to be deficient owing to aging. However, it is unclear whether the interaction of local kinematics is affected by aging. We investigated the age-related changes in inter-joint interactions, especially local kinematics, during standing. Diffe...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 1, 2023 Category: Neuroscience Source Type: research

An Upper-Limb Rehabilitation Exoskeleton System Controlled by MI Recognition Model With Deep Emphasized Informative Features in a VR Scene
The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain–computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient’s motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the fre...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 1, 2023 Category: Neuroscience Source Type: research

Broader Estimates of Gastrocnemius Activity Generated a More Representative Cocontraction Index: A Study in Pediatric Population
The electromyography (EMG) cocontraction index (CCI) given by the antagonistic/agonistic Root Mean Square (RMS) amplitude ratio of the same muscle is a qualified biomarker used for spastic cocontraction quantification and management in cerebral palsy children. However, this normative EMG ratio is likely subject to a potential source of errors with biased estimates when measuring the gastrocnemius plantar flexors activity. Due to the uneven distribution of electrical activity within the muscle volume, cocontraction levels can be misestimated, if EMGs are obtained from the sole traditional bipolar sensor location recommended...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 1, 2023 Category: Neuroscience Source Type: research

Investigating the Association of Quantitative Gait Stability Metrics With User Perception of Gait Interruption Due to Control Faults During Human-Prosthesis Interaction
This study aims to compare the association of different gait stability metrics with the prosthesis users’ perception of their own gait stability. Lack of perceived confidence on the device functionality can influence the gait pattern, level of daily activities, and overall quality of life for individuals with lower limb motor deficits. However, the perception of gait stability is subjective and difficult to acquire online. The quantitative gait stability metrics can be objectively measured and monitored using wearable sensors; however, objective measurements of gait stability associated with human’s perception of their...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 31, 2023 Category: Neuroscience Source Type: research

A SSVEP-Based Brain–Computer Interface With Low-Pixel Density of Stimuli
This study aims to achieve a perfect balance between comfort and effectiveness by reducing the pixel density of SSVEP stimuli. Three experiments were conducted to determine the most suitable presentation form (flickering square vs. flickering checkerboard), pixel distribution pattern (random vs. uniform), and pixel density value (100%, 90%, 80%, 70%, 60%, 40%, 20%). Subjects’ electroencephalogram (EEG) and fatigue scores were recorded, while comfort and effectiveness were measured by fatigue score and classification accuracy, respectively. The results showed that the flickering square with random pixel distribution achie...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 31, 2023 Category: Neuroscience Source Type: research