Narcolepsy Diagnosis With Sleep Stage Features Using PSG Recordings
Narcolepsy is a sleep disorder affecting millions of people worldwide and causes serious public health problems. It is hard for doctors to correctly and objectively diagnose narcolepsy. Polysomnography (PSG) recordings, a gold standard for sleep monitoring and quality measurement, can provide abundant and objective cues for the narcolepsy diagnosis. There have been some studies on automatic narcolepsy diagnosis using PSG recordings. However, the sleep stage information, an important cue for narcolepsy diagnosis, has not been fully utilized. For example, some studies have not considered the sleep stage information to diagno...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 14, 2023 Category: Neuroscience Source Type: research

Clinical, Kinematic and Muscle Assessment of Bilateral Coordinated Upper-Limb Movements Following Cervical Spinal Cord Injury
Cervical spinal cord injury (cSCI) often results in bilateral impairment of the arms, leading to difficulties in performing daily activities. However, little is known about the neuromotor alterations that affect the ability of individuals with cSCI to perform coordinated movements with both arms. To address this issue, we developed and tested a functional assessment that integrates clinical, kinematic, and muscle activity measures, including the evaluation of bilateral arm movements. Twelve subjects with a C5-C7 spinal lesion and six unimpaired subjects underwent an evaluation that included three tests: the Manual Muscle T...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 14, 2023 Category: Neuroscience Source Type: research

Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal Models and LSTM Networks
Accurately predicting joint torque using wearable sensors is crucial for designing assist-as-needed exoskeleton controllers to assist muscle-generated torque and ensure successful task performance. In this paper, we estimated ankle dorsiflexion/plantarflexion, knee flexion/extension, hip flexion/extension, and hip abduction/adduction torques from electromyography (EMG) and kinematics during daily activities using neuromusculoskeletal (NMS) models and long short-term memory (LSTM) networks. The joint torque ground truth for model calibrating and training was obtained through inverse dynamics of captured motion data. A clust...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 14, 2023 Category: Neuroscience Source Type: research

Fully Implantable Neurostimulation System for Long-Term Behavioral Animal Study
This study proposed a fully wireless neurostimulation system that can efficiently support a long-term animal study for neuropathic pain relief. The developed system consists of an implantable stimulator, an animal cage with an external charging coil, and a wireless communication interface. The proposed device has the feature of remotely controlling stimulation parameters via radio-frequency (RF) communication and wirelessly charging via magnetic induction in freely moving rats. Users can program stimulation parameters such as pulse width, intensity, and duration through an interface on a computer. The stimulator was packag...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 14, 2023 Category: Neuroscience Source Type: research

An Adaptive Spatial Filtering Method for Multi-Channel EMG Artifact Removal During Functional Electrical Stimulation With Time-Variant Parameters
This study provides a new and accessible approach to resolving the problem of removing FES-evoked stimulation artifacts. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 13, 2023 Category: Neuroscience Source Type: research

Non-Linearity in Motor Unit Velocity Twitch Dynamics: Implications for Ultrafast Ultrasound Source Separation
Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity t...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 13, 2023 Category: Neuroscience Source Type: research

Gaze and Environmental Context-Guided Deep Neural Network and Sequential Decision Fusion for Grasp Intention Recognition
This study is expected to inspire additional gaze-related grasp intention recognition works. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 13, 2023 Category: Neuroscience Source Type: research

Pattern Matching for Real-Time Extraction of Fast and Slow Spectral Components From sEMG Signals
Previous studies have demonstrated the potential of surface electromyography (sEMG) spectral decomposition in evaluating muscle performance, motor learning, and early diagnosis of muscle conditions. However, decomposition techniques require large data sets and are computationally demanding, making their implementation in real-life scenarios challenging. Based on the hypothesis that spectral components will present low inter-subject variability, the present paper proposes the foundational principles for developing a real-time system for their extraction by utilizing a pre-defined library of components derived from an extens...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 12, 2023 Category: Neuroscience Source Type: research

User Identity Protection in EEG-Based Brain–Computer Interfaces
A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be asso...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 12, 2023 Category: Neuroscience Source Type: research

Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation
Electroencephalography-to-Text generation (EEG-to-Text), which aims to directly generate natural text from EEG signals has drawn increasing attention in recent years due to the enormous potential for Brain-computer interfaces. However, the remarkable discrepancy between the subject-dependent EEG representation and the semantic-dependent text representation poses a great challenge to this task. To mitigate this, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C- SCL), which effectively recalibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thereby reducing th...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 12, 2023 Category: Neuroscience Source Type: research

A Real-Time Non-Implantation Bi-Directional Brain–Computer Interface Solution Without Stimulation Artifacts
The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both “reading” neural signals and “writing” stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between sti...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 11, 2023 Category: Neuroscience Source Type: research

Cross Domain Correlation Maximization for Enhancing the Target Recognition of SSVEP-Based Brain–Computer Interfaces
This study proposes a transfer-related component analysis (TransRCA) method for addressing the above issue. In this method, the SSVEP-related components are extracted from a small number of training data of the current individual and combined with those extracted from a large number of existing training data of other individuals. The TransRCA method maximizes not only the inter-trial covariances between the source and target subjects, but also the correlation between the reference signals and SSVEP signals from the source and target subjects. The proposed method was validated on the SSVEP public Benchmark and BETA datasets...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 11, 2023 Category: Neuroscience Source Type: research

Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling
Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 11, 2023 Category: Neuroscience Source Type: research

An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson’s Disease
This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor locate...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 11, 2023 Category: Neuroscience Source Type: research

Ankle-Foot-Orthosis “Hermes” Compensates Pathological Ankle Stiffness of Chronic Stroke—A Proof of Concept
Individuals with an upper motor neuron syndrome, e.g., stroke survivors, may have a pathological increase of passive ankle stiffness due to spasticity, that impairs ankle function and activities such as walking. To improve mobility, walking aids such as ankle-foot orthoses and orthopaedic shoes are prescribed. However, these walking aids generally limit the range of motion (ROM) of the foot and may therewith negatively influence activities that require a larger ROM. Here we present a new ankle-foot orthosis “Hermes”, and its first experimental results from four hemiparetic chronic stroke patients. Hermes was ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 7, 2023 Category: Neuroscience Source Type: research