Filtered By:
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering
This page shows you your search results in order of relevance. This is page number 19.
Order by Relevance | Date
Total 279 results found since Jan 2013.
Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network
In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 4, 2022 Category: Neuroscience Source Type: research
Intelligent Headband System for Evaluating Rehabilitation Effectiveness
Stroke is an acute cerebrovascular condition causing damage to cranial nerves and requires subsequent rehabilitation treatment. In clinical practice, the effectiveness of rehabilitation is usually subjectively assessed by experienced physicians or using global prognostic scales. Several brain imaging techniques, such as positron emission tomography, functional magnetic resonance imaging, and computed tomography angiography, can be applied in rehabilitation effectiveness evaluation, but their complexity and long measurement times limit the activity of patients during measurement. This paper proposes an intelligent headband ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - February 10, 2023 Category: Neuroscience Source Type: research
A Machine Learning-Based Initial Difficulty Level Adjustment Method for Balance Exercise on a Trunk Rehabilitation Robot
Trunk rehabilitation exercises such as those for remediating core stability can help improve the seated balance of patients with weakness or loss of proprioception caused by diseases such as stroke, and aid the recovery of other functions such as gait. However, there has not yet been any reported method for automatically determining the parameters that define exercise difficulty on a trunk rehabilitation robot (TRR) based on data such as the patient’s demographic information, balancing ability, and training sequence, etc. We have proposed a machine learning (ML)-based difficulty adjustment method to determine an app...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 31, 2023 Category: Neuroscience Source Type: research
Decoding Imagined Musical Pitch From Human Scalp Electroencephalograms
This study evaluated the feasibility of decoding pitch imagery information directly from human electroencephalography (EEG). Twenty participants performed a random imagery task with seven musical pitches (C4–B4). We used two approaches to explore EEG features of pitch imagery: multiband spectral power at individual channels (IC) and differences between bilaterally symmetric channels (DC). The selected spectral power features revealed remarkable contrasts between left and right hemispheres, low- (< 13 Hz) and high-frequency ( $>$ 13 Hz) bands, and frontal and parietal areas. We classified two EEG feature se...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - May 5, 2023 Category: Neuroscience Source Type: research
Motor Imagery and Action Observation Induced Electroencephalographic Activations to Guide Subject-Specific Training Paradigm: A Pilot Study
Brain-computer interface (BCI)-based motor rehabilitation feedback training system can facilitate motor function reconstruction, but its rehabilitation mechanism with suitable training protocol is unclear, which affects the application effect. To this end, we probed the electroencephalographic (EEG) activations induced by motor imagery (MI) and action observation (AO) to provide an effective method to optimize motor feedback training. We grouped subjects according to their alpha–band sensorimotor cortical excitability under MI and AO conditions, and investigated the EEG response under the same paradigm between group...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - May 30, 2023 Category: Neuroscience Source Type: research
Design and Performance Analysis of a Bioelectronic Controlled Hybrid Serial-Parallel Wrist Exoskeleton
Wrist exoskeletons are increasingly being used in the rehabilitation of stroke and hand dysfunction because of its ability to assist patients in high intensity, repetitive, targeted and interactive rehabilitation training. However, the existing wrist exoskeletons cannot effectively replace the work of therapist and improve hand function, mainly because the existing exoskeletons cannot assist patients to perform natural hand movement covering the entire physiological motor space (PMS). Here, we present a bioelectronic controlled hybrid serial-parallel wrist exoskeleton HrWr-ExoSkeleton (HrWE) which is based on the PMS desig...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - June 16, 2023 Category: Neuroscience Source Type: research
Lower-Limb Non-Parametric Functional Muscle Network: Test-Retest Reliability Analysis
Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-st...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - August 1, 2023 Category: Neuroscience Source Type: research
A Novel Algorithmic Structure of EEG Channel Attention Combined With Swin Transformer for Motor Patterns Classification
With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight ofte...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - August 8, 2023 Category: Neuroscience Source Type: research
A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation
It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and ri...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - August 25, 2023 Category: Neuroscience Source Type: research
