IEE Transactions on Neural Systems and Rehabilitation Engineering IEE Transactions on Neural Systems and Rehabilitation Engineering RSS feedThis 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.

An Upper Limb Exoskeleton Motion Generation Algorithm Based on Separating Shoulder and Arm Motion
Many rehabilitation exoskeletons have been used in the field of stroke rehabilitation. Generating human-like motion is necessary for exoskeletons to help patients perform activities of daily living (ADL) while maintaining interaction quality and ergonomics. However, most of the current motion generation algorithms utilize inverse kinematics (IK) to solve the final configuration before generation, and do not consider the movement of shoulder girdle. Separately considering the shoulder girdle motion and arm motion, this paper proposes an algorithm integrated IK to generate human-like motion. The arm moves towards the target ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination
Gait coordination (GC), meaning that one leg moves in the same pattern but with a specific phase lag to the other, is a spontaneous behavior in the walking of a healthy person. It is also crucial for unilateral amputees with the robotic leg prosthesis to perform ambulation cooperatively in the real world. However, achieving the GC for amputees poses significant challenges to the prostheses’ dynamic modeling and control design. Still, there has not been a clear survey on the initiation and evolution of the detailed solutions, hindering the precise decision of future explorations. To this end, this paper comprehensively re...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research

Multi-Scale FC-Based Multi-Order GCN: A Novel Model for Predicting Individual Behavior From fMRI
Predicting individual behavior from brain imaging data using machine learning is a rapidly growing field in neuroscience. Functional connectivity (FC), which captures interactions between different brain regions, contains valuable information about the organization of the brain and is considered a crucial feature for modeling human behavior. Graph convolutional networks (GCN) have proven to be a powerful tool for extracting graph structure features and have shown promising results in various FC-based classification tasks, such as disease classification and prognosis prediction. Despite this success, few behavior prediction...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research

Motor Imagery Classification for Asynchronous EEG-Based Brain–Computer Interfaces
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user’s MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, wh...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 22, 2024 Category: Neuroscience Source Type: research

In Vivo Transcranial Acoustoelectric Brain Imaging of Different Deep Brain Stimulation Currents
Deep brain stimulation (DBS) is an effective treatment for neurologic disease and its clinical effect is highly dependent on the DBS leads localization and current stimulating state. However, standard human brain imaging modalities could not provide direct feedback on DBS currents spatial distribution and dynamic changes. Acoustoelectric brain imaging (AEBI) is an emerging neuroimaging method that can directly map current density distribution. Here, we investigate in vivo AEBI of different DBS currents to explore the potential of DBS visualization using AEBI. According to the typical DBS stimulus parameters, four types of ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 19, 2024 Category: Neuroscience Source Type: research

Resting State EEG Variability and Implications for Interpreting Clinical Effect Sizes
Resting state electroencephalography (rsEEG) is widely used to investigate intrinsic brain activity, with the potential for detecting neurophysiological abnormalities in clinical conditions from neurodegenerative disease to developmental disorders. When interpreting quantitative rsEEG changes, a key question is: how much deviation from a healthy normal brain state indicates a clinically significant change? Here, we build on the existing rsEEG variability literature by quantifying how this baseline rsEEG range can be attributed to common but underinvestigated sources of variability: experiment day, time of day, and pre-reco...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 19, 2024 Category: Neuroscience Source Type: research

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint
The functional architecture undergoes alterations during the preclinical phase of Alzheimer’s disease. Consequently, the primary research focus has shifted towards identifying Alzheimer’s disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer’s Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functiona...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 18, 2024 Category: Neuroscience Source Type: research

Force Control Issues in Upper and Lower Limbs in Parkinson’s Disease and Freezing of Gait
Parkinson’s Disease (PD) has been found to cause force control deficits in upper and lower limbs. About 50% of patients with advanced PD develop a debilitating symptom called freezing of gait (FOG), which has been linked to force control problems in the lower limbs, and some may only have a limited response to the gold standard pharmaceutical therapy, levodopa, resulting in partially levodopa-responsive FOG (PLR-FOG). There has been limited research on investigating upper-limb force control in people with PD with PLR-FOG, and without FOG. In this pilot study, force control was explored using an upper-and-lower-limb hapti...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 18, 2024 Category: Neuroscience Source Type: research

Graph Neural Network-Based EEG Classification: A Survey
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify stan...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 18, 2024 Category: Neuroscience Source Type: research

Toward Domain-Free Transformer for Generalized EEG Pre-Training
Electroencephalography (EEG) signals are the brain signals acquired using the non-invasive approach. Owing to the high portability and practicality, EEG signals have found extensive application in monitoring human physiological states across various domains. In recent years, deep learning methodologies have been explored to decode the intricate information embedded in EEG signals. However, since EEG signals are acquired from humans, it has issues with acquiring enormous amounts of data for training the deep learning models. Therefore, previous research has attempted to develop pre-trained models that could show significant...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 18, 2024 Category: Neuroscience Source Type: research

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance
Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-d...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 18, 2024 Category: Neuroscience Source Type: research

Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis
The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 17, 2024 Category: Neuroscience Source Type: research

Robot-Assisted Training to Improve Proprioception of Wrist
In recent years, robot-assisted training has been shown to significantly improve motor function and proprioception in people with functional disabilities, but the efficiency of proprioceptive acuity was unclear. To characterize the efficiency of joint proprioceptive acuity improvement in space, we designed a robot-assisted ipsilateral joint position matching experiment using the wrist as the study object. We conducted 2-way repeated measures ANOVA on error data before and after training in 12 healthy subjects and mapped the distribution of wrist proprioceptive learning ability in different workspaces. The results showed si...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 17, 2024 Category: Neuroscience Source Type: research

Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface
The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( $8\times8$ ) nerve cuff electrode was implanted on each rat’s sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 17, 2024 Category: Neuroscience Source Type: research

Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models
The objective of this study is to build an unobtrusive and accurate system to monitor OEP for older adults. Data was collected from 18 older adults wearing a single waist-mounted Inertial Measurement Unit (IMU). Two datasets were recorded, one in a laboratory setting, and one at the homes of the patients. A hierarchical system is proposed with two stages: 1) using a deep learning model to recognize whether the patients are performing OEP or activities of daily life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a 6-second sliding window to recognize the OEP sub-classes. Results showed that in stage 1, ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 17, 2024 Category: Neuroscience Source Type: research