Frequency-Dependent Microstate Characteristics for Mild Cognitive Impairment in Parkinson’s Disease
Cognitive impairment is typically reflected in the time and frequency variations of electroencephalography (EEG). Integrating time-domain and frequency-domain analysis methods is essential to better understand and assess cognitive ability. Timely identification of cognitive levels in early Parkinson’s disease (ePD) patients can help mitigate the risk of future dementia. For the investigation of the brain activity and states related to cognitive levels, this study recruited forty ePD patients for EEG microstate analysis, including 13 with mild cognitive impairment (MCI) and 27 without MCI (control group). To determine the...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 27, 2023 Category: Neuroscience Source Type: research

Electroencephalogram-Driven Machine-Learning Scenario for Assessing Impulse Control Disorder Comorbidity in Parkinson’s Disease Using a Low-Cost, Custom LEGO-Like Headset
This study proposes an electroencephalogram (EEG)-driven machine-learning scenario to automatically assess ICD comorbidity in PD. We employed a classic Go/NoGo task to appraise the capacity of cognitive and motoric inhibition with a low-cost, custom LEGO-like headset to record task-relevant EEG activity. Further, we optimized a support vector machine (SVM) and support vector regression (SVR) pipeline to learn discriminative EEG spectral signatures for the detection of ICD comorbidity and the estimation of ICD severity, respectively. With a dataset of 21 subjects with typical PD, 9 subjects with PD and ICD comorbidity (ICD)...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 27, 2023 Category: Neuroscience Source Type: research

Exploring Spatio-Spectral Electroencephalogram Modulations of Imbuing Emotional Intent During Active Piano Playing
This study attempts to exploit reproducible spatio-spectral electroencephalogram (EEG) oscillations of emotional intent using a data-driven independent component analysis framework in an ecological multiday piano playing experiment. Through the four-day 32-ch EEG dataset of 10 professional players, we showed that EEG patterns were substantially affected by both intra- and inter-individual variability underlying the emotional intent of the dichotomized valence (positive vs. negative) and arousal (high vs. low) categories. Less than half (3–4) of the 10 participants analogously exhibited day-reproducible ( $\ge $ three day...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 26, 2023 Category: Neuroscience Source Type: research

Neural Correlation of EEG and Eye Movement in Natural Grasping Intention Estimation
Decoding the user’s natural grasp intent enhances the application of wearable robots, improving the daily lives of individuals with disabilities. Electroencephalogram (EEG) and eye movements are two natural representations when users generate grasp intent in their minds, with current studies decoding human intent by fusing EEG and eye movement signals. However, the neural correlation between these two signals remains unclear. Thus, this paper aims to explore the consistency between EEG and eye movement in natural grasping intention estimation. Specifically, six grasp intent pairs are decoded by combining feature vectors ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 26, 2023 Category: Neuroscience Source Type: research

A Locomotion Mode Recognition Algorithm Using Adaptive Dynamic Movement Primitives
Control systems of robotic prostheses should be designed to decode the users’ intent to start, stop, or change locomotion; and to select the suitable control strategy, accordingly. This paper describes a locomotion mode recognition algorithm based on adaptive Dynamic Movement Primitive models used as locomotion templates. The models take foot-ground contact information and thigh roll angle, measured by an inertial measurement unit, for generating continuous model variables to extract features for a set of Support Vector Machines. The proposed algorithm was tested offline on data acquired from 10 intact subjects and 1 sub...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 26, 2023 Category: Neuroscience Source Type: research

Enhanced Motor Imagery Decoding by Calibration Model-Assisted With Tactile ERD
Conclusion. Indeed, the SA-MI Calibration could significantly improve the performance and reduce the calibration time as compared with the Conventional Calibration. Significance. The proposed tactile stimulation-assisted MI Calibration method holds great potential for a faster and more accurate system setup at the beginning of BCI usage. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 26, 2023 Category: Neuroscience Source Type: research

Development of an Aerogel-Based Wet Electrode for Functional Electrical Stimulation
Functional electrical stimulation (FES) has been a useful therapeutic tool in rehabilitation, particularly for people with paralysis. To deliver stimulation in its most basic setup, a stimulator and at least a pair of electrodes are needed. The electrodes are an essential part of the system since they allow the transduction of the stimulator signals into the body. Their performance can influence the experience of both patient and therapist in terms of movement generation, comfort, and ease of use. For non-invasive surface stimulation, current electrode options have several limitations involving their interfacing with the s...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 24, 2023 Category: Neuroscience Source Type: research

A Novel Sleep Staging Method Based on EEG and ECG Multimodal Features Combination
In this study, we propose a generalized EEG and ECG multimodal feature combination to classify sleep stages with high efficiency and accuracy. Briefly, a hybrid features combination in terms of multiscale entropy and intrinsic mode function are used to reflect nonlinear dynamics in multichannel EEGs, along with heart rate variability measures over time/frequency domains, and sample entropy across scales are applied for ECGs. For both the max-relevance and min-redundancy method and principal component analysis were used for dimensionality reduction. The selected features were classified by four traditional machine learning ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 24, 2023 Category: Neuroscience Source Type: research

A Data-Driven and Personalized Stance Symmetry Controller for Robotic Ankle-Foot Prostheses: A Preliminary Investigation
People with unilateral transtibial amputation generally exhibit asymmetric gait, likely due to inadequate prosthetic ankle function. This results in compensatory behavior, leading to long-term musculoskeletal impairments (e.g., osteoarthritis in the joints of the intact limb). Powered prostheses can better emulate biological ankles, however, control methods are over-reliant on non-disabled data, require extensive amounts of tuning by experts, and cannot adapt to each user’s unique gait patterns. This work directly addresses all these limitations with a personalized and data-driven control strategy. Our controller uses a ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 24, 2023 Category: Neuroscience Source Type: research

Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease Using EEG Data
Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer’s disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 20, 2023 Category: Neuroscience Source Type: research

Power Budget of a Skull Unit in a Fully-Implantable Brain-Computer Interface: Bio-Heat Model
The aim of this study is to estimate the maximum power consumption that guarantees the thermal safety of a skull unit (SU). The SU is part of a fully-implantable bi-directional brain computer-interface (BD-BCI) system that aims to restore walking and leg sensation to those with spinal cord injury (SCI). To estimate the SU power budget, we created a bio-heat model using the finite element method (FEM) implemented in COMSOL. To ensure that our predictions were robust against the natural variation of the model’s parameters, we also performed a sensitivity analysis. Based on our simulations, we estimated that the SU can nomi...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 19, 2023 Category: Neuroscience Source Type: research

A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI
Conclusion: The findings of this review suggest that a smart, portable, wearable-based gait and balance assessment system can be developed using multimodal sensing of the most cutting-edge, clinically relevant tools and technology available. The information presented in this article may serve as a vital springboard for such development. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 17, 2023 Category: Neuroscience Source Type: research

BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems
We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 17, 2023 Category: Neuroscience Source Type: research

Front-End Replication Dynamic Window (FRDW) for Online Motor Imagery Classification
Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed; front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification exp...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 13, 2023 Category: Neuroscience Source Type: research

A Learning-Free Method for Locomotion Mode Prediction by Terrain Reconstruction and Visual-Inertial Odometry
This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 13, 2023 Category: Neuroscience Source Type: research