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CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training
Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient’s neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 28, 2021 Category: Neuroscience Source Type: research

A Robotic Platform to Assess, Guide and Perturb Rat Forelimb Movements
Animal models are widely used to explore the mechanisms underlying sensorimotor control and learning. However, current experimental paradigms allow only limited control over task difficulty and cannot provide detailed information on forelimb kinematics and dynamics. Here we propose a novel robotic device for use in motor learning investigations with rats. The compact, highly transparent, three degree-of-freedom manipulandum is capable of rendering nominal forces of 2 N to guide or perturb rat forelimb movements, while providing objective and quantitative assessments of endpoint motor performance in a $50times 30~{hbox{mm}}...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 1, 2013 Category: Neuroscience Source Type: research

EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation
Advances in brain–computer interface (BCI) technology have facilitated the detection of Motor Imagery (MI) from electroencephalography (EEG). First, we present three strategies of using BCI to detect MI from EEG: operant conditioning that employed a fixed model, machine learning that employed a subject-specific model computed from calibration, and adaptive strategy that continuously compute the subject-specific model. Second, we review prevailing works that employed the operant conditioning and machine learning strategies. Third, we present our past work on six stroke patients who underwent a BCI rehabilitation clin...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - April 1, 2017 Category: Neuroscience Source Type: research

On the Adaptation of Pelvic Motion by Applying 3-dimensional Guidance Forces Using TPAD
Pelvic movement is important to human locomotion as the center of mass is located near the center of pelvis. Lateral pelvic motion plays a crucial role to shift the center of mass on the stance leg, while swinging the other leg and keeping the body balanced. In addition, vertical pelvic movement helps to reduce metabolic energy expenditure by exchanging potential and kinetic energy during the gait cycle. However, patient groups with cerebral palsy or stroke have excessive pelvic motion that leads to high energy expenditure. In addition, they have higher chances of falls as the center ofmass could deviate outside the base o...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 1, 2017 Category: Neuroscience Source Type: research

An Automated Classification of Pathological Gait Using Unobtrusive Sensing Technology
This paper integrates an unobtrusive and affordable sensing technology with machine learning methods to discriminate between healthy and pathological gait patterns as a result of stroke or acquired brain injury. A feature analysis is used to identify the role of each body part in separating pathological patterns from healthy patterns. Gait features, including the orientations of the hips and spine (trunk), shoulders and neck (upper limb), knees and ankles (lower limb), are calculated during walking based on Kinect skeletal tracking sequences. Sequences of these features during three types of walking conditions were examine...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2017 Category: Neuroscience Source Type: research

Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation
This paper proposes a method to enable the use of non-intrusive, small, wearable, and wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - February 1, 2018 Category: Neuroscience Source Type: research

Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters
Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory compon...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - February 28, 2019 Category: Neuroscience Source Type: research

Hierarchical Bayesian Optimization of Spatiotemporal Neurostimulations for Targeted Motor Outputs
The development of neurostimulation techniques to evoke motor patterns is an active area of research. It serves as a crucial experimental tool to probe computation in neural circuits, and has applications in neuroprostheses used to aid recovery of motor function after stroke or injury to the nervous system. There are two important challenges when designing algorithms to unveil and control neurostimulation-to-motor correspondences, thereby linking spatiotemporal patterns of neural stimulation to muscle activation: (1) the exploration of motor maps needs to be fast and efficient (exhaustive search is to be avoided for clinic...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - May 31, 2020 Category: Neuroscience Source Type: research

Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises
Health professionals often prescribe patients to perform specific exercises for rehabilitation of several diseases (e.g., stroke, Parkinson, backpain). When patients perform those exercises in the absence of an expert (e.g., physicians/therapists), they cannot assess the correctness of the performance. Automatic assessment of physical rehabilitation exercises aims to assign a quality score given an RGBD video of the body movement as input. Recent deep learning approaches address this problem by extracting CNN features from co-ordinate grids of skeleton data (body-joints) obtained from videos. However, they could not extrac...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - February 25, 2022 Category: Neuroscience Source Type: research

AI Empowered Virtual Reality Integrated Systems for Sleep Stage Classification and Quality Enhancement
Insomnia is a common public health problem and an open biomedical research topic. Insomnia results in various health problems, including memory decline, decreases concentration and weakens problem-solving ability. The insufficient sleep also leads to skin ageing, heart disease, high blood pressure, arrhythmia and stroke. While it remains as a global health concern, sleep quality improvement using modern technologies, such as machine learning, classification technologies, virtual reality (VR), becomes an open and hot research problem. These modern technologies offer new curing solutions under certain conditions. In this pap...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - June 14, 2022 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

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

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