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Source: IEE Transactions on Neural Systems and Rehabilitation Engineering
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Total 279 results found since Jan 2013.
Design and Development of a Portable Exoskeleton for Hand Rehabilitation
Improvement in hand function to promote functional recovery is one of the major goals of stroke rehabilitation. This paper introduces a newly developed exoskeleton for hand rehabilitation with a user-centered design concept, which integrates the requirements of practical use, mechanical structure, and control system. The paper also evaluated the function with two prototypes in a local hospital. Results of functional evaluation showed that significant improvements were found in ARAT (P = 0.014), WMFT (P = 0.020), and FMA_WH (P = 0.021). Increase in the mean values of FMA_SE was observed bu...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2018 Category: Neuroscience Source Type: research
Comparison of Muscular Activity and Movement Performance in Robot-Assisted and Freely Performed Exercises
End-effector-based robotic systems are, in particular, suitable for extending physical therapy in stroke rehabilitation. An adequate therapy and thus the recovery of movement can only be guaranteed if the physiological muscular activation and movement performance are influenced as little as possible by the robot itself. Yet, this relation has not been investigated in the literature. Therefore, 20 healthy subjects performed free and robot-assisted exercises under different control settings supported by an end-effector-based system. The control settings differed concerning changes in the end-effector velocity and the stiffne...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2019 Category: Neuroscience Source Type: research
Gait and Dynamic Balance Sensing Using Wearable Foot Sensors
In conclusion, this paper shows that pressure sensors, minimally under the heel and toe, offer a lightweight and inconspicuous alternative for F&M sensing, toward estimating ambulatory gait and dynamic balance.
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - February 1, 2019 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
Age-Related Changes in Vibro-Tactile EEG Response and Its Implications in BCI Applications: A Comparison Between Older and Younger Populations
The rapid increase in the number of older adults around the world is accelerating research in applications to support age-related conditions, such as brain–computer interface (BCI) applications for post-stroke neurorehabilitation. The signal processing algorithms for electroencephalogram (EEG) and other physiological signals that are currently used in BCI have been developed on data from much younger populations. It is unclear how age-related changes may affect the EEG signal and therefore the use of BCI by older adults. This research investigated the EEG response to vibro-tactile stimulation from 11 younger (21.7&#...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 31, 2019 Category: Neuroscience Source Type: research
Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing
This paper presents a new approach to wearable hand gesture recognition and finger angle estimation based on the modified barometric pressure sensing. Barometric pressure sensors were encased and injected with VytaFlex rubber such that the rubber directly contacted the sensing element allowing pressure change detection when the encasing rubber was pressed. A wearable prototype consisting of an array of ten modified barometric pressure sensors around the wrist was developed and validated with experimental testing for three different hand gesture sets and finger flexion/extension trials for each of the five fingers. The over...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 31, 2019 Category: Neuroscience Source Type: research
Simulating Hemiparetic Gait in Healthy Subjects Using TPAD With a Closed-Loop Controller
Hemiparetic gait is abnormal asymmetric walking, often observed among patients with cerebral palsy or stroke. One of the major features of asymmetric gait is excessive reliance on the healthy leg, which results in improper load shift, slow walking speed, higher metabolic cost, and weakness of the unused leg. Hence, clinically it is desirable to promote gait symmetry to improve walking. While there are no clear methods to achieve this goal, we are exploring new methods where we guide the pelvis to change the gait symmetry. This controller is designed to mimic the hands of a physical therapist holding the pelvis and guiding ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - April 30, 2019 Category: Neuroscience Source Type: research
Segmentation of Exercise Repetitions Enabling Real-Time Patient Analysis and Feedback Using a Single Exemplar
We present a segmentation algorithm capable of segmenting exercise repetitions in real time. This approach uses subsequence dynamic time warping and requires only a single exemplar repetition of an exercise to correctly segment repetitions from other subjects, including those with limited mobility. This approach is invariant to low range of motion, instability in movements, and sensor noise while remaining selective to different exercises. This algorithm enables responsive feedback for technology-assisted physical rehabilitation systems. We evaluated the algorithm against a publicly available dataset (CMU) and against a he...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - April 30, 2019 Category: Neuroscience Source Type: research
Controlling the Cadence and Admittance of a Functional Electrical Stimulation Cycle
For an individual suffering from a neurological condition, such as spinal cord injury, traumatic brain injury, or stroke, motorized functional electrical stimulation (FES) cycling is a rehabilitation strategy, which offers numerous health benefits. Motorized FES cycling is an example of physical human–robot interaction in which both systems must be controlled; the human is actuated by applying neuromuscular electrical stimulation to the large leg muscle groups, and the cycle is actuated through its onboard electric motor. While the rider is stimulated using a robust sliding-mode controller, the cycle utilizes an adm...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - May 31, 2019 Category: Neuroscience Source Type: research
A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential
A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple states (sub-states) exist within the IC state. Existing recognition methods rely on a threshold technique, which is difficult to realize high accuracy, i.e., simultaneously high true positive rate and low false positive rate. To address this issue, we proposed a novel convolutional neural network (CNN) to detect IC and NC states in a SSVEP-BCI system for the first time. Specific...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - May 31, 2019 Category: Neuroscience Source Type: research
Action Observation of Own Hand Movement Enhances Event-Related Desynchronization
A stroke occurs when blood flow to the brain is critically reduced or blocked, potentially resulting in motor paralysis. One of the most promising and effective neurorehabilitation methods for strokes is a closed-loop brain–computer interface (BCI) based on the motor imagery (MI). For the design of MI-based BCI, action observation (AO) during MI facilitates the detection of a user’s motor intention. In this paper, we investigated whether or not the AO’s targeted objects (the hand of a participant or another person) affects brain activity during MI. To investigate the differences in brain activity induc...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - June 30, 2019 Category: Neuroscience Source Type: research
Hemicraniectomy in Traumatic Brain Injury: A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces
Brain–machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - June 30, 2019 Category: Neuroscience Source Type: research
Reconfiguring Motor Circuits for a Joint Manual and BCI Task
This study provides insight into the neural activity that enables a dual-control brain-computer interface.
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 31, 2019 Category: Neuroscience Source Type: research
Continuous Description of Human 3D Motion Intent Through Switching Mechanism
This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 31, 2019 Category: Neuroscience Source Type: research
Utilizing High-Density Electroencephalography and Motion Capture Technology to Characterize Sensorimotor Integration While Performing Complex Actions
Studies of sensorimotor integration often use sensory stimuli that require a simple motor response, such as a reach or a grasp. Recent advances in neural recording techniques, motion capture technologies, and time-synchronization methods enable studying sensorimotor integration using more complex sensory stimuli and performed actions. Here, we demonstrate that prehensile actions that require using complex sensory instructions for manipulating different objects can be characterized using high-density electroencephalography and motion capture systems. In 20 participants, we presented stimuli in different sensory modalities (...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 31, 2019 Category: Neuroscience Source Type: research
