Effectiveness of virtual reality-and gaming-based interventions for upper extremity rehabilitation poststroke: a meta-analysis
Objective To investigate the efficacy of virtual reality (VR)-and gaming-based interventions
for improving upper extremity function poststroke, and to examine demographic and …
for improving upper extremity function poststroke, and to examine demographic and …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
AutoEncoder filter bank common spatial patterns to decode motor imagery from EEG
The present paper introduces a novel method, named AutoEncoder-Filter Bank Common
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
Deep convolution generative adversarial network-based electroencephalogram data augmentation for post-stroke rehabilitation with motor imagery
The motor imagery brain–computer interface (MI-BCI) system is currently one of the most
advanced rehabilitation technologies, and it can be used to restore the motor function of …
advanced rehabilitation technologies, and it can be used to restore the motor function of …
Interhemispheric structural connectivity underlies motor recovery after stroke
Objective Although ample evidence highlights that the ipsilesional corticospinal tract (CST)
plays a crucial role in motor recovery after stroke, studies on cortico‐cortical motor …
plays a crucial role in motor recovery after stroke, studies on cortico‐cortical motor …
Self-Supervised Learning for Near-Wild Cognitive Workload Estimation
Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-
based models can produce feedback from physiological data such as …
based models can produce feedback from physiological data such as …
Predicting outcome in patients with brain injury: differences between machine learning versus conventional statistics
Defining reliable tools for early prediction of outcome is the main target for physicians to
guide care decisions in patients with brain injury. The application of machine learning (ML) …
guide care decisions in patients with brain injury. The application of machine learning (ML) …
Augmented efficacy of intermittent theta burst stimulation on the virtual reality-based cycling training for upper limb function in patients with stroke: a double-blinded …
YH Chen, CL Chen, YZ Huang, HC Chen… - Journal of …, 2021 - Springer
Background Virtual reality and arm cycling have been reported as effective treatments for
improving upper limb motor recovery in patients with stroke. Intermittent theta burst …
improving upper limb motor recovery in patients with stroke. Intermittent theta burst …
Feature selection combining filter and wrapper methods for motor-imagery based brain–computer interfaces
Motor imagery (MI) based brain–computer interfaces help patients with movement disorders
to regain the ability to control external devices. Common spatial pattern (CSP) is a popular …
to regain the ability to control external devices. Common spatial pattern (CSP) is a popular …
Intermittent theta burst stimulation enhances upper limb motor function in patients with chronic stroke: a pilot randomized controlled trial
YJ Chen, YZ Huang, CY Chen, CL Chen, HC Chen… - BMC neurology, 2019 - Springer
Background Intermittent theta burst stimulation (iTBS) is a form of repetitive transcranial
stimulation that has been used to enhance upper limb (UL) motor recovery. However, only …
stimulation that has been used to enhance upper limb (UL) motor recovery. However, only …