IEEE Journal of Translational Engineering in Health and Medicine

Articles
LoCoMo-Net: A Low-Complex Deep Learning Framework for sEMG Based Hand Movement Recognition for Prosthetic Control
Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named ‘Low-Complex Movement recognition-Net’ (LoCoMo-Net)... Read more
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From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
         There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are... Read more
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Validation of Polymer-Based Screen-Printed Textile Electrodes for Surface EMG Detection
      In recent years, the variety of textile electrodes developed for electrophysiological signal detection has increased rapidly. Among the applications that could benefit from this advancement, those based on surface electromyography (sEMG) are particularly relevant in rehabilitation, training, and muscle function... Read more
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A Carbon Slurry Separated Interface Nerve Electrode for Electrical Block of Nerve Conduction
      Direct current (DC) nerve block has been shown to provide a complete block of nerve conduction without unwanted neural firing. Previous work shows that high capacitance electrodes can be used to safely deliver a DC block. Another way of delivering... Read more
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Wearable Devices for Precision Medicine and Health State Monitoring
   A key requirement for precision medicine is the ability to define clinically-relevant subgroups that enable improved patient management as well as the elucidation of disease mechanisms. The ability to define subgroups requires measurements with high information content and with a... Read more
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Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
        In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyographybased gesture recognition, deep learning algorithms are seldom employed as... Read more
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Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography
       Brain–machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could... Read more
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Cortico-Muscular Coherence Modulated by High-Definition Transcranial Direct Current Stimulation in People With Chronic Stroke
    High-definition transcranial direct current stimulation (HD-tDCS) is a potential neuromodulation apparatus for stroke rehabilitation. However, its modulatory effects in stroke subjects is still not well understood. In this paper, the offline modulatory effects of HD-tDCS on the ipsilesional primary motor... Read more
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Graph Theory Analysis of Functional Connectivity in Major Depression Disorder with High-Density Resting State EEG data
        Existing studies have shown functional brain networks in patients with major depressive disorder (MDD) have abnormal network topology structure. But the methods to construct brain network still exist some issues to be solved. This study is to explore reliable and... Read more
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Unimanual vs Bimanual Motor Imagery Classifiers for Assistive and Rehabilitative Brain Computer Interfaces
   Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time.... Read more