IEEE Transactions on Biomedical Engineering

Featured Articles
Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networks
Movement prediction from EMG can be performed by compressing a short window of EMG into a feature-encoding that is meaningful for classification— an approach that can cause erratic prediction behavior. Temporal convolutional networks (TCN) leverage temporal information from EMG to achieve superior predictions for 3 simultaneous degrees-of-freedom that are more accurate and stable, have a very low response delay, and allow for novel types of interactive training. Addressing EMG decoding as a sequential prediction problem requires a new set of considerations that will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems... Read more
Featured Articles
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... Read more
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Neural Decoding for Macaque’s Finger Position: Convolutional Space Model
   In this paper, we study how to use the number of spike signals in a macaque’s motor cortex to estimate the position of its finger movement. First, we analyze the time correlation of a traditional state space model (SSM) and... Read more
Articles, Published Articles
Cardiac-DeepIED: Automatic Pixel-level Deep Segmentation for Cardiac Bi-ventricle Using Improved End-to-End Encoder-Decoder Network
     Abstract Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge.... 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
Featured Articles
Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform
Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we... Read more
Featured Articles
Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME)
The selection of suitable peripheral nerve electrodes for biomedical applications implies a trade-off between invasiveness and selectivity. The optimal design should provide the highest selectivity for targeting a large number of nerve fascicles with the least invasiveness and potential damage... Read more
Featured Articles, Special Issue: BRAIN
Decoding Brain States Based on Magnetoencephalography from Pre-specified Cortical Regions
Jinyin Zhang, Xin Li, Stephen T. Foldes, Wei Wang, Jennifer L. Collinger, Douglas J. Weber, Anto Bagić, Carnegie Mellon University, & University of Pittsburgh, USA Magnetoencephalography (MEG) decoding is a critical tool for many neuroscience studies and neuroengineering applications, such as... Read more
Featured Articles
Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces
Abstract Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the... Read more