IEEE Transactions on Biomedical Engineering

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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
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Modulating the Structure of Motor Variability for Skill Learning Through Specific Muscle Synergies in Elderlies and Young Adults
Motor variability – performance variations across task repetitions – has been assumed to be undesirable. But recent studies argue that variability facilitates early motor learning by allowing exploratory search of reward-generating motion, and that variability’s structure may be modulated by... Read more
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Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?
EEG-based brain-computer interface (BCI) has been used to detect movement intention in severely affected stroke patients during assisted therapy, but current EEG-BCI systems are not practical for routine clinical use. If detectable in this population, residual EMG in the target... Read more
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A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
     Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection... Read more
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Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations from Extreme Learning
Electromyogram (EMG) signals can be used to decode the intended movements of an amputee for control of a dexterous upper-limb prosthesis. However, normal prosthesis use involves changes in upper-limb positions that influence the EMG signals, hindering the ability of pattern... Read more
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Quantifying Nonlinear Contributions to Cortical Responses Evoked by Continuous Wrist Manipulation
Cortical responses to continuous stimuli as recorded using either magneto- or electroencephalography (EEG) have shown power at harmonics of the stimulated frequency, indicating nonlinear behavior. Even though the selection of analysis techniques depends on the linearity of the system under... Read more