Electromyography
PulseAugur coverage of Electromyography — every cluster mentioning Electromyography across labs, papers, and developer communities, ranked by signal.
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AEMG framework enables generalizable action representations from EMG signals
Researchers have developed Any Electromyography (AEMG), a novel self-supervised representation learning framework designed to improve the generalization of electromyography (EMG) signals across different subjects, devic…
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AI decodes driver behavior and auditory signals using advanced machine learning
Researchers have developed a new framework for classifying driver behavior using a combination of physiological signals like EEG, EMG, and GSR. The system employs SHAP-based feature selection to identify the most predic…
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NAPS model fuses heterogeneous physiological signals using attention for sleep staging
Researchers have developed NAPS, a novel neural module designed to fuse heterogeneous physiological signals for more robust machine learning representations. This module employs a tri-axial attention mechanism and dimen…
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AI learns muscle-driven control for realistic piano playing
Researchers have developed a novel data-driven method for controlling physics-based, muscle-driven hands to play piano with remarkable dexterity. Their hierarchical approach combines high-frequency muscle control with l…
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BandRouteNet neural network offers adaptive EEG artifact removal
Researchers have developed BandRouteNet, a novel neural network designed to remove artifacts from electroencephalography (EEG) signals. This adaptive, frequency-aware model processes EEG data in specific frequency bands…
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Researchers develop new AI model for decoding high-dimensional finger motion from EMG signals
Researchers have developed a new framework for decoding high-dimensional finger motion from electromyography (EMG) signals using consumer-grade hardware. This system combines an EMG armband and a webcam to collect a new…