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Found 6 papers, 1 papers with code
Bayesian Approach for Adaptive EMG Pattern Classification Via Semi-Supervised Sequential Learning
Intuitive human-machine interfaces may be developed using pattern classification to estimate executed human motions from electromyogram (EMG) signals generated during muscle contraction.
Evaluating Classifier Confidence for Surface EMG Pattern Recognition
By contrast, a scale mixture model-based classifier, which is a generative classifier that can account for uncertainty in EMG variance, exhibits superior performance in terms of both accuracy and confidence.
Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network
In this study, we propose an automated GMs classification method, which consists of preprocessing networks that remove unnecessary background information from GMs videos and adjust the infant's body position, and a subsequent motion classification network based on a two-stream structure.
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection
The covariance matrix of the Gaussian distribution is weighted with a latent scale parameter, which is also a random variable, resulting in the stochastic fluctuations of covariances.
EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models
These results indicated the validity of the proposed method and its applicability to EMG-based control systems.
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures
Experiments using simulated and real EEG data demonstrated the validity of the model and its applicability to epileptic seizure detection.
