Search Results for author:
Found 8 papers, 3 papers with code
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
New subjects only demonstrate the single component gestures and we seek to extrapolate from these to all possible single or combination gestures.
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects.
User Training with Error Augmentation for Electromyogram-based Gesture Classification
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration.
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time.
Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models
We study the problem of inferring user intent from noninvasive electroencephalography (EEG) to restore communication for people with severe speech and physical impairments (SSPI).
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data
We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label.
Generator Surgery for Compressed Sensing
We introduce a method for achieving low representation error using generators as signal priors.
Fair and Useful Cohort Selection
A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, and downstream applications based on fair classifiers might not preserve fairness.
