Search Results for author:
Found 8 papers, 3 papers with code
MarkovType: A Markov Decision Process Strategy for Non-Invasive Brain-Computer Interfaces Typing Systems
To the best of our knowledge, this is the first work to formulate the RSVP typing task as a POMDP for recursive classification.
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.
Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules
We use Algorithmic Differentiation (AD) and Bayesian filters computed with cubature rules conjointly to quickly estimate complex fluid-structure interactions.
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver\'s policy.
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility.
