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Found 10 papers, 1 papers with code
Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks
We present a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient.
EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks
Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing.
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals
Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering.
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users.
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner.
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways.
Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects
Across- and within-recording variabilities in electroencephalographic (EEG) activity is a major limitation in EEG-based brain-computer interfaces (BCIs).
Information Theoretic Feature Transformation Learning for Brain Interfaces
Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics.
Adversarial Deep Learning in EEG Biometrics
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG.
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs).
