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Found 44 papers, 18 papers with code
Contrastive learning-based agent modeling for deep reinforcement learning
However, existing agent modeling approaches typically assume the availability of local observations from other agents (modeled agents) during training or a long observation trajectory for policy adaption.
Neuroadaptation in Physical Human-Robot Collaboration
Robots for physical Human-Robot Collaboration (pHRC) systems need to change their behavior and how they operate in consideration of several factors, such as the performance and intention of a human co-worker and the capabilities of different human-co-workers in collision avoidance and singularity of the robot operation.
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision
The proposed BELT method is a generic and efficient framework that bootstraps EEG representation learning using off-the-shelf large-scale pretrained language models (LMs).
Generalizing Multimodal Variational Methods to Sets
Making sense of multiple modalities can yield a more comprehensive description of real-world phenomena.
Deep Learning for Brain Age Estimation: A Systematic Review
In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data.
FedTP: Federated Learning by Transformer Personalization
Instead of using a vanilla personalization mechanism that maintains personalized self-attention layers of each client locally, we develop a learn-to-personalize mechanism to further encourage the cooperation among clients and to increase the scablability and generalization of FedTP.
Federated Fuzzy Neural Network with Evolutionary Rule Learning
ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data.
Cross Task Neural Architecture Search for EEG Signal Classifications
Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition.
Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data
The network is trained with a two-stage optimization algorithm, and the parameters at low levels of the hierarchy are learned with a scheme based on the well-known alternating direction method of multipliers, which does not reveal local data to other agents.
Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization
In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR.
Toward multi-target self-organizing pursuit in a partially observable Markov game
The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit.
Semantic Autoencoder and Its Potential Usage for Adversarial Attack
Autoencoder can give rise to an appropriate latent representation of the input data, however, the representation which is solely based on the intrinsic property of the input data, is usually inferior to express some semantic information.
On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive BCI
In the case of mental task classification, the availability of training samples to features are minimal.
Deep hierarchical reinforcement agents for automated penetration testing
Penetration testing the organised attack of a computer system in order to test existing defences has been used extensively to evaluate network security.
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature.
Ranked #1 on
EEG 4 classes
on BCI Competition IV 2a
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Test samples with the backdoor key will then be classified into the target class specified by the attacker.
Weak Human Preference Supervision For Deep Reinforcement Learning
The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments.
Multi-Subspace Neural Network for Image Recognition
Associating subspace with the deep network is a novel designing, providing various viewpoints of data.
EnK: Encoding time-information in convolution
There have been several efforts to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information by incorporating hand-crafted features, slicing the input data in a smaller time-windows, and recurrent convolution.
Clustering via torque balance with mass and distance
Grouping similar objects is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition.
A General Approach for Using Deep Neural Network for Digital Watermarking
Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way.
Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers
An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought.
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph.
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning
To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can be potentially generalized to other BCI cases.
Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis
In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly.
Adaptive Initialization Method for K-means Algorithm
(c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets.
White-Box Target Attack for EEG-Based BCI Regression Problems
Experiments on two BCI regression problems verified that both approaches are effective.
Protecting Privacy of Users in Brain-Computer Interface Applications
Machine learning (ML) is revolutionizing research and industry.
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes application to three Brain Computer Interface (BCI) applications.
Mental Fatigue Monitoring using Brain Dynamics Preferences
Furthermore, a transition matrix is introduced to characterize the reliability of each channel used in EEG data, which helps in learning brain dynamics preferences only from informative EEG channels.
Preference Neural Network
This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function.
On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression
Fuzzy systems have achieved great success in numerous applications.
Reinforcement Learning from Hierarchical Critics
Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learning from hierarchical critics (RLHC) algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Effects of Repetitive SSVEPs on EEG Complexity using Multiscale Inherent Fuzzy Entropy
These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Signal Processing
Extraction of SSVEPs-based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the inter-ictal phase but a reverse trend in patients in the pre-ictal phase.
Human-Computer Interaction
Active Learning for Regression Using Greedy Sampling
Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection.
Semi-supervised Feature Learning For Improving Writer Identification
In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously.
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
Ensemble learning is a powerful approach to construct a strong learner from multiple base learners.
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
There are many important regression problems in real-world brain-computer interface (BCI) applications, e. g., driver drowsiness estimation from EEG signals.
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance.
Inherent fuzzy entropy for the improvement of EEG complexity evaluation
In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals.
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR.
Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noises, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression.
