270 citations · 722 across the 20 of their papers we have counts for
21 papers
A Self-Constructing Multi-Expert Fuzzy System for High-dimensional Data Classification
Yingtao Ren, Yu-Cheng Chang, Thomas Do +2
Fuzzy Neural Networks (FNNs) are effective machine learning models for classification tasks, commonly based on the Takagi-Sugeno-Kang (TSK) fuzzy system. However, when faced with h…
Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization
Ye Shi, Leijie Zhang, Zehong Cao +2
Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot sh…
Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving
Zehong Cao, Jie Yun
Autonomous driving has been at the forefront of public interest, and a pivotal debate to widespread concerns is safety in the transportation system. Deep reinforcement learning (DR…
Retrieving Event-related Human Brain Dynamics from Natural Sentence Reading
Xinping Liu, Zehong Cao
Electroencephalography (EEG) signals recordings when people reading natural languages are commonly used as a cognitive method to interpret human language understanding in neuroscie…
Identification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients with Parkinson's Disease
Zehong Cao, Alka Rachel John, Hsiang-Ting Chen +6
Mobility is severely impacted in patients with Parkinson's disease (PD), especially when they experience involuntary stopping from the freezing of gait (FOG). Understanding the neu…
CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation
Xinping Liu, Zehong Cao, Son Tran
Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources. In…
Weak Human Preference Supervision For Deep Reinforcement Learning
Zehong Cao, KaiChiu Wong, Chin-Teng Lin
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…
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification
Xianglin Zheng, Zehong Cao, Quan Bai
The new perspective in visual classification aims to decode the feature representation of visual objects from human brain activities. Recording electroencephalogram (EEG) from the…
A General Approach for Using Deep Neural Network for Digital Watermarking
Yurui Ming, Weiping Ding, Zehong Cao +1
Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way. However, consideration from the privacy or legislation pers…
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
Xiaotong Gu, Zehong Cao, Alireza Jolfaei +4
Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with th…
A Rule-Based EEG Classification System for Discrimination of Hand Motor Attempts in Stroke Patients
Xiaotong Gu, Zehong Cao
Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient's physical mobility, such as freezing of hand movements. Although rehabilita…
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
Chin-Teng Lin, Kuan-Chih Huang, Yu-Ting Liu +6
Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with…
Reinforcement Learning from Hierarchical Critics
Zehong Cao, Chin-Teng Lin
In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks…
Effects of Repetitive SSVEPs on EEG Complexity using Multiscale Inherent Fuzzy Entropy
Zehong Cao, Weiping Ding, Yu-Kai Wang +3
Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generall…
Extraction of SSVEPs-based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
Zehong Cao, Chin-Teng Lin, Kuan-Lin Lai +4
Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the robustness of brain systems. In this study, we present a novel applica…
Reconfiguration of Brain Network between Resting-state and Oddball Paradigm
Fali Li, Chanlin Yi, Yuanyuan Liao +8
The oddball paradigm is widely applied to the investigation of multiple cognitive functions. Prior studies have explored the cortical oscillation and power spectral differing from…
Multi-channel EEG recordings during a sustained-attention driving task
Zehong Cao, Chun-Hsiang Chuang, Jung-Kai King +1
We described driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data include 62 copies of 32 channel elec…
Dynamically Weighted Ensemble-based Prediction System for Adaptively Modeling Driver Reaction Time
Chun-Hsiang Chuang, Zehong Cao, Po-Tsang Chen +3
Predicting a driver's cognitive state, or more specifically, modeling a driver's reaction time (RT) in response to the appearance of a potential hazard warrants urgent research. In…
Semi-supervised Feature Learning For Improving Writer Identification
Shiming Chen, Yisong Wang, Chin-Teng Lin +2
Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overf…
Tensor Decomposition for EEG Signal Retrieval
Zehong Cao, Mukesh Prasad, M. Tanveer +1
Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial sce…
Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG
Zehong Cao, Chin-Teng Lin, Weiping Ding +3
This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited fift…