142 citations · 707 across the 24 of their papers we have counts for
25 papers
An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments
Carlos Alfredo Tirado Cortes, Chin-Teng Lin, Tien-Thong Nguyen Do +1
Previous studies showed that natural walking reduces the susceptibility to VR sickness. However, many users still experience VR sickness when wearing VR headsets that allow free wa…
Cross Task Neural Architecture Search for EEG Signal Classifications
Yiqun Duan, Zhen Wang, Yi Li +3
Electroencephalograms (EEGs) are brain dynamics measured outside the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various…
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…
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Lubin Meng, Jian Huang, Zhigang Zeng +6
Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adopti…
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
Javier Fumanal-Idocin, Yu-Kai Wang, Chin-Teng Lin +3
Brain Computer Interface technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery.…
EnK: Encoding time-information in convolution
Avinash Kumar Singh, Chin-Teng Lin
Recent development in deep learning techniques has attracted attention in decoding and classification in EEG signals. Despite several efforts utilizing different features of EEG si…
Clustering via torque balance with mass and distance
Jie Yang, Chin-Teng Lin
Grouping similar objects is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition. Inspired by the to…
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…
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
Zhenhua Shi, Dongrui Wu, Jian Huang +2
Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy hig…
Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers
Xiao Zhang, Dongrui Wu, Lieyun Ding +4
An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled indi…
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning
Yurui Ming, Dongrui Wu, Yu-Kai Wang +2
Fatigue is the most vital factor of road fatalities and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to analyze an ele…
Adaptive Initialization Method for K-means Algorithm
Jie Yang, Yu-Kai Wang, Xin Yao +1
The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine th…
White-Box Target Attack for EEG-Based BCI Regression Problems
Lubin Meng, Chin-Teng Lin, Tzyy-Ring Jung +1
Machine learning has achieved great success in many applications, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). Unfortunately, many machine learning…
Protecting Privacy of Users in Brain-Computer Interface Applications
Anisha Agarwal, Rafael Dowsley, Nicholas D. McKinney +4
Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most in…
On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression
Dongrui Wu, Chin-Teng Lin, Jian Huang +1
Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently optimize…
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…
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…
Active Learning for Regression Using Greedy Sampling
Dongrui Wu, Chin-Teng Lin, Jian Huang
Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization abil…
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
Dongrui Wu, Vernon J. Lawhern, Stephen Gordon +2
Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting,…
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
Dongrui Wu, Vernon J. Lawhern, Stephen Gordon +2
There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers of…
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…
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Dongrui Wu, Brent J. Lance, Vernon J. Lawhern +3
Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first ti…
Measuring Cognitive Conflict in Virtual Reality with Feedback-Related Negativity
Avinash Kumar Singh, Hsiang-Ting Chen, Jung-Tai King +1
As virtual reality (VR) emerges as a mainstream platform, designers have started to experiment new interaction techniques to enhance the user experience. This is a challenging task…