349 citations · 958 across the 20 of their papers we have counts for
20 papers
Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario
Kuan-Jung Chiang, Steven Dong, Chung-Kuan Cheng +1
Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias,…
IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal
Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang +1
Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent misinterpretatio…
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning
Kuan-Jung Chiang, Chun-Shu Wei, Masaki Nakanishi +1
Objective: This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain-computer…
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…
A Virtual Reality Game as a Tool to Assess Physiological Correlations of Stress
Daniel H. Lee, Tzyy-Ping Jung
The objective of this study is to develop and use a virtual reality game as a tool to assess the effects of realistic stress on the behavioral and physiological responses of partic…
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…
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…
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…
Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events
Sharaj Panwar, Paul Rad, Tzyy-Ping Jung +1
Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful dee…
Decision-Making in a Social Multi-Armed Bandit Task: Behavior, Electrophysiology and Pupillometry
Julia Anna Adrian, Siddharth Siddharth, Syed Zain Ali Baquar +2
Understanding, predicting, and learning from other people's actions are fundamental human social-cognitive skills. Little is known about how and when we consider other's actions an…
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing
Siddharth Siddharth, Tzyy-Ping Jung, Terrence J. Sejnowski
In recent years, the use of bio-sensing signals such as electroencephalogram (EEG), electrocardiogram (ECG), etc. have garnered interest towards applications in affective computing…
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces
Kuan-Jung Chiang, Chun-Shu Wei, Masaki Nakanishi +1
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication. State-of-the-art traini…
Multi-modal Approach for Affective Computing
Siddharth Siddharth, Tzyy-Ping Jung, Terrence J. Sejnowski
Throughout the past decade, many studies have classified human emotions using only a single sensing modality such as face video, electroencephalogram (EEG), electrocardiogram (ECG)…
An Affordable Bio-Sensing and Activity Tagging Platform for HCI Research
Siddharth, Aashish Patel, Tzyy-Ping Jung +1
We present a novel multi-modal bio-sensing platform capable of integrating multiple data streams for use in real-time applications. The system is composed of a central compute modu…
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…
Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
Dongrui Wu, Jung-Tai King, Chun-Hsiang Chuang +2
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…
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig +2
Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its…
Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities
Zhilin Zhang, Bhaskar D. Rao, Tzyy-Ping Jung
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make poss…
Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware
Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig +1
Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a sy…
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig +1
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambul…