2.5k citations
- University of California San DiegoUS13 papers
- The University of Texas at San AntonioUS2 papers
- University of Technology SydneyAU2 papers
- Center for Neurologic StudyUS1 paper
- DCS Corporation (United States)US1 paper
- DEVCOM Army Research LaboratoryUS1 paper
- Huazhong University of Science and TechnologyCN1 paper
- Microsoft (United States)US1 paper
- National Yang Ming Chiao Tung UniversityTW1 paper
- Salk Institute for Biological StudiesUS1 paper
- Samsung (United States)US1 paper
- The University of Texas at DallasUS1 paper
13 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,…
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…
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…
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…
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
Luca Pion-Tonachini, Ken Kreutz-Delgado, Scott Makeig
The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution. Although s…
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…