Yuan-Cheng Liu

dblp:156/2194 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0003-0171-3769ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 77% Wearable and physiological sensing · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction › automation
automation levels
0.812024
From Driver to Supervisor: Comparing Cognitive Load and EEG-Based Attentional Resource Allocation Across Automation Levels · Int. J. Hum. Comput. Stud. 2024
Wearable and physiological sensing
electroencephalography
0.212024
From Driver to Supervisor: Comparing Cognitive Load and EEG-Based Attentional Resource Allocation Across Automation Levels · Int. J. Hum. Comput. Stud. 2024

Methods — techniques the papers use, named apart from their topics

cognitive load measurement · 0.8EEG-based attentional resource allocation · 0.8
YearPublicationVenuePosition
2024 From Driver to Supervisor: Comparing Cognitive Load and EEG-Based Attentional Resource Allocation Across Automation Levels
Nikol Figalová, Hans-Joachim Bieg, Julian Elias Reiser, Yuan-Cheng Liu, Martin Baumann 0001, Lewis L. Chuang, Olga Pollatos
Int. J. Hum. Comput. Stud.4
2023 Human-Machine Interface Evaluation Using EEG in Driving Simulator
abstract
Automated vehicles are pictured as the future of transportation, and facilitating safer driving is only one of the many benefits. However, due to the constantly changing role of the human driver, users are easily confused and have little knowledge about their responsibilities. Being the bridge between automation and human, the human-machine interface (HMI) is of great importance to driving safety. This study was conducted in a static driving simulator. Three HMI designs were developed, among which significant differences in mental workload using NASA-TLX and the subjective transparency test were found. An electroencephalogram was applied throughout the study to determine if differences in the mental workload could also be found using EEG’s spectral power analysis. Results suggested that more studies are required to determine the effectiveness of the spectral power of EEG on mental workload, but the three interface designs developed in this study could serve as a solid basis for future research to evaluate the effectiveness of psychophysiological measures.
Yuan-Cheng Liu, Nikol Figalová, Martin Baumann 0001, Klaus Bengler
IV1