Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jieming Tian
dblp:342/3181
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 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 |
Wearable and physiological sensing · 50% Haptics and multimodal interaction · 50% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing
brain-computer interface |
0.7 | 1 | 2023 | A Haptic Stimulation-Based Training Method to Improve the Quality of Motor Imagery EEG Signal in VR · VR 2023 |
Haptics and multimodal interaction › haptics
haptic training |
0.7 | 1 | 2023 | A Haptic Stimulation-Based Training Method to Improve the Quality of Motor Imagery EEG Signal in VR · VR 2023 |
Virtual and augmented reality
immersive interaction |
0.2 | 1 | 2023 | A Haptic Stimulation-Based Training Method to Improve the Quality of Motor Imagery EEG Signal in VR · VR 2023 |
Methods — techniques the papers use, named apart from their topics
classification · 1.3EEG · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A Haptic Stimulation-Based Training Method to Improve the Quality of Motor Imagery EEG Signal in VRabstractWith the emergence of brain-computer interface (BCI) technology and virtual reality (VR), how to improve the quality of motor imagery (MI) electroencephalogram (EEG) signal has become a key issue for MI BCI applications under VR. In this paper, we proposed to enhance the quality of MI EEG signal by using haptic stimulation training. We designed a first-person perspective and a third-person perspective scene under VR, and the experimental results showed that the left- and right-hand MI EEG quality of the participants improved significantly compared with that before training, and the mean differentiation of the left- and right-hand MI tasks was improved by 21.8% and 15.7%, respectively. We implemented a BCI application system in VR and developed a game based on MI EEG for control of ball movement, in which the average classification accuracy by the participants after training in the first-person perspective reached 93.5%, which was a significant improvement over existing study. Shiwei Cheng 0001, Jieming Tian |
VR | 2 |