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Jieming Tian

dblp:342/3181 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing
brain-computer interface
0.712023
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
0.712023
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
0.212023
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
YearPublicationVenuePosition
2023 A Haptic Stimulation-Based Training Method to Improve the Quality of Motor Imagery EEG Signal in VR
abstract
With 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
VR2