Demo Data from VLDB 2026 / reviewers in the wild / expert
Elnur Imamaliyev
dblp:427/9824
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0005-2331-0073ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-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 · 87% Immersive interaction · 13% |
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 |
1.0 | 1 | 2026 | Anticipation Before Action: EEG-Based Implicit Intent Detection for Adaptive Gaze Interaction in Mixed Reality · CHI 2026 |
Wearable and physiological sensing › eye tracking
gaze-based interaction |
1.0 | 1 | 2026 | Anticipation Before Action: EEG-Based Implicit Intent Detection for Adaptive Gaze Interaction in Mixed Reality · CHI 2026 |
Immersive interaction
mixed reality |
0.3 | 1 | 2026 | Anticipation Before Action: EEG-Based Implicit Intent Detection for Adaptive Gaze Interaction in Mixed Reality · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
eye tracking · 1.0deep learning · 1.0EEG · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Anticipation Before Action: EEG-Based Implicit Intent Detection for Adaptive Gaze Interaction in Mixed RealityabstractMixed Reality (MR) interfaces increasingly rely on gaze for interaction, yet distinguishing visual attention from intentional action remains difficult, leading to the Midas Touch problem. Existing solutions require explicit confirmations, while brain–computer interfaces may provide an implicit marker of intention using Stimulus-Preceding Negativity (SPN). We investigated how Intention (Select vs. Observe) and Feedback (With vs. Without) modulate SPN during gaze-based MR interactions. During realistic selection tasks, we acquired EEG and eye-tracking data from 28 participants. SPN was robustly elicited and sensitive to both factors: observation without feedback produced the strongest amplitudes, while intention to select and expectation of feedback reduced activity, suggesting SPN reflects anticipatory uncertainty rather than motor preparation. Complementary decoding with deep learning models achieved reliable person-dependent classification of user intention, with accuracies ranging from 75% to 97% across participants. These findings identify SPN as an implicit marker for building intention-aware MR interfaces that mitigate the Midas Touch. Francesco Chiossi, Elnur Imamaliyev, Martin Bleichner, Sven Mayer |
CHI | 2 |