Anticipation Before Action: EEG-Based Implicit Intent Detection for Adaptive Gaze Interaction in Mixed Reality

TL;DR

This study demonstrates that EEG signals, specifically Stimulus-Preceding Negativity (SPN), can be used to implicitly detect user intention in mixed reality gaze interactions, enabling more natural and intention-aware interfaces.

Contribution

The paper introduces the use of SPN as an implicit marker for intention detection in MR, and employs deep learning for reliable person-dependent intention classification.

Findings

SPN is sensitive to intention and feedback during gaze tasks.

Deep learning models achieved 75-97% accuracy in intention classification.

SPN reflects anticipatory uncertainty, not motor preparation.

Abstract

Mixed 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.