VR/AR Display Gaze Estimation with EEG Selection Validation
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Solution Overview
Problem
Existing VR/AR headsets face errors in accurately detecting user gaze and confirming intended selections due to inaccuracies in eye-tracking and EEG signal interpretation, leading to unintended item selections.
Innovation Solution
A VR/AR headset system incorporating IR cameras and EEG sensors to track eye movement and brain activity, combined with processing resources to analyze gaze patterns and EEG signals, confirms selection intent through a correlation of gaze duration and EEG excitation levels, supported by additional sensory inputs like gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If eye-tracking and EEG sensors are used to detect user gaze and selection intent, then selection accuracy can be improved, but errors in detecting eye gaze and confirming intended selections still occur leading to unintended item selections
Solution Approach 1:
The patent combines multiple sensing modalities (eye-tracking cameras, EEG sensors, and additional sensors) into an integrated system that processes signals together to determine selection intent. This multi-sensory fusion approach allows the system to cross-validate signals and reduce false positives from any single modality, thereby improving both measurement precision and selection reliability simultaneously.
2Measurement precision
If multiple sensors (eye-tracking, EEG, additional sensors) are combined to confirm selection intent, then selection accuracy improves, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional sensing system where a single integrated apparatus performs multiple functions: eye-tracking for gaze detection, EEG for brain activity monitoring, and additional sensors for supplementary signals. This universal device consolidates what would otherwise require separate systems, improving selection intent detection while managing complexity through integration rather than proliferation of separate components.
Solution Approach 2:
The patent introduces a processing system that acts as an intermediary between the multiple sensors and the final selection determination. This mediator processes and integrates signals from eye-tracking, EEG, and additional sensors, coordinating their outputs to reach a unified selection intent determination. The intermediary manages the complexity by providing a structured framework for signal integration rather than direct sensor-to-action connections.
3Ease of operation
If passive calibration with mouse-click events is used to establish coordinate mapping, then ease of operation is improved, but measurement precision of eye-tracking may be insufficient for accurate selection
Solution Approach 1:
The patent employs feedback mechanisms where the system continuously monitors selection outcomes and adjusts calibration parameters accordingly. User interactions (including mouse-clicks during calibration) provide feedback that refines the coordinate mapping between eye position and display coordinates. This iterative feedback process maintains ease of operation during initial calibration while progressively improving measurement precision through adaptive adjustments based on actual usage patterns.
Data Source
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AI summary
Example implementations relate to virtual reality/augmented reality signals. For example, a non-transitory computer readable medium storing instructions executable by a processing resource to receive eye-tracking signals from a camera mounted on a display. The instructions can cause the processing resource to estimate a location on a display using the eye-tracking signals. The instructions can cause the processing resource to receive electroencephalographic (EEG) signals. The instructions can cause the processing resource to confirm the estimated location based on the eye-tracking signals and the EEG signals.