Idea #2

EEG-Guided Image Search / Target Detection

Brief:

A “brain-in-the-loop” visual search system that uses EEG signals during Rapid Serial Visual Presentation (RSVP) to detect when a user recognizes a target image (e.g., via P300 / ERP target responses) and then re-ranks / flags likely targets for faster triage (security footage, medical screening queues, content moderation, satellite imagery).

Aim:

Reduce time-to-find and missed targets in large image collections by using EEG-derived target-recognition signals as an additional implicit feedback channel (alongside clicks/labels).

Deliverables:

• RSVP image-stream UI (desktop/web prototype) with configurable target types
• Real-time EEG pipeline (stream → preprocess → feature extraction → classifier)
• “Triage mode” output: ranked shortlist + confidence + review interface
• Pilot evaluation: EEG-guided triage vs mouse-only/manual tagging (speed + accuracy + workload)

Novelty:

Implicit neural feedback for image search: the user can “recognize” a target without explicitly clicking it, enabling faster screening and potentially catching targets users miss behaviourally.

Research Questions:

Main:

• Does EEG-guided triage (EEG + RSVP) improve target-finding performance (time, recall, false positives) compared to standard manual review?

Mechanism:

• Can we reliably detect target-recognition responses (e.g., P300/ERP signatures) in single-trial or low-trial settings using consumer/lab EEG, and generalise across sessions and users?

UX:

• Does RSVP + EEG triage reduce mental workload overall, or does it increase fatigue/annoyance compared to traditional search workflows?

Scope / Assumptions:

• EEG is treated as a noisy but useful signal; the system degrades gracefully to behaviour-only ranking if EEG quality drops.
• Initial prototype focuses on binary target detection (target vs non-target) in constrained domains (e.g., “find a knife”, “find a tumour-like patch”, “find a red car”).
• RSVP speed and session length must be tuned to avoid excessive fatigue; short sessions + breaks are part of the design.
• Evaluation prioritises measurable outcomes (speed/recall/workload) rather than claiming “mind reading” or true semantic understanding.

References:

• EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces (Scientific Data, 2022) — includes RSVP target detection data and benchmarks
  • https://www.nature.com/articles/s41597-022-01509-w
• Brain activity-based image classification from rapid serial visual presentation (UCSD, 2008) — early RSVP/EEG target-detection work; shows neural detection can occur even when behavioural detection fails
  • https://sccn.ucsd.edu/~nima/downloads/brain_activity_based_image_classification.pdf
• Multirapid / Triple-RSVP paradigms for EEG-based image retrieval (e.g., Lin et al., 2017) — multi-image RSVP variants aimed at improving retrieval performance
  • https://pmc.ncbi.nlm.nih.gov/articles/PMC5541818/
• An EEG Image-search Dataset: A First-of-its-kind in IR/IIR (Healy et al.) — dataset framing EEG signals as implicit feedback for image search
  • https://core.ac.uk/download/pdf/147610007.pdf