# An EEG-based Image Annotation System

arXiv: https://arxiv.org/abs/1711.02383 · HTML: https://mlsanity.com/paper/1711.02383/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification](https://mlsanity.com/paper/2107.03983/) (2021)
- [Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN](https://mlsanity.com/paper/2402.10115/) (2024)
- [Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model](https://mlsanity.com/paper/2403.06532/) (2024)
- [Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Classical Machine Learning and Deep Learning Optimization Techniques with Neurofeedback](https://mlsanity.com/paper/2509.14078/) (2025)

## Abstract (arXiv)

The success of deep learning in computer vision has greatly increased the need for annotated image datasets. We propose an EEG (Electroencephalogram)-based image annotation system. While humans can recognize objects in 20-200 milliseconds, the need to manually label images results in a low annotation throughput. Our system employs brain signals captured via a consumer EEG device to achieve an annotation rate of up to 10 images per second. We exploit the P300 event-related potential (ERP) signature to identify target images during a rapid serial visual presentation (RSVP) task. We further perform unsupervised outlier removal to achieve an F1-score of 0.88 on the test set. The proposed system does not depend on category-specific EEG signatures enabling the annotation of any new image category without any model pre-training.