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.
@article{arxiv.1711.02383,
title = {An EEG-based Image Annotation System},
author = {Viral Parekh and Ramanathan Subramanian and Dipanjan Roy and C. V. Jawahar},
journal= {arXiv preprint arXiv:1711.02383},
year = {2017}
}