  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.
