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An EEG-based image annotation system
V. Parekh, R. Subramanian, , C.V. Jawahar
Published in Springer Verlag
2018
Volume: 841
   
Pages: 303 - 313
Abstract
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 ms, 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. © Springer Nature Singapore Pte Ltd. 2018.
About the journal
JournalData powered by TypesetCommunications in Computer and Information Science
PublisherData powered by TypesetSpringer Verlag
ISSN18650929