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2018 | OriginalPaper | Chapter

An EEG-Based Image Annotation System

Authors : Viral Parekh, Ramanathan Subramanian, Dipanjan Roy, C. V. Jawahar

Published in: Computer Vision, Pattern Recognition, Image Processing, and Graphics

Publisher: Springer Singapore

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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.

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Title: An EEG-Based Image Annotation System
Authors: Viral Parekh
Ramanathan Subramanian
Dipanjan Roy
C. V. Jawahar
Publisher: Springer Singapore
Book: Computer Vision, Pattern Recognition, Image Processing, and Graphics
Print ISBN: 978-981-13-0019-6

Electronic ISBN: 978-981-13-0020-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-981-13-0020-2_27

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