Realtime Microsaccade Detection with Convolutional Neural Network

Joshua EMOTO  Yutaka HIRATA  

Publication
D - Abstracts of IEICE TRANSACTIONS on Information and Systems (Japanese Edition)   Vol.J101-D   No.2   pp.456-467
Publication Date: 2018/02/01
Online ISSN: 1881-0225
Type of Manuscript: PAPER
Category: 
Keyword: 
eye movement,  microsaccade,  covert attention,  deep learning,  

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Summary: 
Microsaccade (MSC) is a rapid and miniature eye movement, which recently has been considered to reflect covert attention. There are many potential applications of MSC where estimation of attentional states is demanded. For such purposes, a real time, robust and highly accurate MSC detection system is required. Presently, we propose a novel MSC detection system, using a convolutional neural network and deep learning. We demonstrate that the proposed system runs in real time on a general PC, and outperforms representative conventional MSC detection methods in terms of accuracy by using real eye movement data recorded in human subjects.