NDLI: A spatial selective visual attention pattern recognition method based on joint short SSVEP

Content Provider	IEEE Xplore Digital Library
Author	Songyun Xie Fangshi Zhu Obermayer, K. Ritter, P. Linan Wang
Copyright Year	2013
Description	Author affiliation: Sch. of Electron. Eng. & Comput. Sci., Tech. Univ. Berlin, Berlin, Germany (Obermayer, K.) \|\| Sch. of Electron. & Inf., Northwestern Polytech. Univ., Xi'an, China (Songyun Xie; Fangshi Zhu; Linan Wang) \|\| Minerva Res. Group BrainModes, Max Planck Inst. for Human Cognitive & Brain Sci., Leipzig, Germany (Ritter, P.)
Abstract	Spatial selective attention pattern recognition plays a significant role in specific people's (e.g.: pilot's) state monitoring. Steady-State Visual Evoked Potentials (SSVEP) were recorded from the scalp of 6 subjects who were cued to attend to a flickering sequence displayed in one visual field while ignoring a similar one with a different flickering rate in the opposite field. The SSVEP to either flickering stimulus was enhanced when attention was lead to the same direction rather than to the opposite direction. The most significant enlargement is generally located on the posterior scalp contralateral to the visual field of stimulation. This attention-caused amplitude enhancement of SSVEP can be used to measure the attention shifting. In this paper, we developed an algorithm to extract short SSVEP, selectively combine them to form a joint temporal spatial selective attention feature, and use Support Vector Machine (SVM) to classify different attention pattern joint features. By segmenting the long single trial SSVEP (12s) data into short pieces (1s), we are able to largely decrease the training time while still keeping a high recognition accuracy (>93%) for most subjects, which makes it possible to monitor spatial selective attention on time.
Starting Page	1
Ending Page	7
File Size	1498011
Page Count	7
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6706872
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Joints Feature extraction Support vector machines Pattern recognition Classification algorithms Accuracy
Content Type	Text
Resource Type	Article

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