Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

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ID: 35010
2018
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Abstract
Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.
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yoon2018spatialcomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yoon, Jaehong;Lee, Jungnyun;Whang, Mincheol;
Journal Computational Intelligence and Neuroscience
Year 2018
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2018/6058065
Keywords Keywords not found

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