A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection

Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operator...

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Bibliographic Details
Published in:International journal of neural systems Vol. 29; no. 1; p. 1850015
Main Authors: Hua, Chengcheng, Wang, Hong, Lu, Shaowen, Liu, Chong, Khalid, Syed Madiha
Format: Journal Article
Language:English
Published: Singapore 01.02.2019
Subjects:
Functional brain connection
supervised fine-tuning
proficiency detection
EEG
stacked autoencoder
ISSN:1793-6462, 1793-6462
Online Access:Get more information
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Summary:Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-data-driven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper. This method uses stacked autoencoder (SAE) to encode the multi-channel EEG data into codes and then computes the dissimilarity between the codes from every pair of electrodes to build FBN. The highlight of this method is that the SAE has a multi-layered structure and is semi-supervised, which means it can dig deeper information and generate better features. Then an experiment was performed, the EEG of the operators were collected while they were operating and analyzed to detect their proficiency. The results show that the BCSAE method generated more number of separable features with less redundancy, and the average accuracy of classification (96.18%) is higher than that of the control methods: PLV (92.19%) and PLI (78.39%).
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ISSN:1793-6462
1793-6462
DOI:10.1142/S0129065718500156