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Connectivity Analysis Using Functional Brain Networks to Evaluate Cognitive Activity during 3D Modelling

Authors
  • Baig, Muhammad Zeeshan
  • Kavakli, Manolya
Type
Published Article
Journal
Brain Sciences
Publisher
MDPI AG
Publication Date
Jan 24, 2019
Volume
9
Issue
2
Identifiers
DOI: 10.3390/brainsci9020024
PMID: 30682814
PMCID: PMC6406638
Source
PubMed Central
Keywords
License
Green

Abstract

Modelling 3D objects in CAD software requires special skills which require a novice user to undergo a series of training exercises to obtain. To minimize the training time for a novice user, the user-dependent factors must be studied. we have presented a comparative analysis of novice/expert information flow patterns. We have used Normalized Transfer Entropy (NTE) and Electroencephalogram (EEG) to investigate the differences. The experiment was divided into three cognitive states i.e., rest, drawing, and manipulation. We applied classification algorithms on NTE matrices and graph theory measures to see the effectiveness of NTE. The results revealed that the experts show approximately the same cognitive activation in drawing and manipulation states, whereas for novices the brain activation is more in manipulation state than drawing state. The hemisphere- and lobe-wise analysis showed that expert users have developed an ability to control the information flow in various brain regions. On the other hand, novice users have shown a continuous increase in information flow activity in almost all regions when doing drawing and manipulation tasks. A classification accuracy of more than 90% was achieved with a simple K-nearest neighbors (k-NN) to classify novice and expert users. The results showed that the proposed technique can be used to develop adaptive 3D modelling systems.

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