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Dorsal-Ventral Visual Pathways and Object Characteristics: Beamformer Source Analysis of EEG

Akanksha Tiwari1, Ram Bilas Pachori1,2, Premjit Khanganba Sanjram1,3,4,*

1 Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, 453552, India
2 Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, 453552, India
3 Discipline of Psychology, Indian Institute of Technology Indore, Indore, 453552, India
4 Center for Electric Vehicles and Intelligent Transport Systems, Indian Institute of Technology Indore, Indore, 453552, India

* Corresponding Author: Premjit Khanganba Sanjram. Email: email

(This article belongs to the Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 70(2), 2347-2363. https://doi.org/10.32604/cmc.2022.020299

Abstract

In performing a gaming task, mental rotation (MR) is one of the important aspects of visuospatial processing. MR involves dorsal-ventral pathways of the brain. Visual objects/models used in computer-games play a crucial role in gaming experience of the users. The visuospatial characteristics of the objects used in the computer-game influence the engagement of dorsal-ventral visual pathways. The current study investigates how the objects’ visuospatial characteristics (i.e., angular disparity and dimensionality) in an MR-based computer-game influence the cortical activities in dorsal-ventral visual pathways. Both the factors have two levels, angular disparity: convex angle (CA) vs. reflex angle (RA) and dimensionality: 2D vs. 3D. Sixty healthy adults, aged, 18–29 years (M = 21.6) were recruited for the study and randomly assigned to four gaming conditions i.e., 15 participants in each group. The multichannel electroencephalogram (EEG) data were recorded from 60 healthy adults while playing the game. The source reconstruction was done for ∼3000 sources inside the brain using the Dynamic Imaging of Coherent Sources (DICS) beamforming method for θ1(4–5.75), θ2(5.75–7.5), α1(7.5–9), α2(9–11), α3(11–13), β1(13–17.25), β2(17.25–21.5) Hz frequency sub-bands. The reconstructed neuronal sources were segmented into 68 functionally parcellated brain regions, and the percentage of active sources for each region was computed. Further, the differences across the 68 regions among the four gaming conditions were evaluated using the percentage of active sources. The differences in activation for the dorsal-ventral pathways and some additional brain regions were observed among the four groups. The game with 2D objects and CA showed higher activation than that with 3D objects and RA, respectively. The dorsal pathway was found to be more active in contrast to the ventral pathway. The findings suggest that angular disparity and dimensionality in MR influence the engagement of dorsal-ventral visual pathways in such a way that angular disparity has a greater impact on cortical activation across this region than dimensionality. Also, higher activation for CA as compared to RA irrespective of dimensionality reflects the complexity of spatial information processing under CA. Similarly, greater activation was seen for 2D objects than 3D, indicating difficulty in information processing due to deficient visual features.

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APA Style
Tiwari, A., Pachori, R.B., Sanjram, P.K. (2022). Dorsal-ventral visual pathways and object characteristics: beamformer source analysis of EEG. Computers, Materials & Continua, 70(2), 2347-2363. https://doi.org/10.32604/cmc.2022.020299
Vancouver Style
Tiwari A, Pachori RB, Sanjram PK. Dorsal-ventral visual pathways and object characteristics: beamformer source analysis of EEG. Comput Mater Contin. 2022;70(2):2347-2363 https://doi.org/10.32604/cmc.2022.020299
IEEE Style
A. Tiwari, R.B. Pachori, and P.K. Sanjram, “Dorsal-Ventral Visual Pathways and Object Characteristics: Beamformer Source Analysis of EEG,” Comput. Mater. Contin., vol. 70, no. 2, pp. 2347-2363, 2022. https://doi.org/10.32604/cmc.2022.020299



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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