A Novel Approach of Dynamic Vision Reconstruction from fMRI Profiles Using Siamese Conditional Generative Adversarial Network

Karuppasamy, Rathi; Velusamy, Gomathi; Raj, Raja Soosaimarian Peter

doi:10.1590/1678-4324-2023220330

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Article - Engineering, Technology and Techniques • Braz. arch. biol. technol. 66 • 2023 • https://doi.org/10.1590/1678-4324-2023220330 copy

A Novel Approach of Dynamic Vision Reconstruction from fMRI Profiles Using Siamese Conditional Generative Adversarial Network

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

This paper aims to improve the quality of reconstructed visual stimuli and reduce the computational complexity of the visual stimuli reconstruction processes in the form of functional Magnetic Resonance Imaging (fMRI) profiles. The preceding work envisions the non-cognitive contents of brain activity vain to integrate visual data of diverse hierarchical levels. Existing approaches such as Deep Canonically Correlated Auto Encoder detect the significant challenges of reconstructing visual stimuli from brain activity: fMRI noise, large dimensionality of a limited number of data instances, and complex structure of visual stimuli. In this activity, we will also analyze the scope for utilizing the spatiotemporal data to resolve the neural correlates of visual stimulus representations and reconstruct the resembling visual stimuli. The purpose of this work is to manipulate those suffering from developmental disabilities. A novel Siamese conditional Generative Adversarial Network (ScGAN) approach is proposed to resolve these significant issues. The key features of ScGAN are as follows: 1. Siamese Neural Network (SNN) is a dimensionality reduction approach that takes as visual stimulus information alloy component and its goals to discover each of them effectively. It shows the critical component of visual stimuli. 2. In a conditional Generative Adversarial Network, the labels portrayan expansion to a latent variable to better generate and discriminate visual stimuli. Experiments on four fMRI datasets prove that our technique can reconstruct visual stimuli precisely. The performance metrics are evaluated by Mean Squared Error (MSE), Accuracy, Pearson Correlation Coefficient (PCC), Losses, Structural Similarity Index (SSIM), Computational Time, etc. It proves that the proposed method yields better outcomes in terms of accuracy.

Keywords:
Vision reconstruction; fMRI; visual cortex; encoding and decoding; CGAN; SNN

HIGHLIGHTS

• We combined Siamese Neural Network with CGAN to create a SCGAN framework.

• Visual perception reconstructed by Conditional Generative Adversarial Network (CGAN).

• Reducing image loss, the linear version learns to be expecting the latent area out of Blood Oxygen Level Dependent.

• Reconstruction on video fMRI dataset was objectively identifiable.

Approach	Datasets	Loss	E2E Training	Pre-training
DCGAN	BRAINS, GOD,vim-1	MSE,MAE	No	AlexNet based Comparator trained in ImageNet
DNN	Natural Images, Alphabetical Letters	MSE	No	AlexNet and VGG-19based pre-trained on ImageNet
GAN	Natural Images, Artificial Shapes	MSE,Adv	Yes	Caffeneet based comparator pre-trained on ImageNet
GAN-BVRM	Vim-1	MSE	No	Generator of BigGAN pre-trained on ImageNet
DVAE/GAN	6-9,BRAINS,GOD	KL,Adv	No	Model pre-trained on external data from ImageNet
SSGAN	Natural Images	MAE,Adv	No	-
BigBiGAN	GOD	Adv	No	BigBiGAN pre-trained on ImageNet

Dataset	No.of.Instances	Pixel Resolution	No.of.Voxels	ROIs
Natural Movies	374	800*600	10214	V1, V2, V3, V4, LOC, PPA, FFA, TPJ, LIP, FEF, and PEF.

S.No	Metric	Formula	Description
1.	Mean Squared Error	$M S E = \frac{1}{N} \sum_{i = 1}^{N} {(X_{i} - Y_{i})}^{2}$	Mean Squared Error (MSE) is the most basic statistic for evaluating the quality of visual stimulus reconstruction. Given X_i and Y_i, the original stimuli and the reconstructed stimuli are flattened representations of one-dimensional space.
2.	Pearson’s Correlation Coefficient	$P C C (x, y) = \frac{\sum (x - μ_{x}) (y - μ_{y})}{\sqrt{\sum {(x - μ_{x})}^{2} \sum {(y - μ_{y})}^{2}}}$	Pearson’s Correlation Coefficient (PCC) is a popular statistical tool for determining the linear correlation amid two variables.
3.	Structural Similarity Index	$S S I M (p, q) = \frac{(2 μ_{p} μ_{q} + C 1) (2 σ_{p q} + C 2)}{(μ_{p}^{2} + μ_{q}^{2} + C 1) (σ_{p}^{2} + σ_{q}^{2} + C 2)}$	Structural Similarity Index (SSIM) is a popular similarity metric of images for extracting structural information from visual images. The alikeness of spatially close information of pixels among the original and reconstructed visual stimulus is measured by SSIM. Here, µ and σ indicate the mean and variance of the p and q values; σ_pq indicates the covariance value of p and q: C1 and C2 are constants.
4.	Propensity Score Matching	$d (x, y) = \sum_{l} \frac{1}{H_{l} W_{l}} \sum_{h, w} ‖ w_{l} \cdot (f_{x}^{l} - f_{y}^{l}) ‖_{2}^{2}$	The conditional likelihood of assignment to a specific treatment specified by a vector of observable covariates is a propensity score matching. Where, w_lis denoted as channel-wise activation, d(x,y) denotes the distance amid the reconstructed (y) and original visual stimuli(x).
5.	Maximum Mean Discrepancy	$M_{k} (P_{r}, P_{g}) = E_{x, x^{'} ~ P_{r}} [k (x, x^{'})] - 2 E_{x ~ P_{r}, y ~ P_{g}} [k (x, y)] + E_{y, y^{'} ~ P_{g}} [k (y, y^{'})]$	The Maximum Mean Discrepancy (MMD) method compares two probability distributions, Pr and Pg, by utilizing samples selected randomly from each distribution.
6.	Inception Score	$I S = \exp (E_{x ~ p} \underset{K L D i v e r g e n c e}{\underset{︸}{D_{K L} (p {y \| x} ‖ p (y))}})$	In adversarial networks, Kullback-Leibler divergence is calculated by comparing the conditional and marginal label distributions over the generated data.
7.	Frechet Inception Distance	$F I D = {‖ μ_{X} - μ_{Y} ‖}^{2} + T r (Σ_{X} + Σ_{Y} - 2 \sqrt{Σ_{X} Σ_{Y}})$	The Wasserstein-2 distance between Gaussian fitted multi-variants data stored in a feature space. Where μis the mean value, andΣ is the covariance of the real (X) and false (Y) generated images.
8.	Accuracy	$A c c u r a c y = \frac{N u m b e r o f a c c u r a t e p r e d i c t i o n}{T o t a l n u m b e r o f p r e d i c t i o n}$	One of the measures used to measure accuracy is the percentage of correct predictions made from the total number of input samples.
9.	Loss	$E_{x} [\log D (x)] + E_{z} [\log (1 - D (G (z)))]$	The min-max function is utilized in this paper. The CGAN network's generator seeks to minimize loss, but the discriminator seeks to maximize loss function.

Dataset	Work	Approach	PCC(mean±std)
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	. 252±.141
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	.278±.146
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	.309±.152
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	.327±.157
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	.352±.168
	Proposed:	Proposed:
	Ours	SCGAN	.358±.174

Dataset	Work	Approach	MSE(mean±std)
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	. 092±.127
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	.088±.120
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	.070±.116
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	.064±.110
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	.054±.107
	Proposed:	Proposed:
	Ours	SCGAN	.046±.104

Dataset	Work	Approach	SSIM(mean±std)
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	0.180±.170
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	0.188±.153
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	0.192±.145
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	0.200±.138
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	0.303±.135
	Proposed:	Proposed:
	Ours	SCGAN	0.410±.126

Dataset	Work	Approach	PSM
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	0.986
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	0.976
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	0.968
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	0.949
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	0.943
	Proposed:	Proposed:
	Ours	SCGAN	0.937

Dataset	Work	Approach	MMD
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	0.95
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	0.88
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	0.85
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	0.81
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	0.78
	Proposed:	Proposed:
	Ours	SCGAN	0.75

Dataset	Work	Approach	IS (mean ± std)
Dataset	Work	Approach	Real Data	Ours
Natural Movies	Existing:	Existing:	28.45± .22
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN		16.38± .30
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN		17.54± .28
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM		18.90± .25
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN		19.99± .21
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN		20.72± .19
	Proposed:	Proposed:
	Ours	SCGAN		21.8± .18

Dataset	Work	Approach	FID (mean ± std)
Dataset	Work	Approach	Real Data	Ours
Natural Movies	Existing:	Existing:	34.32± .96
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN		22.82± .80
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN		23.54± .82
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM		25.90± .85
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN		26.74± .81
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN		27.25± .84
	Proposed:	Proposed:
	Ours	SCGAN		28.80± .86

Dataset	Work	Approach	Accuracy (%)
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	40
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	35
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	35
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	38
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	42
	Proposed:	Proposed:
	Ours	SCGAN	44

Dataset	Work	Approach	Loss
Dataset	Work	Approach	Generator	Discriminator
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	2.5310	0.7445
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	2.4962	0.9524
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	2.5463	1.1289
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	2.4264	1.3221
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	2.4519	1.0815
	Proposed:	Proposed:
	Ours	SCGAN	2.4803	1.0926

Dataset	Work	Approach	Time (s)
Natural Movies	Existing:	Existing:
	Wen et.al.[¹⁵15 Seeliger K, Guclu U, Ambrogioni L, Gucluturk Y, vanGerven MAJ. Generative ¨ adversarial networks for reconstructing natural images from brain activity. NeuroImage.2018; 181: 775-785. Available from: DOI: 10.1016/j.neuroimage.2018.07.043 https://doi.org/10.1016/j.neuroimage.201... ]	CNN	168
	Ian et.al. [²³23 Mirza M,Osindero S. Conditional Generative Adversarial Nets.ArXiv.2014.Available from: https://doi.org/10.48550/arXiv.1411.1784 https://doi.org/10.48550/arXiv.1411.1784... ]	GAN	150.8
	Qiao et.al. [¹⁸18 Donahue J,Simonyan K. Large Scale Adversarial Representation Learning. In 2019 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019 Vancouver, Canada.vol. 32.Available from: https://doi.org/10.48550/arXiv.1907.02544 https://doi.org/10.48550/arXiv.1907.0254... ]	GAN-BVRM	162.4
	Jiang et.al. [²¹21 Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,NeuroImage.2021; 228:117602. ISSN 1053-8119. Available from: https://doi.org/10.1016/j.neuroimage.2020.117602 https://doi.org/10.1016/j.neuroimage.202... ]	SRN	145.6
	Ziqi et.al. [²²22 Goodfellow IJ,Abadie JP,Mirza M,Xu B, Farley DW, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. In NeurIPS.2014;2672-2680.]	DVAE/GAN	130.1
	Proposed:	Proposed:
	Ours	SCGAN	120.9

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[1] *Correspondence: rathikaruppasamy@gmail.com;Tel.: +91-9994768305 (R.K.).