Visually evoked brain signals guided image regeneration using GAN variants
Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be use...
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| Autor*in: |
Kumari, Nandini [verfasserIn] |
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| Format: |
Artikel |
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Englisch |
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2023 |
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2023), 21 vom: 03. März, Seite 32259-32279 |
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volume:82 ; year:2023 ; number:21 ; day:03 ; month:03 ; pages:32259-32279 |
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10.1007/s11042-023-14769-4 |
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| 520 | |a Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. | ||
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| 650 | 4 | |a Visually evoked EEG | |
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10.1007/s11042-023-14769-4 doi (DE-627)OLC2145335501 (DE-He213)s11042-023-14769-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Kumari, Nandini verfasserin aut Visually evoked brain signals guided image regeneration using GAN variants 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. Image regeneration Visually evoked EEG Generative adversarial network CapsGAN Structural similarity index measure Anwar, Shamama aut Bhattacharjee, Vandana aut Sahana, Sudip Kumar (orcid)0000-0002-2493-3695 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 21 vom: 03. März, Seite 32259-32279 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:21 day:03 month:03 pages:32259-32279 https://doi.org/10.1007/s11042-023-14769-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 21 03 03 32259-32279 |
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10.1007/s11042-023-14769-4 doi (DE-627)OLC2145335501 (DE-He213)s11042-023-14769-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Kumari, Nandini verfasserin aut Visually evoked brain signals guided image regeneration using GAN variants 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. Image regeneration Visually evoked EEG Generative adversarial network CapsGAN Structural similarity index measure Anwar, Shamama aut Bhattacharjee, Vandana aut Sahana, Sudip Kumar (orcid)0000-0002-2493-3695 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 21 vom: 03. März, Seite 32259-32279 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:21 day:03 month:03 pages:32259-32279 https://doi.org/10.1007/s11042-023-14769-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 21 03 03 32259-32279 |
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10.1007/s11042-023-14769-4 doi (DE-627)OLC2145335501 (DE-He213)s11042-023-14769-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Kumari, Nandini verfasserin aut Visually evoked brain signals guided image regeneration using GAN variants 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. Image regeneration Visually evoked EEG Generative adversarial network CapsGAN Structural similarity index measure Anwar, Shamama aut Bhattacharjee, Vandana aut Sahana, Sudip Kumar (orcid)0000-0002-2493-3695 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 21 vom: 03. März, Seite 32259-32279 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:21 day:03 month:03 pages:32259-32279 https://doi.org/10.1007/s11042-023-14769-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 21 03 03 32259-32279 |
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10.1007/s11042-023-14769-4 doi (DE-627)OLC2145335501 (DE-He213)s11042-023-14769-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Kumari, Nandini verfasserin aut Visually evoked brain signals guided image regeneration using GAN variants 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. Image regeneration Visually evoked EEG Generative adversarial network CapsGAN Structural similarity index measure Anwar, Shamama aut Bhattacharjee, Vandana aut Sahana, Sudip Kumar (orcid)0000-0002-2493-3695 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 21 vom: 03. März, Seite 32259-32279 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:21 day:03 month:03 pages:32259-32279 https://doi.org/10.1007/s11042-023-14769-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 21 03 03 32259-32279 |
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10.1007/s11042-023-14769-4 doi (DE-627)OLC2145335501 (DE-He213)s11042-023-14769-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Kumari, Nandini verfasserin aut Visually evoked brain signals guided image regeneration using GAN variants 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. Image regeneration Visually evoked EEG Generative adversarial network CapsGAN Structural similarity index measure Anwar, Shamama aut Bhattacharjee, Vandana aut Sahana, Sudip Kumar (orcid)0000-0002-2493-3695 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 21 vom: 03. März, Seite 32259-32279 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:21 day:03 month:03 pages:32259-32279 https://doi.org/10.1007/s11042-023-14769-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 21 03 03 32259-32279 |
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070 004 VZ Visually evoked brain signals guided image regeneration using GAN variants Image regeneration Visually evoked EEG Generative adversarial network CapsGAN Structural similarity index measure |
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Visually evoked brain signals guided image regeneration using GAN variants |
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Visually evoked brain signals guided image regeneration using GAN variants |
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Kumari, Nandini |
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Kumari, Nandini Anwar, Shamama Bhattacharjee, Vandana Sahana, Sudip Kumar |
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visually evoked brain signals guided image regeneration using gan variants |
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Visually evoked brain signals guided image regeneration using GAN variants |
| abstract |
Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Generative Adversarial Networks have recently proven to be very effective in generative applications involving images, and they are now being used to regenerate images using visually evoked brain signals. Recent neuroscience research has discovered evidence that brain-evoked data can be used to decipher how the human brain functions. Simultaneously, the latest advancement in deep learning integrated with a high-level interest in generative methods has made learning the data distribution possible and realistic images can be produced from random noise. In this work, an advanced generative adversarial method that incorporates the capsule network with the generative adversarial networks model i.e. Capsule Generative Adversarial Network is proposed to regenerate images with decoded information and formulated features from visually evoked brain signals. There are two stages in the proposed method: Encoder, for data formulation of visually evoked brain activity and the image reconstruction phase from the brain signals. The image regeneration technique has been experimentally tested on a variety of generative adversarial networks including the proposed model and the final reconstructed image samples are compared to assess the quality using various evaluation metrics. The Structural Similarity Index Measure metric for Capsule Generative Adversarial Network has achieved highest value i.e., 0.9203 and outperforms the other GAN variants and also indicates that the Capsule Generative Adversarial Network reconstructed the images similar to original images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Visually evoked brain signals guided image regeneration using GAN variants |
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Anwar, Shamama Bhattacharjee, Vandana Sahana, Sudip Kumar |
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