Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding
Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual col...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022035757 |
| _version_ | 1828071273601695744 |
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| author | Yijia Wu Yanni Zhang Yanjing Mao Kaiqiang Feng Donglai Wei Liang Song |
| author_facet | Yijia Wu Yanni Zhang Yanjing Mao Kaiqiang Feng Donglai Wei Liang Song |
| author_sort | Yijia Wu |
| collection | DOAJ |
| description | Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex (V1) regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in V1 can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location. |
| first_indexed | 2024-04-11T00:50:54Z |
| format | Article |
| id | doaj.art-2e223c89d0d84e778b562854be0c878a |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-11T00:50:54Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-2e223c89d0d84e778b562854be0c878a2023-01-05T08:39:47ZengElsevierHeliyon2405-84402022-12-01812e12287Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decodingYijia Wu0Yanni Zhang1Yanjing Mao2Kaiqiang Feng3Donglai Wei4Liang Song5Academy for Engineering & Technology, Fudan University, Shang Hai, China; Shanghai East-bund Institute on Networking Systems of AI, Shang Hai, China; Corresponding author.Shanghai East-bund Institute on Networking Systems of AI, Shang Hai, ChinaAcademy for Engineering & Technology, Fudan University, Shang Hai, ChinaAcademy for Engineering & Technology, Fudan University, Shang Hai, ChinaAcademy for Engineering & Technology, Fudan University, Shang Hai, ChinaAcademy for Engineering & Technology, Fudan University, Shang Hai, China; Shanghai East-bund Institute on Networking Systems of AI, Shang Hai, ChinaVisual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex (V1) regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in V1 can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.http://www.sciencedirect.com/science/article/pii/S2405844022035757ComputerMachine learningDecodingReconstructingEEGVisual |
| spellingShingle | Yijia Wu Yanni Zhang Yanjing Mao Kaiqiang Feng Donglai Wei Liang Song Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding Heliyon Computer Machine learning Decoding Reconstructing EEG Visual |
| title | Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding |
| title_full | Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding |
| title_fullStr | Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding |
| title_full_unstemmed | Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding |
| title_short | Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding |
| title_sort | reconstructing sources location of visual color cortex by the task irrelevant visual stimuli through machine learning decoding |
| topic | Computer Machine learning Decoding Reconstructing EEG Visual |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022035757 |
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