Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process
The neuropsychological characteristics inside the brain are still not sufficiently understood in previous Gestalt psychological analyses. In particular, the extraction and analysis of human brain consciousness information itself have not received enough attention for the time being. In this paper, w...
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2021
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oai:doaj.org-article:191954ae75f34ca591f7e0aa4863a76d2021-11-15T01:20:04ZPersistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process2040-230910.1155/2021/2334332https://doaj.org/article/191954ae75f34ca591f7e0aa4863a76d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2334332https://doaj.org/toc/2040-2309The neuropsychological characteristics inside the brain are still not sufficiently understood in previous Gestalt psychological analyses. In particular, the extraction and analysis of human brain consciousness information itself have not received enough attention for the time being. In this paper, we aim to investigate the features of EEG signals from different conscious thoughts. Specifically, we try to extract the physiologically meaningful features of the brain responding to different contours and shapes in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG). The experimental results show that more brain regions in the frontal lobe are involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images. Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) is significantly different from that evoked by the ordered Gestalt (GST) images in several frequency bands, which indicate that the human cognition of the shape and contour of images can be separated to some extent through topological analysis. This implies the feasibility to digitize the neural signals while preserving the whole and local features of the original signals, which are further verified by our extensive experiments. In general, this paper evaluates and quantifies cognitively related neural correlates by persistent homology features of EEG signals, which provides an approach to realizing the digitization of neural signals. Preliminary verification of the analyzability of human consciousness signals provides reliable research ideas and directions for the realization of feature extraction and analysis of human brain consciousness cognition.Zaisheng LiuFei NiRongpeng LiHonggang ZhangChang LiuJiefang ZhangSongyun XieHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021) |
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Medicine (General) R5-920 Medical technology R855-855.5 Zaisheng Liu Fei Ni Rongpeng Li Honggang Zhang Chang Liu Jiefang Zhang Songyun Xie Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process |
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The neuropsychological characteristics inside the brain are still not sufficiently understood in previous Gestalt psychological analyses. In particular, the extraction and analysis of human brain consciousness information itself have not received enough attention for the time being. In this paper, we aim to investigate the features of EEG signals from different conscious thoughts. Specifically, we try to extract the physiologically meaningful features of the brain responding to different contours and shapes in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG). The experimental results show that more brain regions in the frontal lobe are involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images. Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) is significantly different from that evoked by the ordered Gestalt (GST) images in several frequency bands, which indicate that the human cognition of the shape and contour of images can be separated to some extent through topological analysis. This implies the feasibility to digitize the neural signals while preserving the whole and local features of the original signals, which are further verified by our extensive experiments. In general, this paper evaluates and quantifies cognitively related neural correlates by persistent homology features of EEG signals, which provides an approach to realizing the digitization of neural signals. Preliminary verification of the analyzability of human consciousness signals provides reliable research ideas and directions for the realization of feature extraction and analysis of human brain consciousness cognition. |
format |
article |
author |
Zaisheng Liu Fei Ni Rongpeng Li Honggang Zhang Chang Liu Jiefang Zhang Songyun Xie |
author_facet |
Zaisheng Liu Fei Ni Rongpeng Li Honggang Zhang Chang Liu Jiefang Zhang Songyun Xie |
author_sort |
Zaisheng Liu |
title |
Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process |
title_short |
Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process |
title_full |
Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process |
title_fullStr |
Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process |
title_full_unstemmed |
Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process |
title_sort |
persistent homology-based topological analysis on the gestalt patterns during human brain cognition process |
publisher |
Hindawi Limited |
publishDate |
2021 |
url |
https://doaj.org/article/191954ae75f34ca591f7e0aa4863a76d |
work_keys_str_mv |
AT zaishengliu persistenthomologybasedtopologicalanalysisonthegestaltpatternsduringhumanbraincognitionprocess AT feini persistenthomologybasedtopologicalanalysisonthegestaltpatternsduringhumanbraincognitionprocess AT rongpengli persistenthomologybasedtopologicalanalysisonthegestaltpatternsduringhumanbraincognitionprocess AT honggangzhang persistenthomologybasedtopologicalanalysisonthegestaltpatternsduringhumanbraincognitionprocess AT changliu persistenthomologybasedtopologicalanalysisonthegestaltpatternsduringhumanbraincognitionprocess AT jiefangzhang persistenthomologybasedtopologicalanalysisonthegestaltpatternsduringhumanbraincognitionprocess AT songyunxie persistenthomologybasedtopologicalanalysisonthegestaltpatternsduringhumanbraincognitionprocess |
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1718428909245038592 |