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Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process.

Zaisheng Liu1, Fei Ni1, Rongpeng Li1

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Zheda Road 38, Hangzhou 310027, China.

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|November 11, 2021
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This summary is machine-generated.

This study uses persistent homology and electroencephalogram (EEG) to analyze brain activity during visual perception. Findings reveal distinct neural patterns for ordered versus disordered images, suggesting a method for digitizing consciousness signals.

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Computational Topology

Background:

  • Understanding neuropsychological characteristics of consciousness remains a challenge.
  • Extraction and analysis of human brain consciousness information require further investigation.
  • Gestalt psychology offers a framework for visual perception but lacks detailed neural correlates.

Purpose of the Study:

  • To investigate electroencephalogram (EEG) signal features associated with different conscious thoughts.
  • To extract physiologically meaningful brain features responding to image contours and shapes in Gestalt cognitive tests.
  • To explore the application of persistent homology analysis in understanding visual cognition.

Main Methods:

  • Combining persistent homology analysis with electroencephalogram (EEG) data.
  • Analyzing EEG signals evoked by ordered Gestalt (GST) images and random sequence diagrams (RSD).
  • Quantifying cognitively related neural correlates using persistent homology features.

Main Results:

  • Perceiving disordered images activates more frontal lobe brain regions than ordered images.
  • Persistence entropy of EEG data differs significantly between RSD and GST images across several frequency bands.
  • Topological analysis can partially separate human cognition of image shape and contour.

Conclusions:

  • Persistent homology features of EEG signals provide a method for digitizing neural signals while preserving signal features.
  • The study demonstrates the feasibility of analyzing human consciousness signals through topological data analysis.
  • Findings offer new research directions for feature extraction and analysis of human brain consciousness cognition.