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Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features.

Yuting Wang1,2, Shujian Wang1,2, Ming Xu1,2,3

  • 1Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China.

International Journal of Environmental Research and Public Health
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for landscape recognition using aerial videos and electroencephalogram (EEG) signals. Machine learning models achieved high accuracy in classifying landscapes based on brainwave patterns, offering a new way to quantify human perception.

Keywords:
electroencephalogram (EEG) featureslandscape perceptionmachine learningunmanned aerial vehicle (UAV)

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Area of Science:

  • Neuroscience
  • Environmental Science
  • Computer Science

Background:

  • Human perception of landscapes is complex and subjective.
  • Objective methods for landscape evaluation are needed.
  • Electroencephalogram (EEG) technology captures brain activity related to sensory experiences.

Purpose of the Study:

  • To develop and validate a new method for landscape recognition and evaluation.
  • To investigate the correlation between EEG signals and landscape types.
  • To quantify human perception of different landscapes using neuroimaging and machine learning.

Main Methods:

  • Seven distinct landscape types were selected for video stimuli.
  • Electroencephalogram (EEG) signals were recorded from participants viewing landscape videos.
  • EEG features (MAS, PSD, DE, DASM, RASM, DCAU) were extracted across five frequency bands.
  • Four machine learning classifiers (BP, KNN, RF, SVM) were employed for landscape classification.

Main Results:

  • Support Vector Machine (SVM) and Random Forest (RF) classifiers achieved high landscape recognition accuracies (98.24% and 96.72%, respectively).
  • Frequency domain EEG features (MAS, PSD, DE) outperformed spatial domain features in classification accuracy.
  • The gamma frequency band showed the highest average classification accuracy (98.24%).

Conclusions:

  • Multi-channel EEG signals can effectively identify and classify landscape perception.
  • This neuro-based approach offers a novel method for the objective quantification of human perception of environments.
  • The findings have implications for landscape planning, environmental psychology, and human-computer interaction.