Residential Outdoor Landscape Node Analysis Using EEG Adjacency Networks
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Solution Overview
Problem
Existing methods for analyzing landscape approach motivation in residential outdoor environments are subjective, lack integration with cognitive feedback, and fail to account for spatio-temporal changes, leading to inaccurate and inefficient decision-making for environmental renewal and governance.
Innovation Solution
A decision-making method and system that combines brain cognitive tests, EEG signals, and spatial analysis to create a landscape approach motivation decision-making matrix, incorporating evolution and similarity states of landscape nodes, and builds a distribution adjacency network for accurate environmental classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If user self-report methods are used for cognitive data collection, then data collection is simple and low-cost, but accuracy and reliability are reduced due to memory bias and personal interpretation
Solution Approach 1:
The patent replaces subjective self-report methods with objective physiological measurement systems (EEG, eye-tracking, biometric sensors) to capture cognitive data. This substitution eliminates memory bias and personal interpretation while maintaining systematic data collection, thereby improving measurement precision without completely abandoning structured assessment approaches.
Solution Approach 2:
The patent introduces physiological markers and biometric indicators as intermediary measures between the user's internal cognitive state and the external measurement system. These intermediaries (brain waves, pupil dilation, skin conductance) provide objective proxies for cognitive processes, enabling accurate measurement without relying on direct self-reporting.
2Device complexity
If static analytical methods are used, then analysis process is simplified, but spatial fluidity and temporal evolution of landscape environments cannot be fully grasped
Solution Approach 1:
The patent transitions from static landscape analysis to dynamic assessment by continuously monitoring physiological responses as users move through and interact with the environment. The system captures temporal evolution of cognitive states and spatial variations in landscape perception, enabling analysis of how cognitive responses change over time and space rather than providing a single static snapshot.
Solution Approach 2:
The patent adds temporal and spatial dimensions to traditional landscape analysis by incorporating continuous physiological monitoring data that varies with user movement and environmental context. This transforms the analysis from two-dimensional static evaluation to multi-dimensional dynamic assessment, capturing the fluidity of user experience across different locations and time points.
3Measurement precision
If comprehensive cognitive testing and spatial analysis are integrated, then decision-making accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple assessment components (cognitive testing, physiological monitoring, spatial analysis, behavioral observation) into an integrated decision-making system. By combining these previously separate functions into a unified platform, the system achieves comprehensive and accurate environmental assessment while managing complexity through integrated architecture rather than separate standalone systems.
Data Source
AI summary
A decision-making method and system for a residential outdoor environment based on landscape approach motivation are provided. The method includes: collecting engineering drawing data and environmental element material test data of the residential outdoor environment, and translating test results of residential outdoor environmental elements into landscape approach motivation structural information; delineating landscape nodes in the residential outdoor environment, building an initial decision-making matrix of approach motivation change states of the environmental elements corresponding to the landscape nodes, and decomposing the landscape approach motivation change states into an evolution state and similarity state, building a decision-making matrix of a landscape approach motivation distribution adjacency network, and calculating a driving degree of the distribution adjacency network in the residential outdoor environment; distinguishing level patterns of the landscape approach motivation change states, generating a decision-making network of the residential outdoor environment, and clarifying priorities of renewal and management for the residential outdoor environment.