A preference detection method, system, device and medium based on electroencephalogram signals
By collecting and analyzing multi-sensory EEG signals, extracting unique features using a deep learning model, and combining them with a comprehensive relaxation index, the problem of limited sensory coverage and poor cross-platform adaptability in existing technologies has been solved, achieving high-precision, multi-dimensional preference detection and cross-platform application.
Patent Information
- Application Number
- CN202610835069.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for EEG signal preference detection suffer from problems such as limited sensory coverage, lack of specificity in feature extraction, and poor cross-platform adaptability, resulting in low preference detection accuracy.
By collecting EEG signals under multiple sensory stimuli, we can extract specific preference features for vision, hearing, smell, tactile sensation, and temperature sensation. We can then use a deep learning model for feature extraction and decision-making, combine it with a comprehensive relaxation index to determine the optimal preferred sensory experience, and support cross-platform applications.
It achieves full-dimensional sensory preference detection, improving the comprehensiveness, accuracy, and practicality of detection, supporting cross-platform applications, and adapting to different scenarios and individual user differences.
Smart Images
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