EEG Signal Preprocessing via Artifact Removal and Segmentation
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Solution Overview
Problem
Existing EEG data preprocessing systems lack the ability to adapt integrally to the preprocessing requirements of EEG data under different circumstances, necessitating specialized programming and lacking comprehensive noise and artifact removal capabilities.
Innovation Solution
An EEG signal preprocessing method involving artifact and noise removal operations, including blind source separation, canonical correlation analysis, deep learning models, and filtering techniques, to obtain pure EEG signals, which are then segmented and analyzed in time and frequency domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If professionals conduct specialized programming processing for EEG signal preprocessing, then the preprocessing can be tailored to specific requirements, but the complexity and time consumption increase significantly
Solution Approach 1:
The patent implements a universal preprocessing system that can handle multiple EEG processing scenarios through a unified architecture. The system provides standardized interfaces and integrated algorithms that adapt to different preprocessing needs without requiring separate programming for each case, thus achieving versatility while reducing complexity.
Solution Approach 2:
The system enables automatic artifact removal and noise filtering through integrated algorithms that self-adjust to the input data characteristics. The preprocessing pipeline automatically identifies and removes artifacts without requiring manual programming intervention for each specific case, reducing the burden on professionals while maintaining adaptability.
2Measurement precision
If comprehensive artifact removal and noise removal operations are implemented, then EEG data quality improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary filtering and artifact removal operations in real-time during data acquisition. By preprocessing the signal immediately upon collection and removing obvious artifacts early in the pipeline, the system reduces the computational burden for subsequent detailed analysis, thereby improving data quality without proportionally increasing total processing time.
Solution Approach 2:
The preprocessing operations are divided into multiple sequential stages including initial filtering, artifact removal, and refined noise reduction. This segmentation allows the system to apply different processing intensities at different stages, achieving high data quality while managing computational resources efficiently across the processing timeline.
3Measurement precision
If EEG signals from bilateral mastoid point locations and electrooculogram channels are removed, then artifact contamination is reduced, but the loss of potentially useful signal information occurs
Solution Approach 1:
The system selectively extracts and removes only the artifact-contaminated components from bilateral mastoid and electrooculogram channels while preserving the clean EEG signal portions. Through component analysis, the system identifies and separates artifact sources from neural signals, removing only the harmful artifacts and retaining useful information.
Solution Approach 2:
The system utilizes the artifact-containing channels as additional sources of information about brain activity. Rather than completely discarding mastoid and electrooculogram data, the system processes these channels to extract both artifact components for removal and valid neural signals for analysis, converting previously harmful data into beneficial information sources.
Data Source
AI summary
Provided is an electroencephalogram (EEG) signal preprocessing method, system and terminal device. The method includes: receiving original EEG signals collected by an EEG collection device, data structure of the original EEG signals corresponds to pre-positioned EEG signal channel locations; performing artifact removal and noise removal operations on the collected original EEG signals to obtain pure EEG signals; obtaining event marks of the original EEG signals, dividing the pure EEG signals into multiple segments each containing EEG signals within preset length of time range before and after an event occurs; obtaining, for the segments, the preset analysis indicators to be extracted, performing time-domain analysis to extract time-domain analysis indicators, and performing frequency-domain analysis to extract frequency-domain analysis indicators. The technical solution can improve the processing efficiency of removing noise and artifacts from the original EEG data, thereby improving the quality of EEG data.