EEG Data Frame Reconstruction Using Surrogate Sequences
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Solution Overview
Problem
Existing methods for handling missing or corrupted electroencephalogram (EEG) data sequences, such as replacing them with copies, result in large analysis errors due to inadequate signal processing, especially in real-time applications where data resending is not practical.
Innovation Solution
A method involving the formation of a neutral data sequence using surrogate algorithms to optimize the selection and insertion of surrogate data sequences, ensuring minimal disturbance to the EEG analysis by maintaining statistical and spectral properties, and using artificial intelligence for real-time prediction and correction of missing or corrupted data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a lost or contaminated data sequence is replaced by a copy of a properly received data sequence, then the data frame can be completed for analysis, but the analysis error increases significantly
Solution Approach 1:
The patent uses copying by creating surrogate data sequences that replicate the statistical and spectral properties of the original EEG data. Instead of directly copying existing data segments (which causes large analysis errors), the system generates new surrogate sequences that mimic the characteristics of missing or contaminated data, thereby completing the data frame while maintaining analysis accuracy.
2Reliability
If data is resent to replace lost or contaminated sequences, then data quality improves, but real-time analysis requirements cannot be met
Solution Approach 1:
The patent applies preliminary action by pre-generating surrogate data sequences that can immediately replace lost or contaminated data without requiring retransmission. The system prepares replacement data in advance using statistical and spectral modeling, allowing real-time analysis to proceed without waiting for resent data, thus eliminating processing delays while maintaining data quality.
3Measurement precision
If surrogate algorithms are used to generate neutral data sequences, then analysis error is reduced, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by adjusting the complexity parameters of surrogate algorithms based on the specific characteristics of the EEG data and the nature of missing or contaminated segments. The system dynamically selects and configures surrogate generation parameters (such as spectral density, statistical moments, and temporal correlations) to achieve accurate replacements while optimizing computational efficiency for real-time processing.
Data Source
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AI summary
A method of forming modifying data related to a data sequence for a data frame (100) including electroencephalogram data where the modifying data is formed by selecting: one sequence from at least one surrogate data sequence (662) for a neutral data sequence (500) that is for replacing a missing or corrupted data sequence of the electroencephalogram data, or a surrogate algorithm (652), which is for generating at least one surrogate data sequence (662), which includes the neutral data sequence (500). The selection is based on an optimization comparison (606) between a first data and a second data in order to limit disturbance caused in case the neutral data sequence (500) is applied to the data frame (100). The first data comprises reference data or a reference algorithm for generating the reference data. The second data comprises at least one result formed by applying a result algorithm, which provides characterizing information on the data frame (100) including the electroencephalogram data, to the electroencephalogram data with the at least one surrogate data sequence (662) replacing the missing or corrupted data sequence of the electroencephalogram data, or the result algorithm based on the electroencephalogram data and the surrogate algorithm (652).