ECoG Signal Compression Preprocessing Using Neighbor Electrode Prediction
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Solution Overview
Problem
Existing methods for compressing electrocorticographic (ECoG) measurement data over time are inefficient, requiring significant computational resources and often result in loss of information, while failing to faithfully track temporal evolution with low delay.
Innovation Solution
A compression preprocessing method that uses a prediction function based on neighboring electrodes' past signals to transform raw ECoG data into preprocessed data with lower entropy, followed by generic entropy encoding, minimizing computational resources and preserving information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression methods (video/image compression algorithms) are applied to ECoG data, then data volume is reduced, but computational resources are excessively consumed and information loss occurs
Solution Approach 1:
The patent segments the ECoG data processing by separating the compression into two distinct stages: (1) a specialized preprocessing stage that exploits temporal and spatial correlations specific to neural signals, and (2) a generic entropy encoding stage. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining compression effectiveness.
Solution Approach 2:
The patent applies preliminary action by performing correlation-based preprocessing on the ECoG data before applying generic entropy encoding. This preliminary step transforms the raw neural signals into a form that has reduced redundancy and lower entropy, making the subsequent compression more efficient and requiring fewer computational resources.
2Quantity of substance
If conventional compression methods are used on ECoG data, then data volume is reduced, but information loss occurs
Solution Approach 1:
The patent implements feedback by using the statistical properties and correlation structures inherent in ECoG data to guide the preprocessing transformation. The method adapts to the specific characteristics of the neural signals being compressed, ensuring that the transformation preserves the essential information content while removing redundant correlations.
Solution Approach 2:
The patent applies parameter changes by transforming the ECoG data into a different representation space through the correlation-based preprocessing step. This transformation changes the statistical parameters of the data (reducing entropy and redundancy) while maintaining the underlying information, enabling more efficient compression without information loss.
3Productivity
If generic entropy encoding is applied directly to raw ECoG data, then compression is achieved, but compression rate is insufficient
Solution Approach 1:
The patent performs preliminary action by applying a correlation-based preprocessing transformation to the raw ECoG data before entropy encoding. This preliminary step exploits the temporal and spatial correlations in neural signals to reduce the data entropy, thereby significantly improving the compression rate achieved by the subsequent generic entropy encoding stage.
Solution Approach 2:
The patent introduces an intermediary preprocessing stage that acts as a mediator between the raw ECoG data and the generic entropy encoder. This intermediary transformation converts the raw neural signals into a preprocessed form with reduced redundancy, enabling the entropy encoder to achieve higher compression rates without increasing its own complexity.
Data Source
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AI summary
A method for preprocessing compression of raw data ui(tN) of time-varying electrocorticographic (ECoG) signal measurements using electrodes disposed in direct contact with a cortex comprises an effective compression preprocessing step in which each raw signal ui(tN) acquired by the observed electrode i is transformed into a preprocessed signal εi(tN) equal to the difference between a first term and a second term and suitable for a second entropic encoding step. The first term is equal to the raw signal acquired at electrode i at current time tN, and the second term is a prediction function fi which depends on past raw signals observed in the near past at at least neighboring electrodes σi( j) of the observed electrode or at most in neighboring electrodes σi(j) of the observed electrode i and in the observed electrode