EEG Data Harmonisation via Hardware Filter Weighting
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Solution Overview
Problem
Existing methods struggle to compare and validate EEG data collected from different devices due to differences in hardware filters, which affect the spectral content, especially in low and high frequency ranges.
Innovation Solution
The method involves transforming data from a new machine of a first type to be comparable to a reference machine of a second type by applying machine-type specific weights to the spectral content data. These weights are determined based on the filter frequency domain spectra of the hardware filters of both machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If software filters are used to attempt comparability across different machine types, then time-domain analyses and visual inspection can be improved, but frequency-domain analyses are worsened because the software filters alter the power spectrum in such a way that analyses may no longer be meaningful
Solution Approach 1:
The invention extracts the hardware filter characteristics from each machine type and separates them from the raw data. By identifying and removing the filter-induced spectral modifications, the method recovers the true underlying signal spectrum, enabling accurate frequency-domain analysis while maintaining ease of operation across different machine types
Solution Approach 2:
The invention introduces an intermediary correction process that acts between the raw data and the final analysis. This correction step uses the identified hardware filter characteristics to adjust the spectral content, serving as a mediator that translates data from different machine types into a common reference frame without distorting the underlying signal
2Productivity
If data from different machines with different hardware filters are used directly for analysis, then data collection efficiency is improved, but data comparability and validation are worsened because differences in hardware filters lead to differences in spectral content
Solution Approach 1:
The invention creates a universal correction framework that can handle data from multiple different machine types with different hardware filter configurations. By establishing a reference machine and developing correction weights based on filter characteristics, the method enables any machine type to produce comparable results, making the system multi-functional across diverse hardware platforms
Solution Approach 2:
The invention changes the spectral parameters of the collected data by applying correction weights that compensate for hardware filter differences. This parameter transformation adjusts the power spectrum to remove filter-induced variations, enabling reliable comparison and validation of data collected efficiently from multiple different machines
Data Source
AI summary
A method for harmonising data from a new machine of a first type with data from a reference machine of a second type. The method comprises receiving a new frequency domain spectrum of a process performed by the new machine and determining a harmonised new frequency domain spectrum of the process performed by the new machine by applying a set of harmonising new-machine specific weights to the new frequency domain spectrum, wherein the set of harmonising new machine-specific weights have been determined based on a filter frequency domain spectrum of a hardware filter of the new machine, and a filter frequency domain spectrum of a hardware filter of the reference machine. The harmonised new frequency domain spectrum is harmonised to a corresponding frequency domain spectrum of the process when the process is performed by the reference machine.