EEG Signal Processing for Brain Activity Indexing
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Solution Overview
Problem
Current methods for monitoring brain function, such as quantitative EEG, struggle to distinguish between changes in cortical input and brain state, leading to unclear physiological reflections and limited temporal resolution, making it difficult to assess brain activity accurately.
Innovation Solution
A method involving EEG signal processing using autoregressive moving average (ARMA) signal representation, pole and zero calculations, and infinite order autoregressive modeling to generate an index value representing brain activity, allowing for a more precise differentiation between cortical input and state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantitative EEG spectral analysis is used to monitor brain function, then brain activity can be assessed, but the method cannot distinguish between changes in cortical input and brain state
Solution Approach 1:
The patent segments the EEG signal analysis into two distinct components: cortical state (measuring the brain's inherent receptivity to input) and cortical input (measuring the level of neuronal input). This segmentation is achieved through specific signal processing techniques that separate these two physiological aspects, allowing each to be measured and displayed independently rather than as a single mixed metric.
Solution Approach 2:
The patent transforms the EEG signal parameters to extract specific features that represent cortical state and cortical input separately. By changing the analysis parameters from traditional spectral analysis to methods that can differentiate between these physiological sources, the system achieves greater measurement precision and physiological specificity simultaneously.
2Loss of information
If event related potentials are used to assess cortical input pathways, then information about input integrity can be obtained, but the temporal resolution is limited due to the need for sequentially presented stimuli
Solution Approach 1:
The patent employs continuous EEG signal analysis rather than discrete, sequentially presented stimuli. This allows for continuous monitoring of cortical input without the temporal delays inherent in presenting multiple stimuli in sequence, thereby maintaining high temporal resolution while still extracting meaningful information about input pathways.
Solution Approach 2:
The system performs preliminary signal processing and filtering on the continuous EEG signal to prepare it for analysis, allowing real-time assessment of cortical input without waiting for sequential stimulus presentations. This preliminary processing enables the system to extract input pathway information continuously from the ongoing signal.
3Loss of information
If early components of event related potentials are analyzed to assess cortical input, then input pathway integrity can be evaluated, but not all cortical areas are represented as some areas do not receive sensory information
Solution Approach 1:
The patent develops a universal monitoring system that can assess both cortical input and cortical state across all cortical areas simultaneously. Unlike methods that rely on sensory input pathways that may not reach certain cortical regions, this system uses EEG signals that reflect the functional state of all cortical areas, providing versatile coverage throughout the entire cortex.
Solution Approach 2:
The patent uses EEG signals as an intermediary that indirectly reflects the state of all cortical areas, including those that do not directly receive sensory input. By measuring the electrical activity generated by cortical neurons regardless of their input pathways, the system can assess the functional state of universal cortical regions through this intermediary measurement approach.
Data Source
AI summary
A method of displaying the activity of a brain, the method including the steps of: (i) obtaining an electroencephalogram (EEG) signal from the brain; (ii) segmenting said EEG signal into either contiguous or overlapping segments comprised of a sequential number of samples of said EEG signal; (iii) representing said EEG segments as a fixed order autoregressive moving average (ARMA) signal representation with an autoregressive order between 8 and 13 and a moving average order between 5 and 11; (iv) rewriting in z-domain notation said fixed order ARMA signal representation to obtain a z-domain representation; (v) generating AR coefficient data and MA coefficient data for said segments of said EEG signal for said fixed order ARMA signal representation: (vi) determining the poles and zeros of said z-domain representation for said segments of said EEG signal by substituting said coefficient data for said segments of said EEG signal into said z-domain representation; (vii) calculating the sum of the number of poles determined in step (vi); (viii) calculating the sum of the number of zeros determined in step (vi); (ix) representing said ARMA signal representation as an infinite order autoregressive (AR) model in z-domain notation; (x) determining autoregressive coefficient data for said infinite AR model from the AR and MA coefficient data generated in step (v) of said fixed order ARMA representation; (xi) determining the sum of the poles for said infinite order AR model for said segments of said EEG signal as either: (a) the first autoregressive coefficient of said finite order AR model; or (b) the difference of the sum of poles and the sum of zeros as determined respectively in steps (vii) and (viii); (xii) determining an index value representing the activity of the brain for said segments of said EEG signal by applying a discriminating function to the sum of the poles of said infinite AR model for said segments of said EEG signal as determined in step (xi); and (xiii) displaying said index value on display means.