EEG Analysis System Using Neural Networks to Filter Artifacts
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Solution Overview
Problem
Current EEG analysis systems fail to accurately distinguish between true brain activity and artifacts, requiring expert assessment and struggling to quickly and effectively interpret vast amounts of brain activity data.
Innovation Solution
A system comprising electrodes, amplifiers, processors, and displays that processes EEG signals using neural network algorithms to filter out artifacts, organize spike detections, and provide a summarized analysis, allowing for dynamic sensitivity adjustment and visualization of spike foci for improved detection and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network algorithms are used to filter artifacts from EEG signals, then the ability to distinguish true brain activity from artifacts is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent introduces an intermediary processing layer (neural network algorithms) between the raw EEG signal acquisition and the final interpretation. This intermediary automatically filters artifacts and identifies spike patterns, resolving the contradiction by improving detection accuracy while managing system complexity through automated processing rather than manual analysis.
Solution Approach 2:
The patent replaces the mechanical/manual system of expert visual inspection with an automated computational system using neural network algorithms. This substitution improves measurement precision by consistently identifying patterns that may be missed by human reviewers while transforming the complex task of artifact rejection into an automated computational process.
2Loss of information
If comprehensive EEG analysis is performed on all detected signals, then the completeness of brain activity interpretation is improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary automated analysis of EEG signals using neural network algorithms to identify and flag potential artifacts and spike patterns before detailed review. This preliminary action filters out obvious artifacts and organizes potential findings, allowing clinicians to focus their time on reviewing only the most significant cases while maintaining information completeness.
Solution Approach 2:
The patent segments the EEG analysis process into distinct automated stages: artifact detection, spike identification, pattern classification, and prioritization. This segmentation allows the system to process comprehensive data efficiently by handling different aspects of analysis in separate automated modules, reducing overall analysis time while maintaining completeness.
3Measurement precision
If high sensitivity settings are used to detect all potential spikes, then the detection completeness is improved, but the number of false positive detections increases
Solution Approach 1:
The patent implements feedback mechanisms where the neural network algorithms continuously adjust detection parameters based on the characteristics of detected signals. When potential spikes are detected, the system provides feedback to refine the analysis, allowing high sensitivity detection while using the feedback loop to filter out false positives through pattern recognition and contextual analysis.
Solution Approach 2:
The patent dynamically changes detection parameters based on the specific characteristics of the EEG recording being analyzed. Rather than using fixed high sensitivity settings, the system adjusts parameters such as detection thresholds and time windows based on the recorded signal's amplitude, frequency content, and background activity, thereby maintaining detection completeness while reducing false positives.
Data Source
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AI summary
A method (1000) and system (20) for analyzing EEG data is disclosed herein. A processed EEG recording (200) is analyzed to produce a parameter for the EEG. The EEG recording (200) is analyzed to organize a plurality of detections by spike focus, to determine a relative frequency based on a count of detections by spike focus, to average a plurality of detections by spike focus, and the like.