EEG Sample Entropy Brain Fatigue Detection
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Solution Overview
Problem
Current methods for determining brain fatigue are subjective and lack objectivity, with subjective evaluations being prone to individual variability and objective methods struggling to quantify fatigue accurately due to uncertain relationships between physical and biochemical indexes.
Innovation Solution
A method and apparatus utilizing electroencephalogram (EEG) signals and the sample entropy algorithm to objectively assess brain fatigue by collecting, filtering, and quantifying EEG signals, determining fatigue states based on predetermined entropy value ranges, and generating reminding information for the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective evaluation method is used to determine brain fatigue, then the evaluation can be conducted through questionnaire, but the scoring criterion is vulnerable to subjective factors and not easily unified
Solution Approach 1:
The patent replaces the subjective mechanical questionnaire evaluation system with an objective physiological signal detection system. By using EEG signals and sample entropy algorithm, the invention substitutes human subjective judgment with automated signal processing, thereby eliminating subjective factors while maintaining ease of operation through non-invasive headwear collection.
Solution Approach 2:
The patent introduces EEG signals and sample entropy algorithm as intermediaries between the brain fatigue state and the evaluation result. Instead of directly asking subjects to self-evaluate, the system uses physiological signals as an objective mediator to quantify fatigue levels, achieving unified and standardized measurement criteria.
2Measurement precision
If objective evaluation method is used to determine brain fatigue, then physical and biochemical indexes can be observed by instruments, but the relationship between these indexes and fatigue degree is uncertain with great individual differences
Solution Approach 1:
The patent changes the evaluation parameter from diverse physical and biochemical indexes to a specific EEG signal parameter (sample entropy). By focusing on one robust parameter that has been proven to correlate with brain fatigue, the system achieves both measurement precision and adaptability across different individuals, eliminating the uncertainty of individual differences associated with multiple indexes.
Solution Approach 2:
The patent extracts the most critical and reliable feature from complex EEG signals - the sample entropy value. Instead of analyzing multiple physical and biochemical indexes, the invention isolates and focuses on this single key parameter that effectively represents brain fatigue state, thereby simplifying the evaluation while maintaining precision and adaptability.
3Measurement precision
If traditional objective evaluation methods are used, then multiple physical and biochemical indexes need to be measured, but it is difficult to be objective and quantified
Solution Approach 1:
The patent extracts only the essential EEG signal feature (sample entropy) needed for fatigue evaluation, eliminating the need to measure multiple physical and biochemical indexes. This extraction approach simplifies the device requirements while maintaining objective and quantified measurement capabilities.
Solution Approach 2:
The patent creates a universal evaluation system based on EEG signals that can assess brain fatigue across different individuals and conditions. The sample entropy method serves as a multi-functional tool that provides objective quantification without requiring multiple specialized instruments for different physiological indexes, thereby reducing device complexity.
Data Source
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AI summary
According to an embodiment of the present invention, there is provided a method for determining whether the brain is fatigued, the method comprising: collecting electroencephalogram signals of a testee; quantifying the electroencephalogram signals utilizing a sample entropy algorithm to obtain a final sample entropy value of the electroencephalogram signals; determining whether the brain of the testee is fatigued according to the final sample entropy value, wherein it is determined that the brain of the testee is in a fatigue state when the final sample entropy value is within a predetermined range. According to an embodiment of the present invention, there is also provided an apparatus for executing the method hereinabove. A sample entropy algorithm may be utilized to calculate a sample entropy value of electroencephalogram signals, and the degree of fatigue of the testee's brain can be objectively and accurately evaluated according to the sample entropy value.