Endosomatic EDA Cognitive Load Assessment Using Deviation Signals
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Solution Overview
Problem
Current methods for assessing cognitive load using endosomatic electrodermal activity (EDA) face challenges such as low amplitude bio-signals and the need for specific preprocessing, making it difficult to achieve accurate task classification, whereas exosomatic approaches are more prevalent despite their limitations.
Innovation Solution
A method and system utilizing a multichannel wearable endosomatic device to acquire bio-potential signals, derive deviation signals, extract statistical and spectral features, and use a Feature Discovery Platform to select optimal features for training a classifier to differentiate between high and low cognitive load tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If endosomatic EDA approach is used to measure cognitive load, then measurement reliability is improved, but device complexity and preprocessing requirements increase
Solution Approach 1:
The patent applies preliminary action by performing baseline subtraction and deviation signal derivation before feature extraction and classification. The system pre-processes the bio-potential signals by removing baseline components and computing deviation signals, which simplifies subsequent analysis and improves measurement reliability by eliminating systematic variations.
Solution Approach 2:
The patent extracts specific features (statistical and spectral) from the deviation signals obtained through endosomatic EDA. By taking out only the relevant features needed for cognitive load assessment from the complex bio-potential signals, the system reduces preprocessing complexity while maintaining measurement reliability.
2Reliability
If endosomatic EDA is used for cognitive load assessment, then physiological accuracy is improved, but signal amplitude decreases making detection difficult
Solution Approach 1:
The patent transforms the low-amplitude bio-potential signals into deviation signals by subtracting baseline values. This dimensionality change converts the measurement from absolute potential values to relative deviation values, enhancing the detectability of cognitive load-related variations while preserving physiological accuracy.
Solution Approach 2:
The patent changes the parameter being measured from absolute bio-potential values to deviation from baseline. By computing the difference between instantaneous signals and baseline signals, the system amplifies the relative changes associated with cognitive load while maintaining the physiological fidelity of the endosomatic EDA approach.
3Measurement precision
If feature selection is performed to improve classification accuracy, then cognitive load classification precision is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and selects specific statistical features (mean, standard deviation, skewness, kurtosis) and spectral features from the deviation signals. By taking out only the most discriminative features needed for cognitive load classification rather than using all possible features, the system achieves high classification accuracy (83%) while controlling computational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves accurate classification of cognitive load with an average accuracy of 83% using endosomatic EDA signals, outperforming traditional GSR methods, and demonstrates the feasibility of using endosomatic EDA for real-time cognitive load assessment.
Implementation Method 1
Electrodermal activity is defined as the variation of electrical characteristics of the skin, which is typically governed by the sweat gland activity. Sweat secretion, usually driven by autonomic nervous system, causes the variation in the electrical property of the skin.
Data Source
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AI summary
Direct usage of endosomatic EDA has multiple challenges for practical cognitive load assessment. Embodiments of the method and system disclosed provide a solution to the technical challenges in the art by directly using the bio-potential signals to implement endosomatic approach for assessment of cognitive load. The method utilizes a multichannel wearable endosomatic device capable of acquiring and combining multiple bio-potentials, which are biomarkers of cognitive load experienced by a subject performing a cognitive task. Further, extracts information for classification of the cognitive load, from the acquired bio-signals using a set of statistical and a set of spectral features. Furthermore , utilizes a feature selection approach to identify a set of optimum features to train a Machine Learning (ML) based task classifier to classify the cognitive load experienced by a subject into high load task and low load task.