Cognitive State Prediction From Non-Neural Signals Using Latent Mapping
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Solution Overview
Problem
Current approaches for predicting cognitive states rely solely on neural or non-neural physiological data, resulting in invasiveness, impracticality, or limited accuracy, and lack a reliable method to transform between these data types effectively.
Innovation Solution
A system using a physiological data based neural network and a neural data based neural network, connected by an encoder-decoder, learns a transformation between hidden layers to predict cognitive states from non-neural physiological data, eliminating the need for invasive EEG sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG data is used to predict cognitive states, then prediction accuracy is improved, but device invasiveness increases
Solution Approach 1:
The patent introduces non-neural physiological sensors as an intermediary to capture cognitive state information without direct brain contact. These sensors measure peripheral physiological signals (heart rate, skin conductance, respiration) that correlate with cognitive states, providing a non-invasive alternative to EEG while maintaining prediction capability.
Solution Approach 2:
The patent replaces the mechanical/electrical invasive EEG system with a non-invasive physiological monitoring system. Instead of placing electrodes on the scalp to directly read neural signals, the system uses optical, electrical, and mechanical sensors to detect peripheral physiological responses that indirectly reflect cognitive state.
2Object-affected harmful factors
If non-neural physiological data is used to predict cognitive states, then device invasiveness is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent merges multiple non-neural physiological data streams (heart rate, skin conductance, respiration, eye tracking) into a unified predictive model. By combining these diverse peripheral signals through machine learning algorithms, the system achieves improved prediction accuracy while maintaining non-invasive measurement.
Solution Approach 2:
The patent creates a universal physiological monitoring system that can detect multiple cognitive states (stress, distraction, engagement, fatigue) using the same non-invasive sensor suite. The system adapts to different cognitive state predictions by processing the same peripheral physiological data through different analytical models.
3Measurement precision
If both EEG and non-neural physiological data are used, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts and focuses solely on non-neural physiological data, eliminating the need for EEG hardware while maintaining adequate prediction accuracy. This extraction approach simplifies the system by removing complex invasive components while retaining the essential predictive capability through peripheral physiological monitoring.
Data Source
AI summary
A system for predicting one or more specific cognitive states of an individual based on non-neural physiological data collected by one or more non-neural physiological sensors includes one or more controllers in electronic communication with the one or more non-neural physiological sensors. The one or more controllers include a physiological data based neural network, a neural data based neural network, and an encoder-decoder that learns a transformation between a hidden layer of the physiological data based neural network and a hidden layer of the neural data based neural network during a training phase of the system.