Brain Modeling via Dimensionality Reduction and Feature Extraction
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Solution Overview
Problem
Current bio-signal collection and analysis methods lack the ability to effectively model and predict individual brain states and responses to stimuli, limiting their application in brain-computer interfaces and therapeutic applications.
Innovation Solution
A computer-implemented method that receives time-coded bio-signal data, projects it into a lower dimensioned feature space, extracts features, generates a training dataset, trains a brain model using a processor, and predicts brain states, allowing for feedback stimulus generation to achieve a target brain state, with the option to incorporate non-bio-signal data and utilize machine learning techniques like neural networks and generative adversarial networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bio-signal collection and analysis methods are used, then the system is simple to implement, but the ability to model and predict individual brain states is insufficient
Solution Approach 1:
The patent segments the brain modeling process into distinct components: data collection from multiple sources, feature extraction, model training, and prediction generation. This segmentation allows each component to be optimized independently while maintaining overall system reliability for brain state prediction.
Solution Approach 2:
The patent introduces intermediate processing layers including feature extraction mechanisms and data transformation steps that mediate between raw bio-signal data and the final prediction model. These intermediaries enable complex modeling capabilities while managing system complexity through structured data flow.
2Manufacturing precision
If personalized brain modeling is implemented, then therapeutic intervention effectiveness is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts relevant features from large volumes of multi-source data including bio-sensors, wearables, and electronic health records. This extraction process isolates the most predictive features for personalized brain modeling, reducing the effective data volume that needs to be processed while maintaining treatment precision.
Solution Approach 2:
The patent performs preliminary data processing, cleaning, and feature extraction before model training. This preliminary action prepares the data in advance, reducing the processing burden during actual model execution and enabling precise personalized treatment protocols.
3Measurement precision
If multi-source data integration is used, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data integration framework that can process multiple data types from diverse sources including bio-sensors, wearables, and electronic health records. This multi-functional system handles different data formats and sources through standardized processing pipelines, improving measurement precision while managing complexity through unification.
Solution Approach 2:
The patent transforms multi-source data into a unified feature space, changing the dimensional representation of data from multiple separate sources into an integrated multi-dimensional feature vector. This dimensional transformation enables accurate brain state measurement while simplifying the integration process through mathematical unification.
Data Source
AI summary
Brain modelling includes receiving time-coded bio-signal data associated with a user; receiving time-coded stimulus event data; projecting the time-coded bio-signal data into a lower dimensioned feature space; extracting features from the lower dimensioned feature space that correspond to time codes of the time-coded stimulus event data to identify a brain response; generating a training data set for the brain response using the features; training a brain model using the training set, the brain model unique to the user; generating a brain state prediction for the user output from the trained brain model, and automatically computing similarity metrics of the brain model as compared to other user data; and inputting the brain state prediction to a feedback model to determine a feedback stimulus for the user, wherein the feedback model is associated with a target brain state.