Biosignal Data Transformation for Standardized Brain State Representations
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Solution Overview
Problem
Existing biosignal data analysis technologies face challenges in efficiently reducing dimensions, surfacing relevant features, and standardizing representations across different biosignal devices and users, while also dealing with artifacts and inter-subject/intra-subject variations.
Innovation Solution
A system and method that transforms biosignal data into a standardized brain state representation, using a representation model to segment beneficial and non-beneficial segments, enabling accurate biomarker prediction and real-time inference of mental commands for controlling external devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If biosignal data is processed using traditional dimensionality reduction methods, then computational complexity is reduced, but relevant features are not effectively surfaced and standardization across devices is lost
Solution Approach 1:
The patent segments the high-dimensional biosignal data into multiple frequency bands (e.g., delta, theta, alpha, beta, gamma) and spatial regions. This segmentation allows for targeted feature extraction in each segment while maintaining overall computational efficiency. The brain state representation is also segmented into beneficial and non-beneficial segments, enabling selective processing that reduces complexity without losing relevant features.
Solution Approach 2:
The patent transforms the biosignal data by changing parameters such as frequency domain representation (via FFT or wavelet transforms), spatial projection (onto standardized brain maps), and dimensionality (reducing from raw sensor data to condensed brain state representations). These parameter changes enable efficient processing while preserving and even enhancing relevant features through the transformation process.
2Adaptability or versatility
If biosignal data from different devices and users is standardized, then cross-device compatibility is improved, but inter-subject and intra-subject variations are lost
Solution Approach 1:
The patent introduces an intermediary representation layer called the standardized brain state representation that acts as a mediator between raw device-specific biosignal data and the final analysis. This intermediary transformation uses device-specific calibration data and subject-specific baselines to normalize data into a common framework while preserving individual characteristics through learned transformation parameters that are stored and applied consistently.
Solution Approach 2:
The patent performs preliminary actions during calibration phases where subject-specific baselines and device-specific transformation parameters are established before actual measurement. These preliminary transformations create personalized reference frames that allow subsequent measurements to be standardized across devices while maintaining individual subject characteristics. The calibration data is stored and reused to maintain both standardization and individuality.
3Measurement precision
If all segments of brain state representation are used for biomarker prediction, then prediction accuracy is improved, but computational resources and time are excessive
Solution Approach 1:
The patent extracts and separates beneficial segments of the brain state representation from non-beneficial segments through analysis of feature relevance to specific biomarkers. Only the beneficial segments that contain information relevant to the target biomarker are retained for prediction, while non-beneficial segments are discarded. This extraction process significantly reduces the data volume requiring processing while maintaining prediction accuracy for the specific biomarker of interest.
Solution Approach 2:
The patent applies local quality by treating different segments of the brain state representation differently based on their relevance to specific biomarkers. Rather than uniformly processing all segments, the system identifies and enhances processing of locally relevant segments (those containing biomarker-specific information) while reducing or eliminating processing of irrelevant segments. This localized approach optimizes computational resources according to the specific prediction task.
Data Source
AI summary
A method for biosignal data transformation can include: determining a set of biosignal data, determining a brain state representation (e.g., an embedding) based on the biosignal data, and determining a biomarker value based on the brain state representation. The method can optionally include training a model (e.g., training a model used to determine the brain state representation), and/or any other suitable steps. A system for biosignal data transformation can include a biosignal device and a computing system.