EEG Frequency-Band AI Decoding for Thought-Based Action Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing EEG systems struggle to accurately process multiple frequencies of brain electrical signals to associate them with specific thoughts, ideas, or actions, limiting their effectiveness in communication and device operation, particularly for impaired users.
Innovation Solution
An EEG system that utilizes multiple frequency bands, decomposes EEG signals into constituent waveforms, and employs machine learning-artificial intelligence models to analyze these waveforms, enabling accurate interpretation and execution of user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional EEG systems extract a single frequency at a time from the combination of signals, then the device complexity is reduced, but the measurement precision and ability to associate signals with specific thoughts is deteriorated
Solution Approach 1:
The patent segments the complex EEG signal processing task by dividing it into multiple frequency band analyses. Each frequency band (delta, theta, alpha, beta, gamma) is processed separately through dedicated ML-AI models, allowing the system to handle multiple frequencies simultaneously without overwhelming complexity. This segmentation enables precise association of specific frequency patterns with particular thoughts or actions while maintaining manageable system architecture.
2Measurement precision
If traditional systems process multiple frequencies of EEG signals, then the measurement precision improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-training multiple ML-AI models offline for different frequency bands before actual EEG signal analysis. During real-time operation, the pre-trained models can rapidly process incoming EEG signals without requiring extensive computational resources or time. The decomposition of EEG signals into frequency bands is performed efficiently using Fast Fourier Transform (FFT), enabling quick classification of user intentions while maintaining high precision in thought association.
3Reliability
If the system uses multiple ML-AI models for different frequency bands, then the reliability of action validation improves, but the device complexity increases
Solution Approach 1:
The patent merges the outputs of multiple frequency band analyses through a validation mechanism that combines results from delta, theta, alpha, beta, and gamma band models. This merging approach validates user actions by checking consistency across multiple frequency interpretations, significantly improving reliability. The system integrates these multiple model outputs into a unified decision-making process that can accurately determine user intentions while managing the complexity through structured output aggregation and validation protocols.
Data Source
AI summary
A method is provided. The method comprises obtaining, using electroencephalogram (EEG) sensors, a first set of EEG signals that comprises a plurality of first waveforms, and each of the plurality of first waveforms is associated with a frequency band from a plurality of frequency bands; training a plurality of machine learning-artificial intelligence (ML-AI) models using the first set of EEG signals, wherein each of the plurality of ML-AI models is trained for a different frequency band; obtaining, using the EEG sensors, a second set of EEG signals, wherein the second set of EEG signals comprises a plurality of second waveforms; inputting each of the plurality of second waveforms associated with the frequency band into a corresponding ML-AI model associated with the respective frequency band to generate a plurality of outputs; and performing one or more actions based on the plurality of outputs.