EEG Intent Translation for Computer Control and Mild TBI Prediction
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Solution Overview
Problem
Existing brain-computer interface (BCI) technologies face challenges in efficiently translating complex neural signals to perform specific computing-device operations, and there is a need for improved methods to diagnose traumatic brain injuries (TBIs), particularly mild ones, as current neurological exams and imaging modalities are ineffective.
Innovation Solution
A method and system that utilize electroencephalography (EEG) and electromyography (EMG) data to analyze activation sequences of biological signals to control computing-device operations, and predict TBIs based on sleep stages by processing neural signals with Fourier transforms and machine-learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If brain signals are directly translated to identify tasks, then task identification capability is improved, but signal preprocessing complexity increases
Solution Approach 1:
The system performs signal preprocessing actions in advance during a calibration phase before actual task identification is needed. Baseline signals are collected and processed beforehand to establish reference patterns, which simplifies the real-time translation process by pre-computing the necessary signal characteristics and mappings.
Solution Approach 2:
The patent introduces an intermediary representation layer that translates complex brain signals into simplified task intent models. Instead of directly mapping raw neural signals to tasks, the system uses intermediate feature extraction and pattern matching that bridges the complexity gap between signal processing and task identification.
2Measurement precision
If multiple signal preprocessing steps are performed, then signal translation accuracy is improved, but processing time increases
Solution Approach 1:
Time-consuming preprocessing operations such as baseline signal collection, feature extraction, and pattern establishment are performed in advance during calibration. This preliminary action completes the heavy computational workload before actual use, leaving only lightweight real-time processing needed during task execution.
Solution Approach 2:
The processing pipeline is segmented into offline calibration phase and online execution phase. Complex preprocessing steps are isolated to the offline phase, while the online phase handles only essential real-time signal monitoring and pattern matching, significantly reducing processing time during actual task performance.
3Ease of manufacture
If conventional neurological exams are used for TBI diagnosis, then diagnostic procedure simplicity is maintained, but diagnostic accuracy deteriorates
Solution Approach 1:
The patent replaces conventional mechanical neurological exams with an automated electrical signal-based diagnostic system. Instead of manual physical examination procedures, the system uses EEG signal analysis combined with machine learning models to objectively detect TBI markers, providing both simplicity through automation and improved accuracy through computational analysis.
Solution Approach 2:
The system introduces an intermediary computational analysis layer between signal collection and diagnostic conclusion. Machine learning models process raw EEG signals through intermediate feature extraction and pattern recognition, bridging the gap between simple signal acquisition and accurate TBI diagnosis without requiring complex manual examination procedures.
4Measurement precision
If imaging modalities are used for TBI detection, then objective measurement capability is improved, but detection capability for mild TBIs deteriorates
Solution Approach 1:
The system changes the measurement parameters from structural imaging modalities to functional electrical signal parameters. By analyzing temporal and spectral characteristics of EEG signals during sleep stages, the system detects subtle physiological changes associated with mild TBIs that conventional imaging cannot capture, improving detection sensitivity for mild cases.
Solution Approach 2:
The patent moves the detection dimension from spatial imaging (CT/MRI) to temporal-spectral signal analysis. By examining EEG signals across multiple time points and frequency bands during sleep stages, the system creates a multi-dimensional detection space that reveals mild TBI markers invisible to conventional single-mode imaging techniques.
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
Efficient translation of biological signals for complex computing-device operations and accurate prediction of TBIs, enhancing BCI functionality and providing reliable TBI diagnosis beyond conventional methods.
Implementation Method 1
An electroencephalogram (EEG) is a tool used to measure electrical activity produced by the brain. The functional activity of the brain is collected by electrodes placed on the scalp of a subject.
Implementation Method 2
processing neural signals with Fourier transforms and machine-learning models
Data Source
AI summary
Method and systems for translating biological signals to perform various operations associated with a computing device is provided. The method can include accessing biological-signal data that was collected by a biological-signal data acquisition assembly that comprises a housing having one or more clusters of electrodes. Each cluster of the one or more clusters of electrodes can include at least an active electrode. The method can also include identifying, based on the biological-signal data, a first signal that represents a first intent to move a first portion of a body of the subject. The first signal is generated before a second signal, in which the second signal represents a second intent to move a second portion of the body of the subject. The method can also include translating the first signal to identify a first operation to be performed by a computing device.