EEG-Based Speech Discriminability Assessment System
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Solution Overview
Problem
Current speech discriminability assessment methods for hearing aids require cumbersome user input, posing a burden on both users and evaluators, especially for individuals with hypacusia or the elderly, and may lead to incorrect evaluations due to input errors.
Innovation Solution
A system that measures electroencephalogram signals to detect unexpectedness signals and P300 components in response to presented audio and character stimuli, allowing for automatic assessment of speech sound discriminability without requiring user input, by presenting characters that match or mismatch the audio with a predetermined frequency and using these signals to determine discriminability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech discriminability assessment methods are used requiring user input, then assessment can be performed, but user and evaluator burden increases and input errors may occur
Solution Approach 1:
The patent replaces the mechanical input system (user writing or speaking answers) with a physiological signal detection system (EEG measurement). The system detects unexpectedness signals and P300 components in the user's brain waves to automatically determine whether the presented character matches the heard speech, eliminating the need for manual user input while maintaining assessment accuracy.
Solution Approach 2:
The patent introduces EEG signals as an intermediary between the speech stimulus and the assessment result. Instead of directly capturing user input, the system uses brain wave responses as an intermediate indicator of whether the user correctly perceived the speech, providing an automatic and error-free assessment mechanism.
2Measurement precision
If manual evaluation of user answers is performed, then speech discriminability can be assessed, but evaluator workload increases significantly
Solution Approach 1:
The assessment system performs self-evaluation by automatically analyzing the user's EEG signals. The system itself determines whether the presented character matches the heard speech by detecting unexpectedness signals and P300 components, eliminating the need for external evaluator intervention and significantly reducing time loss.
Solution Approach 2:
The patent replaces the manual evaluation process with an automated physiological signal analysis system. The EEG measurement and analysis system automatically determines assessment results without requiring evaluator time, while maintaining or improving measurement precision through objective brain wave data.
3Productivity
If users with hypacusia or elderly persons perform input operations, then assessment can be completed, but input errors increase due to difficulty
Solution Approach 1:
The patent replaces the mechanical input operation with physiological signal detection. By measuring EEG unexpectedness signals and P300 components, the system eliminates the need for users with hypacusia or elderly persons to perform potentially error-prone input operations, thereby improving input accuracy while maintaining assessment completion.
Solution Approach 2:
The patent uses EEG signals as an intermediary that bypasses the problematic user input stage entirely. The brain wave responses provide a reliable, error-free indicator of speech perception that does not depend on the user's physical or cognitive ability to perform input operations.
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
This approach enables a quantitative and automatic evaluation of aural distinction for speech sounds, reducing user and evaluator burden and improving the accuracy of speech sound discriminability assessments, particularly for those with hearing impairments.
Implementation Method 1
a biological signal measurement section for measuring an electroencephalogram signal of a user
Implementation Method 2
an unexpectedness detection section for detecting presence or absence of an unexpectedness signal from the measured electroencephalogram signal of a user, the unexpectedness signal representing a positive component at 600 ms±100 ms after a time point when the character was presented to the user
Data Source
AI summary
A speech discriminability assessment system includes: a biological signal measurement section for measuring an electroencephalogram signal of a user; a presented-speech sound control section for determining a speech sound to be presented to the user by referring to a speech sound database retaining a plurality of monosyllabic sound data; an audio presentation section for presenting an audio associated with the determined speech sound to the user; a character presentation section for presenting a character associated with the determined speech sound to the user, subsequent to the presentation of the audio by the audio presentation section; an unexpectedness detection section for detecting presence or absence of an unexpectedness signal from the measured electroencephalogram signal of the user, the unexpectedness signal representing a positive component at 600 ms±100 ms after a time point when the character was presented to the user; and a speech sound discriminability determination section for determining a speech sound discriminability based on a result of detection by the unexpectedness detection section.