Hearing Aid EEG Cognitive Load Adaptation
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Solution Overview
Problem
Traditional hearing aid systems struggle to adapt processing modes to individual user needs, particularly in varying noise levels and cognitive loads, leading to inconsistent speech recognition and comprehension.
Innovation Solution
A hearing aid system that continuously monitors audio and EEG signals to determine deviation and cognitive load, switching between processing modes to optimize sound processing based on real-time user conditions, incorporating electrodes for brain activity measurement and adaptive algorithms for noise reduction and directionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple processing modes with different processing algorithms are implemented to handle varying noise levels, then speech intelligibility is improved, but device complexity increases
Solution Approach 1:
The system dynamically switches between different processing modes based on real-time cognitive load assessment. The audio input signal processing unit adapts its processing algorithm according to the user's current cognitive state, transitioning from static to dynamic operation to optimize speech intelligibility while managing complexity.
Solution Approach 2:
The system changes processing parameters based on cognitive load levels. When cognitive load exceeds threshold values, the system modifies processing mode parameters such as noise reduction intensity, directionality settings, and compression levels to reduce the listening effort required, thereby maintaining speech intelligibility without requiring permanent complex processing architecture.
2Object-affected harmful factors
If processing mode switches based on predefined signal-to-noise ratio thresholds are applied, then noise reduction is achieved, but adaptability to individual cognitive needs deteriorates
Solution Approach 1:
The system incorporates feedback from cognitive load assessment into the processing mode selection. The audio input signal analyzing unit continuously monitors cognitive load indicators and feeds this information back to the audio input signal processing unit, enabling real-time adaptation of noise processing strategies to individual cognitive needs rather than relying solely on predefined signal-to-noise ratio thresholds.
Solution Approach 2:
The system performs preliminary cognitive load assessment to determine appropriate processing modes before processing the audio signal. By assessing cognitive load in advance and selecting processing modes proactively, the system can better adapt to individual cognitive needs and prevent cognitive overload rather than reactively adjusting after the fact.
3Adaptability or versatility
If EEG monitoring and cognitive load assessment are continuously performed, then personalized processing is improved, but use of energy increases
Solution Approach 1:
The system performs cognitive load assessment periodically rather than continuously. The audio input signal analyzing unit evaluates cognitive load at specific intervals or triggered by certain conditions (such as detected speech segments or noise level changes), reducing the continuous monitoring burden while still providing personalized processing adjustments when needed.
Solution Approach 2:
The system uses the user's own brain signals (EEG) as the assessment source, requiring no external equipment or additional power-intensive sensors. By leveraging endogenous biological signals that are already present, the system achieves personalized processing without adding external power consumption for additional sensing hardware.
4Ease of operation
If processing algorithms are adjusted based on cognitive load threshold values, then listening effort is reduced, but measurement precision of cognitive state deteriorates
Solution Approach 1:
The system applies partial cognitive load measurement using a limited set of EEG features or simplified assessment metrics rather than comprehensive cognitive state analysis. By measuring only the most relevant cognitive load indicators (such as alpha band power or specific frequency components) rather than the full cognitive state, the system reduces listening effort while accepting reduced measurement precision.
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 allows for personalized sound processing, improving speech recognition and comprehension by dynamically adjusting to changing noise levels and cognitive loads, enhancing the user experience by leveraging electrophysiological measures for adaptive hearing aid settings.
Implementation Method 1
a plurality of electrodes that are configured to be brought into contact with the skin of a user and which are configured - when operationally mounted - to receive an electric signal that represents a user's brain activity and to provide a respective EEG-related signal
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
The invention refers to a hearing aid system that comprises an electric audio signal input, an audio input signal processing unit that is configured to process electric audio input signals in the first processing mode or in the second processing mode and to provide an electric audio output signal, and an output transducer. The hearing aid system further comprises an audio input signal analysing unit that is configured to continuously monitor the electric audio input signal as a function of time and to determine and to provide a number of audio signal values each representing a characteristic of the electric audio input signal at a given time instance. The hearing aid system further comprises a plurality of electrodes that are configured to be brought into contact with the skin of a user and which are configured - when operationally mounted - to receive an electric signal that rep-resents a user's brain activity and to provide a respective EEG-related signal. The hearing aid system further comprises an EEG-related signal analysing unit that is configured to continuously monitor the EEG-related signal as a function of time and to determine and to provide a number of EEG-related values each representing the EEG-related signal at a given time instance, a memory unit which is configured to store a number of audio signal values such that a first history of respective audio signal values is created and/or to store a number of EEG-related values such that a second history of respective EEG-related values is created and a signal comparison unit that is configured to compare a current audio signal value with at least one preceding audio signal value of the first history to determine and to provide a deviation signal and/or to compare a current EEG-related value with at least one preceding EEG-related value of the second history to determine a measure of a user's current cognitive load and to provide a cognitive load representing output signal accordingly. The audio input signal processing unit is further configured to apply the first processing mode or the at least second processing mode depending on said deviation signal and/or depending on said cognitive load representing output signal