Abstract
Individuals with hearing loss can benefit from the use of the hearing aids, which should be fitted in close accordance with the individual's hearing thresholds. Until recently, fitting of hearing aid has predominantly been performed in a clinic based on behavioral tests such as pure tone audiometry. Characterization of hearing loss can alternatively be performed based on electrophysiological tests such as the auditory steady-state response (ASSR), which can be recorded from electroencephalography (EEG) electrodes placed on the scalp. Traditionally, ASSR measurements are limited to laboratory settings and require trained personnel to perform the test. Ear-EEG, where EEG electrodes are placed in the ear, allows hearing threshold estimation to be performed in daily life. Integrated into a hearing device, this technology would enable both initial and recurrent fitting of the hearing aid to be performed automatically in the everyday life of the user.
The ASSR is small compared to the background EEG and various noise sources, which means the low signal-to-noise ratio (SNR) for ASSR. This makes estimation of the ASSR challenging. Several methods can be used to enhance the ASSR amplitude, thereby making the response more prominent. This PhD project has investigated the effect of the stimulus bandwidth on the ASSR. The results show that a small increase in stimulus bandwidth (from pure tone to 1/3 or 1/2 octave) improves the detectability of ASSR at low stimulation levels while only having a small impact on the frequency specificity of measured responses.
The low ASSR SNR becomes an even larger challenge when ASSRs are estimated from ear-EEG recordings. To address this, the project has investigated whether detection of ASSRs in ear-EEG can be improved by applying spatial filtering methods. Spatial filtering is a noise reduction method which utilizes multichannel recordings to suppress noise while maintaining the desired signal. We have designed an iterative gradient-based method which maximizes the ASSR SNR. The results show that the proposed spatial filtering method outperforms the conventional approach, where a pair of electrodes giving the largest SNR (Best Pair) is chosen. We have compared the proposed method with other spatial filtering methods and found no difference between spatial filtering methods, while all spatial filtering methods have shown significant improvement in performance compared to Best Pair approach.
Bracketing technique is usually used in traditional ASSR-based hearing threshold estimation. There, the same stimulus is presented at different levels of intensity and the lowest level at which ASSR is detected is considered as a physiological threshold. The conventional stimuli for ASSR-based hearing assessment - pure tones and chirps - are synthetic and monotonous, which makes them inconvenient for repeated use in daily life. The current PhD project has introduced an approach where the ASSR versus presentation-level relation is estimated using a sub-band amplitude modulated continuous speech signal. The results show that the ASSR can be estimated as a function of the level both in scalp- and ear-EEG with slopes comparable to those reported in the literature for the conventional ASSR stimuli. This allows translation of the relation between ASSR and presentation level to physiological threshold. If incorporated into a hearing device, this approach would enable continuously monitoring of hearing thresholds in everyday life without, or with minimal, inconvenience to the user.
The ASSR is small compared to the background EEG and various noise sources, which means the low signal-to-noise ratio (SNR) for ASSR. This makes estimation of the ASSR challenging. Several methods can be used to enhance the ASSR amplitude, thereby making the response more prominent. This PhD project has investigated the effect of the stimulus bandwidth on the ASSR. The results show that a small increase in stimulus bandwidth (from pure tone to 1/3 or 1/2 octave) improves the detectability of ASSR at low stimulation levels while only having a small impact on the frequency specificity of measured responses.
The low ASSR SNR becomes an even larger challenge when ASSRs are estimated from ear-EEG recordings. To address this, the project has investigated whether detection of ASSRs in ear-EEG can be improved by applying spatial filtering methods. Spatial filtering is a noise reduction method which utilizes multichannel recordings to suppress noise while maintaining the desired signal. We have designed an iterative gradient-based method which maximizes the ASSR SNR. The results show that the proposed spatial filtering method outperforms the conventional approach, where a pair of electrodes giving the largest SNR (Best Pair) is chosen. We have compared the proposed method with other spatial filtering methods and found no difference between spatial filtering methods, while all spatial filtering methods have shown significant improvement in performance compared to Best Pair approach.
Bracketing technique is usually used in traditional ASSR-based hearing threshold estimation. There, the same stimulus is presented at different levels of intensity and the lowest level at which ASSR is detected is considered as a physiological threshold. The conventional stimuli for ASSR-based hearing assessment - pure tones and chirps - are synthetic and monotonous, which makes them inconvenient for repeated use in daily life. The current PhD project has introduced an approach where the ASSR versus presentation-level relation is estimated using a sub-band amplitude modulated continuous speech signal. The results show that the ASSR can be estimated as a function of the level both in scalp- and ear-EEG with slopes comparable to those reported in the literature for the conventional ASSR stimuli. This allows translation of the relation between ASSR and presentation level to physiological threshold. If incorporated into a hearing device, this approach would enable continuously monitoring of hearing thresholds in everyday life without, or with minimal, inconvenience to the user.
| Original language | English |
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| Publisher | Aarhus University |
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| Number of pages | 124 |
| Publication status | Published - Nov 2023 |