Scalp tapping-based protocol for adjusting the parameters of binaural hearing aids. (August 2018)

• Record Type: Journal Article
• Authors: Kim, Joo Young; Nam, Kyoung Won; Lee, Jun Chang; Hwang, Jong Ho; Jang, Dong Pyo; Kim, In Young
• Is Part Of: Biomedical signal processing and control. Volume 45(2018)
• Page Start: 91
• Page End: 97
• Publication Date: 2018-08
• Subjects: Acoustics -- Hearing aid -- Tapping -- Control; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.05.018
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Scalp tapping-based protocol for controlling the parameters of binaural hearing aids was proposed. Reliability, benefits, and clinical feasibility were evaluated using 11 volunteers. Tapping detection accuracies were above 95% and latency-time was reduced. It can provide a simple and convenient way to adjust internal parameters of hearing aid. Abstract: Hearing aids (HAs) are used in everyday life. To improve the subjective satisfaction of HAs, a simple and comprehensive means to adjust the internal parameters to accommodate various situations is required. In this study, we propose a new scalp tapping-based protocol for controlling the parameters of binaural HAs. We then demonstrate the reliability, benefits, and clinical feasibility of the protocol via subjective evaluations from 11 volunteers. In the reliability test, the implemented scalp-tapping detection algorithm showed accuracies above 95.0% in various conditions and no false adjustment of internal parameters occurred from motions or other artifacts. In the benefit test, improvements of signal-to-noise ratio (SNR) and segmental SNR were 2.42–4.04 dB and 1.22–3.95 dB on average for serial connection of beamforming and noise-reduction algorithms. In the feasibility test, the range of control accuracy was 66.67–100% (88.06 ± 10.19%), and the range of latency time was 5.03–22.27 sec (7.25 ± 1.73 s). We expect that the proposed protocol can provide a simple and convenient way to adjust internal parameters of HAs without additional hardware changes and with short-term training.