Noise presents a constant challenge in daily communication. Previous research has demonstrated the effects of ipsilateral noise on speech processing in the human brain. In this study, we examined the effects of contralateral noise on the accuracy of frequency coding at the subcortical level. Byusing a speech stimulus that mimicked the English vowel /i/ with a rising frequency contour, we obtained scalp-recorded, frequency-following responses in nine normal-hearing adults. To determine the effects of contralateral noise, we performed two experimental conditions: with and without the presence of contralateral noise. Results indicated that the fidelity of frequency coding, as reflected through Tracking Accuracy and Slope Error, were significantly degraded when continuous white noise was added to the contralateral ear. These findings provide important information and help us better understand how frequency cues are processed in noisy environments.
Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150 ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150 ms. We computed FFR Enhancement and Noise Residue to estimate algorithm performance, while silent intervals were either included (i.e., the WithSI condition) or excluded (i.e., the WithoutSI condition) in our analysis. The FFR Enhancements and Noise Residues obtained in the WithoutSI condition were significantly better ( p < .05) than those obtained in the WithSI condition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancement and a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research.
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