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Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix
Allison T Giordano1, Fuh-Cherng Jeng1, Taylor R Black1
1Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA.
Perceptual and Motor Skills
|August 3, 2023
Summary
Excluding silent intervals significantly improves the extraction of frequency-following responses (FFRs) using Source-Separation Non-Negative Matrix Factorization (SSNMF). This optimization enhances FFR detection and reduces noise, aiding auditory processing research.
Area of Science:
- Neuroscience
- Signal Processing
- Auditory Neuroscience
Background:
- Source-Separation Non-Negative Matrix Factorization (SSNMF) is a novel algorithm for extracting frequency-following responses (FFRs) from noisy scalp recordings.
- The impact of silent intervals on SSNMF performance for FFR extraction is not well understood.
- FFRs are crucial electrophysiological measures for assessing auditory processing and neuroplasticity.
Purpose of the Study:
- To investigate the effect of including or excluding silent intervals on the performance of the SSNMF algorithm in extracting human FFRs.
- To quantify the impact of silent intervals on FFR extraction efficiency and noise reduction.
Main Methods:
- An English vowel /i/ with a rising frequency contour was used to evoke FFRs in 23 normal-hearing adults.
- Stimuli had a 150 ms duration with a 150 ms silent interval between stimuli.
- Algorithm performance was assessed by computing FFR Enhancement and Noise Residue, comparing conditions with and without silent intervals (WithSI vs. WithoutSI).
Main Results:
- Excluding silent intervals (WithoutSI) resulted in significantly better FFR Enhancement and lower Noise Residue compared to including them (WithSI) (p < .05).
- On average, excluding silent intervals increased FFR Enhancement by 11.78% and decreased Noise Residue by 20.69%.
- These findings demonstrate a quantifiable benefit of removing silent intervals for SSNMF-based FFR extraction.
Conclusions:
- Silent intervals negatively impact the performance of SSNMF for extracting FFRs.
- Excluding silent intervals is recommended for optimizing SSNMF algorithm design and improving FFR extraction in auditory neuroscience research.
- This study provides data-driven recommendations for enhancing SSNMF applications in analyzing electrophysiological auditory responses.
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