The emergence of idiosyncratic patterns in the frequency-following response during the first year of life

Validating a novel paradigm for simultaneously assessing mismatch response and frequency-following response to speech sounds

BACKGROUND

Speech sounds are processed in the human brain through intricate and interconnected cortical and subcortical structures. Two neural signatures, one largely from cortical sources (mismatch response, MMR) and one largely from subcortical sources (frequency-following response, FFR) are critical for assessing speech processing as they both show sensitivity to high-level linguistic information. However, there are distinct prerequisites for recording MMR and FFR, making them difficult to acquire simultaneously NEW METHOD: Using a new paradigm, our study aims to concurrently capture both signals and test them against the following criteria: (1) replicating the effect that the MMR to a native speech contrast significantly differs from the MMR to a nonnative speech contrast, and (2) demonstrating that FFRs to three speech sounds can be reliably differentiated.

RESULTS

Using EEG from 18 adults, we observed a decoding accuracy of 72.2 % between the MMR to native vs. nonnative speech contrasts. A significantly larger native MMR was shown in the expected time window. Similarly, a significant decoding accuracy of 79.6 % was found for FFR. A high stimulus-to-response cross-correlation with a 9 ms lag suggested that FFR closely tracks speech sounds.

COMPARISON WITH EXISTING METHOD(S)

These findings demonstrate that our paradigm reliably captures both MMR and FFR concurrently, replicating and extending past research with much fewer trials (MMR: 50 trials; FFR: 200 trials) and shorter experiment time (12 minutes).

CONCLUSIONS

This study paves the way to understanding cortical-subcortical interactions for speech and language processing, with the ultimate goal of developing an assessment tool specific to early development.

Advancing Auditory Processing by Detecting Frequency-Following Responses Through a Specialized Machine Learning Model

In this study, we explore the feasibility and performance of detecting scalp-recorded frequency-following responses (FFRs) with a specialized machine learning (ML) model. By leveraging the strengths of feature extraction of the source separation non-negative matrix factorization (SSNMF) algorithm and its adeptness in handling limited training data, we adapted the SSNMF algorithm into a specialized ML model with a hybrid architecture to enhance FFR detection amidst background noise. We recruited 40 adults with normal hearing and evoked their scalp recorded FFRs using the English vowel/i/with a rising pitch contour. The model was trained on FFR-present and FFR-absent conditions, and its performance was evaluated using sensitivity, specificity, efficiency, false-positive rate, and false-negative rate metrics. This study revealed that the specialized SSNMF model achieved heightened sensitivity, specificity, and efficiency in detecting FFRs as the number of recording sweeps increased. Sensitivity exceeded 80% at 500 sweeps and maintained over 89% from 1000 sweeps onwards. Similarly, specificity and efficiency also improved rapidly with increasing sweeps. The progressively enhanced sensitivity, specificity, and efficiency of this specialized ML model underscore its practicality and potential for broader applications. These findings have immediate implications for FFR research and clinical use, while paving the way for further advancements in the assessment of auditory processing.

Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix Factorization

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.