Detecting Human Frequency-Following Responses Using an Artificial Neural Network.

Abstract

Frequency-following responses (FFRs) are neural signals that reflect the brain's encoding of acoustic characteristics, such as speech intonation. While traditional machine learning models have been used to classify FFRs elicited under various conditions, the potential of deep learning models in FFR research remains underexplored. This study investigated the efficacy of a three-layer artificial neural network (ANN) in detecting the presence or absence of FFRs elicited by a rising intonation of the English vowel /i/. The ANN was trained and tested on FFR recordings, using F0 estimates derived from the spectral domain as input data. Model performance was evaluated by systematically varying the number of inputs, hidden neurons, and the number of sweeps included in the recordings. The prediction accuracy of the ANN was significantly influenced by the number of inputs, hidden neurons, and sweeps. Optimal configurations included 6-8 inputs and 4-6 hidden neurons, achieving a prediction accuracy of approximately 84% when the signal-to-noise ratio was enhanced by including 100 or more sweeps. These results provide a foundation for future applications in auditory processing assessments and clinical diagnostics.