Speaker
Description
Frequency-following responses (FFR) may be sensitive to auditory nerve degeneration and auditory processing deficits beyond what is captured by the clinical audiogram. The clinical potential of this measure relies on robust, quick and reliable acquisition in the clinic. Multiway canonical component analysis (MCCA) presents a way of utilizing common response patterns across multiple channels and subjects to boost the fidelity and reliability of such responses. Here, we demonstrate the potential of MCCA to substantially improve the signal-to-noise ratio (SNR) of FFRs in a large dataset of EEG recordings (16+2 channels) obtained from normal-hearing adults (n = 110) in response to 326 Hz pure tones. When applied as a denoising method, MCCA increased the proportion of participants with significant FFRs by 17%, allowing for robust responses in 97% of the sample. Additionally, acquisition time was reduced, with FFRs of equivalent significance using only 20% of the full dataset (approximately 2 minutes of recording time), consistent with clinical constraints. Potential overfitting was addressed through cross-validation using unseen EEG noise. These results demonstrate that MCCA can enhance the reliability, efficiency, and clinical feasibility of FFR measurements by aligning patient data to a multi-subject reference space.