Comparing neurophysiological correlates of speech-in-noise perception across MEG, EEG and ear-EEG

Neural speech tracking is a powerful phenomenon in which brain signals synchronize with the time-varying features of continuous speech. This study benchmarks speech-in-noise tracking performance and the feasibility of objective speech reception threshold (SRTneuro) estimation across three sensor modalities: magnetoencephalography (MEG), scalp-electroencephalography (EEG), and ear-EEG. While MEG and scalp-EEG serve as reference measurements providing full-head coverage, ear-EEG offers a more unobtrusive and portable alternative. Twenty-one young, normal-hearing adults participated in simultaneous MEG and 76-channel EEG recordings, which incorporated 16 integrated around-ear electrodes (ear-EEG). During the recordings, participants listened to continuous audiobooks and matrix sentences at fixed intelligibility levels corresponding to individually varied signal-to-noise ratios (SNRs). Neural tracking was quantified via envelope reconstruction using a linear decoder, and psychometric functions were fitted to the data to derive SRTneuro as the midpoint. We observed consistent envelope tracking across all three modalities, suggesting shared underlying neural processes, with reconstruction accuracies increasing alongside SNR. MEG yielded the highest signal quality, with reconstruction accuracies approximately 1.5 times higher than scalp-EEG and 3 times higher than ear-EEG. Although SRTneuro was derived within a 5 dB margin of behavioral thresholds for all participants, these neurophysiological estimates did not significantly correlate with individual behavioral SRTs. Furthermore, ear-EEG proved less reliable and exhibited a slight bias. These results demonstrate that MEG, scalp-EEG, and ear-EEG are all viable for capturing robust neural envelope tracking. However, the acoustic envelope alone may be insufficient as a standalone predictor for diagnostics due to high inter-subject variability. Significance StatementWhen listening to speech, the brain tracks the rhythm of the signal, specifically the temporal envelope. These neural responses provide insights into speech-in-noise perception and offer a potential objective measure of listening ability. This study is the first to benchmark speech-in-noise tracking across high-resolution MEG, affordable scalp-EEG, and emerging ear-EEG using individualized signal-to-noise ratios derived from psychometric functions. Our comparison reveals a clear performance hierarchy, proving that even ear-centered configurations can capture robust neural signals across varying acoustic conditions relative to high-fidelity references. While high inter-subject variability currently limits the diagnostic utility of standalone envelope tracking, characterizing these trade-offs across different sensor modalities establishes a foundation for advanced, individualized neural hearing assessments.