EEG-based classification of natural sounds reveals specialized responses to speech and music

Abstract

Humans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical \"low-level\" acoustic statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the trends in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective and efficient way to study the human brains selectivity for speech and music in other populations.\n\nHighlightsO_LIEEG responses are stronger to speech and music than to other natural sounds\nC_LIO_LIThis selectivity was not replicated using stimuli with the same acoustic statistics\nC_LIO_LIThese techniques can be a cost-effective way to study speech and music selectivity\nC_LI