Cortical Auditory Attention Decoding During Music and Speech Listening

Abstract

It has been demonstrated that from cortical recordings, it is possible to detect which speaker a person is attending in a cocktail party scenario. The stimulus reconstruction approach, based on linear regression, has been shown to be useable to reconstruct an approximation of the envelopes of the sounds attended to and not attended to by a listener from the electroencephalogram data (EEG). Comparing the reconstructed envelopes with the envelopes of the stimuli, a higher correlation between the envelopes of the attended sound is observed. Most of the studies focused on speech listening, and only a few studies investigated the performances and the mechanisms of auditory attention decoding during music listening. In the present study, auditory attention detection (AAD) techniques that have been proven successful for speech listening were applied to a situation where the listener is actively listening to music concomitant with a distracting sound. Results show that AAD can be successful for both speech and music listening while showing differences in the reconstruction accuracy. The results of this study also highlighted the importance of the training data used in the construction of the model. This study is a first attempt to decode auditory attention from EEG data in situations where music and speech are present. The results of this study indicate that linear regression can also be used for AAD when listening to music if the model is trained for musical signals.