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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.

Comprehension of speech in noise is a challenge for hearing-impaired (HI) individuals. Electroencephalography (EEG) provides a tool to investigate the effect of different levels of signal-to-noise ratio (SNR) of the speech. Most studies with EEG have focused on spectral power in well-defined frequency bands such as alpha band. In this study, we investigate how local functional connectivity, i.e. functional connectivity within a localized region of the brain, is affected by two levels of SNR. Twenty-two HI participants performed a continuous speech in noise task at two different SNRs (+3 dB and +8 dB). The local connectivity within eight regions of interest was computed by using a multivariate phase synchrony measure on EEG data. The results showed that phase synchrony increased in the parietal and frontal area as a response to increasing SNR. We contend that local connectivity measures can be used to discriminate between speech-evoked EEG responses at different SNRs.