LAUSR

OBJECTIVE It has been shown that attentional selection in a simple dichotic listening paradigm can be decoded offline by reconstructing the stimulus envelope from single-trial neural response data. Here, we test the efficacy of this approach in an environment with non-stationary talkers. We then look beyond the envelope reconstructions themselves and consider whether incorporating the decoder values-which reflect the weightings applied to the multichannel EEG data at different time lags and scalp locations when reconstructing the stimulus envelope-can improve decoding performance. APPROACH High-density EEG was recorded as subjects attended to one of two talkers. The two speech streams were filtered using HRTFs, and the talkers were alternated between the left and right locations at varying intervals to simulate a dynamic environment. We trained spatio-temporal decoders mapping from EEG data to the attended and unattended stimulus envelopes. We then decoded auditory attention by (1) using the attended decoder to reconstruct the envelope and (2) exploiting the fact that decoder weightings themselves contain signatures of attention, resulting in consistent patterns across subjects that can be classified. MAIN RESULTS The previously established decoding approach was found to be effective even with non-stationary talkers. Signatures of attentional selection and attended direction were found in the spatio-temporal structure of the decoders and were consistent across subjects. The inclusion of decoder weights into the decoding algorithm resulted in significantly improved decoding accuracies (from 61.07% to 65.31% for 4 s windows). An attempt was made to include alpha power lateralization as another feature to improve decoding, although this was unsuccessful at the single-trial level. SIGNIFICANCE This work suggests that the spatial-temporal decoder weights can be utilised to improve decoding. More generally, looking beyond envelope reconstruction and incorporating other signatures of attention is an avenue that should be explored to improve selective auditory attention decoding.