Neural tracking of auditory motion is reflected by delta phase and alpha power of EEG

It is of increasing practical interest to be able to decode the spatial characteristics of an auditory scene from electrophysiological signals. However, the cortical representation of auditory space is not well characterized, and it is unclear how cortical activity reflects the time-varying location of a moving sound. Recently, we demonstrated that cortical response measures to discrete noise bursts can be decoded to determine their origin in space. Here we build on these findings to investigate the cortical representation of a continuously moving auditory stimulus using scalp recorded electroencephalography (EEG). In a first experiment, subjects listened to pink noise over headphones which was spectro-temporally modified to be perceived as randomly moving on a semi-circular trajectory in the horizontal plane. While subjects listened to the stimuli, we recorded their EEG using a 128-channel acquisition system. The data were analysed by 1) building a linear regression model (decoder) mapping the relationship between the stimulus location and a training set of EEG data, and 2) using the decoder to reconstruct an estimate of the time-varying sound source azimuth from the EEG data. The results showed that we can decode sound trajectory with a reconstruction accuracy significantly above chance level. Specifically, we found that the phase of delta (<2 Hz) and power of alpha (8–12 Hz) EEG track the dynamics of a moving auditory object. In a follow-up experiment, we replaced the noise with pulse train stimuli containing only interaural level and time differences (ILDs and ITDs respectively). This allowed us to investigate whether our trajectory decoding is sensitive to both acoustic cues. We found that the sound trajectory can be decoded for both ILD and ITD stimuli. Moreover, their neural signatures were similar and even allowed successful cross-cue classification. This supports the notion of integrated processing of ILD and ITD at the cortical level. These results are particularly relevant for application in devices such as cognitively controlled hearing aids and for the evaluation of virtual acoustic environments.