Where is the cocktail party? Decoding locations of attended and unattended moving sound sources using EEG.

Recently, we showed that in a simple acoustic scene with one sound source, auditory cortex tracks the time-varying location of a continuously moving sound. Specifically, we found that both the delta phase and alpha power of the electroencephalogram (EEG) can be used to reconstruct the sound source azimuth. However, in natural settings, we are often presented with a mixture of multiple competing sounds and so we must focus our attention on the relevant source in order to segregate it from the competing sources e.g. 'cocktail party effect'. While many studies have examined this phenomenon in the context of sound envelope tracking by the cortex, it is unclear how we process and utilize spatial information in complex acoustic scenes with multiple sound sources. To test this, we created an experiment where subjects listened to two concurrent sound stimuli that were moving within the horizontal plane over headphones while we recorded their EEG. Participants were tasked with paying attention to one of the two presented stimuli. The data were analyzed by deriving linear mappings, temporal response functions (TRF), between EEG data and attended as well unattended sound source trajectories. Next, we used these TRFs to reconstruct both trajectories from previously unseen EEG data. In a first experiment we used noise stimuli and included the task involved spatially localizing embedded targets. Then, in a second experiment, we employed speech stimuli and a non-spatial speech comprehension task. Results showed the trajectory of an attended sound source can be reliably reconstructed from both delta phase and alpha power of EEG even in the presence of distracting stimuli. Moreover, the reconstruction was robust to task and stimulus type. The cortical representation of the unattended source position was below detection level for the noise stimuli, but we observed weak tracking of the unattended source location for the speech stimuli by the delta phase of EEG. In addition, we demonstrated that the trajectory reconstruction method can in principle be used to decode selective attention on a single-trial basis, however, its performance was inferior to envelope-based decoders. These results suggest a possible dissociation of delta phase and alpha power of EEG in the context of sound trajectory tracking. Moreover, the demonstrated ability to localize and determine the attended speaker in complex acoustic environments is particularly relevant for cognitively controlled hearing devices.

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.

Different spatio-temporal electroencephalography features drive the successful decoding of binaural and monaural cues for sound localization.

The human ability to localize sound is essential for monitoring our environment and helps us to analyse complex auditory scenes. Although the acoustic cues mediating sound localization have been established, it remains unknown how these cues are represented in human cortex. In particular, it is still a point of contention whether binaural and monaural cues are processed by the same or distinct cortical networks. In this study, participants listened to a sequence of auditory stimuli from different spatial locations while we recorded their neural activity using electroencephalography (EEG). The stimuli were presented over a loudspeaker array, which allowed us to deliver realistic, free-field stimuli in both the horizontal and vertical planes. Using a multivariate classification approach, we showed that it is possible to decode sound source location from scalp-recorded EEG. Robust and consistent decoding was shown for stimuli that provide binaural cues (i.e. Left vs. Right stimuli). Decoding location when only monaural cues were available (i.e. Front vs. Rear and elevational stimuli) was successful for a subset of subjects and showed less consistency. Notably, the spatio-temporal pattern of EEG features that facilitated decoding differed based on the availability of binaural and monaural cues. In particular, we identified neural processing of binaural cues at around 120 ms post-stimulus and found that monaural cues are processed later between 150 and 200 ms. Furthermore, different spatial activation patterns emerged for binaural and monaural cue processing. These spatio-temporal dissimilarities suggest the involvement of separate cortical mechanisms in monaural and binaural acoustic cue processing.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter-often referred to as a temporal response function-that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.