Decoding of selective attention to continuous speech from the human auditory brainstem response

Tobias Reichenbach,Mikolaj Kegler,Octave Etard,Antonio Elia Forte,Chananel Braiman

doi:10.1016/j.neuroimage.2019.06.029

Abstract

Humans are highly skilled at analysing complex acoustic scenes. The segregation of different acoustic streams and the formation of corresponding neural representations is mostly attributed to the auditory cortex. Decoding of selective attention from neuroimaging has therefore focussed on cortical responses to sound. However, the auditory brainstem response to speech is modulated by selective attention as well, as recently shown through measuring the brainstem's response to running speech. Although the response of the auditory brainstem has a smaller magnitude than that of the auditory cortex, it occurs at much higher frequencies and therefore has a higher information rate. Here we develop statistical models for extracting the brainstem response from multi-channel scalp recordings and for analysing the attentional modulation according to the focus of attention. We demonstrate that the attentional modulation of the brainstem response to speech can be employed to decode the attentional focus of a listener from short measurements of 10 s or less in duration. The decoding remains accurate when obtained from three EEG channels only. We further show how out-of-the-box decoding that employs subject-independent models, as well as decoding that is independent of the specific attended speaker is capable of achieving similar accuracy. These results open up new avenues for investigating the neural mechanisms for selective attention in the brainstem and for developing efficient auditory brain-computer interfaces.

Highlights

Humans have an extraordinary capability to analyse crowded auditory scenes
Auditory prosthesis could potentially aid with understanding speech in noise through selectively enhancing a target speech, for instance based on its direction, using algorithms such as beam forming (Kidd et al, 2015)
We found that decoding based on the bandpass filtered audio has a similar accuracy as the one based on the waveform obtained from empirical mode decomposition (EMD), which is encouraging for real-time applications (Figure 7-B)

Summary

Introduction

Humans have an extraordinary capability to analyse crowded auditory scenes. We can, for instance, focus our attention on one of two competing speakers and understand her or him despite the distractor voice (Middlebrooks et al, 2017). Auditory prosthesis could potentially aid with understanding speech in noise through selectively enhancing a target speech, for instance based on its direction, using algorithms such as beam forming (Kidd et al, 2015) Such selective enhancement requires knowledge of which sound the user aims to attend to. Current research attempts to decode an individual's focus of selective attention to sound from non-invasive brain recordings (O'Sullivan et al, 2014; Mirkovic et al., 2015; Biesmans et al, 2016; Fuglsang et al, 2017) If such decoding worked in real time, it could inform the sound processing in an auditory prosthesis. It could form the basis of a non-invasive braincomputer interface for motor-impaired patients with brain injury, for instance, who may not be able to respond behaviourally. Such decoding of selective attention could be employed clinically for a better understanding and characterization of hearing loss

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Conclusion