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Behavioral Account of Attended Stream Enhances Neural Tracking.

Moïra-Phoebé Huet^1,2, Christophe Micheyl³, Etienne Parizet¹

¹Laboratoire Vibrations Acoustique, Institut National des Sciences Appliquées de Lyon, Université de Lyon, Villeurbanne, France.

Frontiers in Neuroscience

|December 30, 2021

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Researchers developed a new behavioral test to track auditory attention during speech perception. This method improves speech reconstruction from electroencephalographic (EEG) signals, especially in difficult listening conditions.

Area of Science:

Neuroscience
Auditory Perception
Speech Processing

Background:

Selective auditory attention is crucial for speech intelligibility in noisy environments.
Previous studies often assume sustained attention, which may not reflect real-world listening.
Electroencephalography (EEG) has been used to identify neural correlates of attention to speech.

Purpose of the Study:

To develop a behavioral method to track attention fluctuations during concurrent speech listening.
To assess if incorporating real-time attention data improves EEG-based speech reconstruction.
To investigate the impact of listening difficulty on attentional focus and EEG analysis.

Main Methods:

A novel listen-then-recall task (Long-SWoRD test) was designed to infer attentional focus (target, masker, or neither).

Keywords:

attentional switches neural tracking speech-on-speech temporal response function (TRF)vocal cues

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EEG signals were recorded during listening to concurrent speech streams with varying masker similarity.

A temporal response function (TRF) model was used for speech stimulus reconstruction, incorporating behavioral attention data during training.

Main Results:

The behavioral recall task successfully inferred participants' attentional focus retrospectively.
Incorporating actual attentional focus data improved EEG-based speech reconstruction accuracy compared to assuming constant attention.
This improvement was most significant in challenging listening conditions with high masker similarity.

Conclusions:

The Long-SWoRD test effectively tracks dynamic changes in auditory selective attention.
Real-time attention data enhances the accuracy of speech decoding from EEG signals.
This approach is valuable for understanding speech perception and improving assistive listening technologies.