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

Bednar, Adam; Lalor, Edmund C.

doi:10.1016/j.neuroimage.2018.07.054

Cited by 32 publications

(30 citation statements)

References 55 publications

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“…This finding is in line with evidence that increases in parieto‐occipital alpha power suppresses processing of distracting visual information (Kelly et al., ) as well as reports that it may index the reliability of visual speech (Shatzer, Shen, Kerlin, Pitt, & Shahin, ). Another possible explanation for these findings is due to the spatial separation of the two speakers (30°), given findings that alpha power tracks the spatial location of a sound (Bednar & Lalor, ; Feng, Störmer, Martinez, McDonald, & Hillyard, ). These studies however, report a lateralization of alpha power across parietal scalp regions in response to a spatialized sound.…”

Section: Discussionmentioning

confidence: 99%

“…Alpha power has a long history of association with attention (Pfurtscheller & Klimesch, 1991; for review, see: Klimesch, 2012). In particular, alpha power has been shown to be important for audio (Ahveninen, Huang, Belliveau, Chang, & Hamalainen, 2013;Bednar & Lalor, 2018;Kerlin, Shahin, & Miller, 2010;Wöstmann et al, 2016) and visual spatial attention (Foxe, Simpson, & Ahlfors, 1998;Frey et al, 2014;Fu et al, 2001;Mazaheri et al, 2014;Worden, Foxe, Wang, & Simpson, 2000). However, the usefulness of this measure in attention decoding of natural, multimodal speech has not been shown before.…”

Section: Alpha Power Of Eeg Can Be Used To Detect Attention To the mentioning

confidence: 99%

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Look at me when I'm talking to you: Selective attention at a multisensory cocktail party can be decoded using stimulus reconstruction and alpha power modulations

O’Sullivan

Lim

Lalor

2019

Eur J of Neuroscience

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Recent work using electroencephalography has applied stimulus reconstruction techniques to identify the attended speaker in a cocktail party environment. The success of these approaches has been primarily based on the ability to detect cortical tracking of the acoustic envelope at the scalp level. However, most studies have ignored the effects of visual input, which is almost always present in naturalistic scenarios. In this study, we investigated the effects of visual input on envelope‐based cocktail party decoding in two multisensory cocktail party situations: (a) Congruent AV—facing the attended speaker while ignoring another speaker represented by the audio‐only stream and (b) Incongruent AV (eavesdropping)—attending the audio‐only speaker while looking at the unattended speaker. We trained and tested decoders for each condition separately and found that we can successfully decode attention to congruent audiovisual speech and can also decode attention when listeners were eavesdropping, i.e., looking at the face of the unattended talker. In addition to this, we found alpha power to be a reliable measure of attention to the visual speech. Using parieto‐occipital alpha power, we found that we can distinguish whether subjects are attending or ignoring the speaker's face. Considering the practical applications of these methods, we demonstrate that with only six near‐ear electrodes we can successfully determine the attended speech. This work extends the current framework for decoding attention to speech to more naturalistic scenarios, and in doing so provides additional neural measures which may be incorporated to improve decoding accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Alpha Power Of Eeg Can Be Used To Detect Attention To the mentioning

confidence: 99%

Look at me when I'm talking to you: Selective attention at a multisensory cocktail party can be decoded using stimulus reconstruction and alpha power modulations

O’Sullivan

Lim

Lalor

2019

Eur J of Neuroscience

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“…We would thus expect that entrainment can also be utilized to study neural implementations of spatial attentional filters. The spatial separation of two talkers improves the performance by which their acoustic streams (e.g., [48,54]) or even an exact spatial position can be recovered from a neural tracking response [55,56]. If, however, the temporal segregation of signals is compromised (e.g., by experimentally inducing a perceptually aligned sequence of two talkers), our ability to segregate two speakers based on their neural tracking response is much reduced [57].…”

Section: Speech Tracking As a Tool To Study Neural Representationmentioning

confidence: 99%

Neural Entrainment and Attentional Selection in the Listening Brain

Obleser

Kayser

2019

Trends in Cognitive Sciences

352

343

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“…It . https://doi.org/10.1101/475673 doi: bioRxiv preprint research (Bednar and Lalor, 2018;Patel et al, 2018;Wolbers et al, 2011) has shown that the direction of auditory attention is neurally encoded, indicating that it could be possible to decode the attended sound position or trajectory from EEG. Especially the alpha power activity could be tracked to determine the locus of auditory attention (Frey et al, 2014;Haegens et al, 2011;Wöstmann et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

EEG-based detection of the locus of auditory attention with convolutional neural networks

Vandecappelle

Deckers

Das

et al. 2018

Preprint

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When multiple people talk simultaneously, the healthy human auditory system is able to attend to one particular speaker of interest. Recently, it has been demonstrated that it is possible to infer to which speaker someone is attending by relating the neural activity, recorded by electroencephalography (EEG), with the speech signals. This is relevant for an effective noise suppression in hearing devices, in order to detect the target speaker in a multi-speaker scenario. Most auditory attention detection algorithms use a linear EEG decoder to reconstruct the attended stimulus envelope, which is then compared to the original stimuli envelopes to determine the attended speaker. Classifying attention within a short time interval remains the main challenge. We present two different convolutional neural network (CNN)-based approaches to solve this problem. One aims to select the attended speaker from a given set of individual speaker envelopes, and the other extracts the locus of auditory attention (left or right), without knowledge of the speech envelopes. Our results show that it is possible to decode attention within 1-2 seconds, with a median accuracy around 80%, without access to the speech envelopes. This is promising for neuro-steered noise suppression in hearing aids, which requires fast and accurate attention detection. Furthermore, the possibility of detecting the locus of auditory attention without access to the speech envelopes is promising for the scenarios in which per-speaker envelopes are unavailable. It will also enable establishing a fast and objective attention measure in future studies. Index TermsConvolutional neural networks (CNN), auditory attention detection (AAD), electroencephalography (EEG), neurosteered auditory prosthesis, brain-computer interface (BCI)

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

Cited by 32 publications

References 55 publications

Look at me when I'm talking to you: Selective attention at a multisensory cocktail party can be decoded using stimulus reconstruction and alpha power modulations

Look at me when I'm talking to you: Selective attention at a multisensory cocktail party can be decoded using stimulus reconstruction and alpha power modulations

Neural Entrainment and Attentional Selection in the Listening Brain

EEG-based detection of the locus of auditory attention with convolutional neural networks

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