Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling

Akram, Sahar; Presacco, Alessandro; Simon, Jonathan Z.; Shamma, Shihab A.; Babadi, Behtash

doi:10.1016/j.neuroimage.2015.09.048

Cited by 74 publications

(127 citation statements)

References 27 publications

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“…This set-up has been extended to reconstruct the attended speech from noisy single-trial EEG recordings [71,72], an especially important development for the EEG domain where noise reduction techniques coupled with averaging a high number of trials are typically necessary to estimate the neural signal. With this established framework, biologically plausible models are being designed to reconstruct the input sound from neural recordings, using dynamic state-space models [73] and deep neural networks [74], extending our understanding of attentional gain at the systems level.…”

Section: Models Of Auditory Attentionmentioning

confidence: 99%

Modelling auditory attention

Kaya¹,

Elhilali²

2017

Phil. Trans. R. Soc. B

103

101

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Sounds in everyday life seldom appear in isolation. Both humans and machines are constantly flooded with a cacophony of sounds that need to be sorted through and scoured for relevant information—a phenomenon referred to as the ‘cocktail party problem’. A key component in parsing acoustic scenes is the role of attention, which mediates perception and behaviour by focusing both sensory and cognitive resources on pertinent information in the stimulus space. The current article provides a review of modelling studies of auditory attention. The review highlights how the term attention refers to a multitude of behavioural and cognitive processes that can shape sensory processing. Attention can be modulated by ‘bottom-up’ sensory-driven factors, as well as ‘top-down’ task-specific goals, expectations and learned schemas. Essentially, it acts as a selection process or processes that focus both sensory and cognitive resources on the most relevant events in the soundscape; with relevance being dictated by the stimulus itself (e.g. a loud explosion) or by a task at hand (e.g. listen to announcements in a busy airport). Recent computational models of auditory attention provide key insights into its role in facilitating perception in cluttered auditory scenes.This article is part of the themed issue ‘Auditory and visual scene analysis’.

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Section: Models Of Auditory Attentionmentioning

confidence: 99%

Modelling auditory attention

Kaya¹,

Elhilali²

2017

Phil. Trans. R. Soc. B

103

101

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“…This will be the case if N > n b and columns in U i are linearly independent. Then, B i can be computed as in (7). The experimental results show that once the number k 2 becomes 90 or higher, one of the matrices U T i U i becomes singular leading to inconsistency and over-fitting.…”

Section: Resultsmentioning

confidence: 99%

“…Regression methods and stimulus reconstruction approaches have already been successfully applied to intracranial EEG or electroencephalographic (ECoG) data [4], [5], [6], MEG data [3], [7] and EEG data [8], [2]. Although impressive results can be obtained using ECoG data, ECoG measurements are invasive and can only be used with listeners under medical care; as such, these approaches are not plausible for everyday applications.…”

Section: Introductionmentioning

confidence: 99%

A system identification approach to determining listening attention from EEG signals

Aličković

Lunner

Gustafsson

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Abstract-We still have very little knowledge about how our brains decouple different sound sources, which is known as solving the cocktail party problem. Several approaches; including ERP, time-frequency analysis and, more recently, regression and stimulus reconstruction approaches; have been suggested for solving this problem. In this work, we study the problem of correlating of EEG signals to different sets of sound sources with the goal of identifying the single source to which the listener is attending. Here, we propose a method for finding the number of parameters needed in a regression model to avoid overlearning, which is necessary for determining the attended sound source with high confidence in order to solve the cocktail party problem.

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“…This problem is particularly important when using neural signals with a lower signal to noise ratio (such as around the ear [23], or in ear EEG [28]). It is possible that more elaborate decoding algorithms can be used to speed up decoding and provide a better trade-off between decoding-accuracy and transition-time [27]. …”

Section: Discussionmentioning

confidence: 99%

Neural decoding of attentional selection in multi-speaker environments without access to clean sources

et al. 2017

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Objective People who suffer from hearing impairments can find it difficult to follow a conversation in a multi-speaker environment. Current hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation amongst many without knowing which speaker the user is attending to. Cognitively controlled hearing aids that use auditory attention decoding (AAD) methods are the next step in offering help. Translating the successes in AAD research to real-world applications poses a number of challenges, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD. Approach We present an end-to-end system that (1) receives a single audio channel containing a mixture of speakers that is heard by a listener along with the listener’s neural signals, (2) automatically separates the individual speakers in the mixture, (3) determines the attended speaker, and (4) amplifies the attended speaker’s voice to assist the listener. Main results Using invasive electrophysiology recordings, we identified the regions of the auditory cortex that contribute to AAD. Given appropriate electrode locations, our system is able to decode the attention of subjects and amplify the attended speaker using only the mixed audio. Our quality assessment of the modified audio demonstrates a significant improvement in both subjective and objective speech quality measures. Significance Our novel framework for AAD bridges the gap between the most recent advancements in speech processing technologies and speech prosthesis research and moves us closer to the development of cognitively controlled hearable devices for the hearing impaired.

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Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling

Cited by 74 publications

References 27 publications

Modelling auditory attention

Modelling auditory attention

A system identification approach to determining listening attention from EEG signals

Neural decoding of attentional selection in multi-speaker environments without access to clean sources

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