The effect of head-related filtering and ear-specific decoding bias on auditory attention detection

Das, Neetha; Biesmans, Wouter; Bertrand, Alexander; Francart, Tom

doi:10.1088/1741-2560/13/5/056014

Cited by 76 publications

(114 citation statements)

References 26 publications

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“…While Das et al. () report a drop of ~11% for the ‘other ear’ decoder this is likely to be the maximum effect for an ear bias as in that experiment the speakers were presented at ±90°.…”

Section: Discussionmentioning

confidence: 67%

“…Nonetheless, our cross-decoding effects are quite large (drops by 9.4% and 23%) for a 30° angle of separation between speakers. While Das et al (2016) report a drop of ~11% for the 'other ear' decoder this is likely to be the maximum effect for an ear bias as in that experiment the speakers were presented at ±90°.…”

Section: Limitationsmentioning

confidence: 67%

“…It is important to point out some limitations of the current study. Firstly, the angle of presentation across conditions (0° for AVc speech and 30° for AVi speech) should be counterbalanced to account for any possible ear bias in the decoders (Das, Biesmans, Bertrand, & Francart, ). Nonetheless, our cross‐decoding effects are quite large (drops by 9.4% and 23%) for a 30° angle of separation between speakers.…”

Section: Discussionmentioning

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: 67%

Section: Limitationsmentioning

confidence: 67%

Section: Discussionmentioning

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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“…All data were split into 30 second trials and the EEG data were bandpass filtered between 1-9 Hz (the frequency range of most interest for tracking speech stimuli [16]- [18]), and down-sampled to 20 Hz. The EEG decoder was trained using all the recorded data, except the trial under test using the envelope extracted from the (known) attended speech stimulus.…”

Section: Methodsmentioning

confidence: 99%

EEG-based attention-driven speech enhancement for noisy speech mixtures using N-fold multi-channel Wiener filters

Das

Eyndhoven

Francart

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Hearing prostheses have built-in algorithms to perform acoustic noise reduction and improve speech intelligibility. However, in a multi-speaker scenario the noise reduction algorithm has to determine which speaker the listener is focusing on, in order to enhance it while suppressing the other interfering sources. Recently, it has been demonstrated that it is possible to detect auditory attention using electroencephalography (EEG). In this paper, we use multi-channel Wiener filters (MWFs), to filter out each speech stream from the speech mixtures in the microphones of a binaural hearing aid, while also reducing background noise. From the demixed and denoised speech streams, we extract envelopes for an EEG-based auditory attention detection (AAD) algorithm. The AAD module can then select the output of the MWF corresponding to the attended speaker. We evaluate our algorithm in a two-speaker scenario in the presence of babble noise and compare it to a previously proposed algorithm. Our algorithm is observed to provide speech envelopes that yield better AAD accuracies, and is more robust to variations in speaker positions and diffuse background noise.

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“…TRFs not only have high temporal precision but are also sensitive to attentional modulation (Akram et al, 2017;Power et al, 2012). Forward modeling thus comes with the advantages of being able to investigate the TRFs and gain a better understanding of how our brain handles auditory stimuli Ding and Simon, 2012a,b), and also the possibility to identify the brain regions involved with stimulus processing (Das et al, 2016;Ding and Simon, 2012b;Etard et al, 2018;Power et al, 2012).…”

mentioning

confidence: 99%

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Das

Vanthornhout

Francart

et al. 2019

Preprint

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A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using noninvasive techniques like magneto-or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to inform the design of a spatial filter that projects the data onto high-SNR directions. However, collecting enough repeated trials is often impractical and even impossible in some paradigms. Therefore, we present a data-driven spatial filter design that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method uses the stimulus-driven neural response, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus, our method resulted in better short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.

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The effect of head-related filtering and ear-specific decoding bias on auditory attention detection

Abstract: This work shows the importance of using realistic binaural listening conditions and training on a balanced set of experimental conditions to obtain results that are more representative for the true AAD performance in practical applications.

Cited by 76 publications

References 26 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

EEG-based attention-driven speech enhancement for noisy speech mixtures using N-fold multi-channel Wiener filters

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

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