Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection

Narayanan, Abhijith Mundanad; Bertrand, Alexander

doi:10.1101/593194

Cited by 21 publications

(49 citation statements)

References 29 publications

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“…They did not report optimal electrode positions. Narayanan and Bertrand (2019) also analyzed the channel subset selection problem in the context of auditory attention decoding, using a channel selection strategy based on the same utility metric discussed in the present study, but without imposing the symmetric grouping approach discussed in Section 2.2.5. They found that, on average, the decoding accuracy remained stable when using a number of channels greater or equal to 10.…”

Section: Discussionmentioning

confidence: 99%

Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

Montoya-Martínez

Vanthornhout

Bertrand

et al. 2019

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Measurement of neural tracking of natural running speech from the 15 electroencephalogram (EEG) is an increasingly popular method in auditory neuroscience 16 and has applications in audiology. The method involves decoding the envelope of the 17 speech signal from the EEG signal, and calculating the correlation with the envelope 18 that was presented to the subject. Typically EEG systems with 64 or more electrodes 19 are used. However, in practical applications, set-ups with fewer electrodes are required. 20 Here, we determine the optimal number of electrodes, and the best position to place 21 a limited number of electrodes on the scalp. We propose a channel selection strategy, 22 aiming to induce the selection of symmetric EEG channel groups in order to avoid 23 hemispheric bias. The proposed method is based on a utility metric, which allows 24 a quick quantitative assessment of the influence of each group of EEG channels on 25 the reconstruction error. We consider two use cases: a subject-specific case, where 26 the optimal number and positions of the electrodes is determined for each subject 27 individually, and a subject-independent case, where the electrodes are placed at the same 28 positions (in the 10-20 system) for all the subjects. We evaluated our approach using 64-29 channel EEG data from 90 subjects. Surprisingly, in the subject-specific case we found 30 that the correlation between actual and reconstructed envelope first increased with 31 decreasing number of electrodes, with an optimum at around 20 electrodes, yielding 38% 32 higher correlations using the optimal number of electrodes. In the subject-independent 33 case, we obtained a stable decoding performance when decreasing from 64 to 32 channels. 34When the number of channels was further decreased, the correlation decreased. For 35 a maximal decrease in correlation of 10%, 32 well-placed electrodes were sufficient in 36 87% of the subjects. Practical electrode placement recommendations are given for 8, 37 16, 24 and 32 electrode systems. 38 42 also has applications in domains such as audiology, as part of an objective measure 43 of speech intelligibility (Vanthornhout et al., 2018; Lesenfants et al., 2019), and coma 44 science (Braiman et al., 2018). 45The relationship between the stimulus and the brain response can be studied using 46 two different models (e.g., Crosse et al., 2016; Lalor and Foxe, 2010; Ding and Simon, 47 2012; Verschueren et al., 2019; Vanthornhout et al., 2018): in the forward model (also 48 know as encoding model), we determine a linear mapping from the stimulus to the 49 brain response. On the other hand, in the backward model (also known as stimulus 50 reconstruction), we determine the linear mapping from the brain response to the stimulus. 51Backward models are referred to as decoding models, because they attempt to reverse 52 the data generation process. Both the forward and backward models involve the solution 53 of a linear least squares (LS) regression problem. The quality of the reconstruction is 54 ...

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Section: Discussionmentioning

confidence: 99%

Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

Montoya-Martínez

Vanthornhout

Bertrand

et al. 2019

Preprint

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“…(2017) ) would improve. For each 120 s trial, we found the mean and standard deviation of the TRF of the channel ‘Tp7’ (one of the channels resulting in high AAD performance as shown in Narayanan and Bertrand (2019) ), across trials, as shown in Fig. 9 .…”

Section: Validation Experimentsmentioning

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

et al. 2020

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A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive 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 infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials. Therefore, we present a data-driven method 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 first estimates the stimulus-driven neural response using the given stimulus, 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 using EEG, our method resulted in more accurate 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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“…To gain a better understanding of the denoising ability of our approach, we estimated short-term TRFs (as in section III-B) for the attended and unattended stimuli on 120 s trials, from the raw EEG as well as the SI-GEVD filtered EEG this time projected back to the electrode space and investigated whether attention decoding from the TRFs directly (as in Akram et al (2017)) would improve. For each 120 s trial, we found the mean and standard deviation of the TRF of the channel 'Tp7' (one of the channels resulting in high AAD performance as shown in Narayanan and Bertrand (2019)), across trials, as shown in figure 9.…”

Section: Auditory Attention Decodingmentioning

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

Self Cite

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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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Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection

Cited by 21 publications

References 29 publications

Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

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

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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