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

Abstract

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.

Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech

“…In the current study, we apply a data-driven method to remove non-stimulus-related activity from the EEG signals and automatically select the channels of interest, which is described in detail by Das, Vanthornhout, Francart, & Bertrand (2019). This method makes use of the Generalized Eigenvalue Decomposition GEVD (Hassani, Bertrand, & Moonen, 2016), which has been used to remove artefacts from the EEG (Somers, Francart, & Bertrand, 2018), and cancel EEG noise (Hajipour Sardouie, Shamsollahi, Albera, & Merlet, 2015;Serizel, Moonen, Van Dijk, & Wouters, 2014).…”