Recipes for the linear analysis of EEG

By Lucas C. Parra, Clay D. Spence, Adam D. Gerson, Paul Sajda. NeuroImage, 2005

602 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/96d0a07b6cc21c88eb5924315fa81d3b8de60263

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Citations in the corpus

On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014, methodology): “A different way of constructing activation patterns from latent factors is provided by Parra et al. (2005).”
Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008, background): “6 The matrices A and W are sometimes called the mixing and de-mixing matrix or the forward and backward model ([41]) in other contexts.”
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, methodology): “Among these methods are so called linear spatial ﬁlters and include classical techniques such as PCA/ICA [18], Common Spatial Patterns for BCI applications [19] or beamforming methods for source localization [20].”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, background): “…et al., 2009; Höhne et al., 2010; Schaefer et al., 2010; Schreuder et al., 2010; Treder and Blankertz, 2010) and data analysis technology (Parra et al., 2005, 2008; Dornhege et al., 2007b; Lotte et al., 2007; Blankertz et al., 2008a; Tomioka and Müller, 2010) as to allow for an easier,…”
Interpretable Deep Neural Networks for Single-Trial EEG Classification (2016, background): “Although researchers have recently started introducing this promising technology into the domain of cognitive neuroscience [5] and Brain-Computer Interfacing (BCI) [6], [7], most of the current techniques in these fields are still based on linear methods [8], [9].”
Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG (2009, methodology): “A linear classifier based on logistic regression was used to find an optimal projection in the sensor space for discriminating between the 2 conditions over a specific temporal window (14, 15).”
The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli (2016, background): “…2014)—which relates the amplitude of neural oscillations to stimulus features or behaviorally relevant parameters—have speciﬁcally used linear models based on the fact that superposition of such oscillations is known to be linear and instantaneous (Parra et al., 2005; Nunez and Srinivasan, 2006).”