TY - JOUR
T1 - Moving beyond ERP components
T2 - A selective review of approaches to integrate EEG and behavior
AU - Bridwell, David A.
AU - Cavanagh, James F.
AU - Collins, Anne G.E.
AU - Nunez, Michael D.
AU - Srinivasan, Ramesh
AU - Stober, Sebastian
AU - Calhoun, Vince D.
N1 - Funding Information:
This work was supported by grants from the National Institutes of Health 2R01EB005846, R01REB020407 and P20GM103472 to VDC; and National Science Foundation grant number 1539067 and 1658303.
Publisher Copyright:
© 2018 Bridwell, Cavanagh, Collins, Nunez, Srinivasan, Stober and Calhoun.
PY - 2018/3/26
Y1 - 2018/3/26
N2 - Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
AB - Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
KW - Blind source separation
KW - Canonical correlations analysis
KW - Deep learning
KW - EEG
KW - ERP
KW - Hierarchical bayesian model
KW - Partial least squares
KW - Representational similarity analysis
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U2 - 10.3389/fnhum.2018.00106
DO - 10.3389/fnhum.2018.00106
M3 - Review article
C2 - 29632480
AN - SCOPUS:85046899346
SN - 1662-5161
VL - 12
JO - Frontiers in Human Neuroscience
JF - Frontiers in Human Neuroscience
M1 - 106
ER -