TY  - GEN
A1  - Bridwell, David A.
A1  - Cavanagh, James F.
A1  - Collins, Anne G. E.
A1  - Nunez, Michael D.
A1  - Srinivasan, Ramesh
A1  - Stober, Sebastian
A1  - Calhoun, Vince D.
T1  - Moving beyond ERP components
BT  - a selective review of approaches to integrate EEG and behavior
T2  - Postprints der Universität Potsdam : Humanwissenschaftliche Reihe
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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 656 
KW  - EEG
KW  - ERP
KW  - blind source separation
KW  - partial least squares
KW  - canonical correlations analysis
KW  - representational similarity analysis
KW  - deep learning
KW  - hierarchical Bayesian model
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-459667
SN  - 1866-8364
IS  - 656
ER  -