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  - 
TY  - JOUR
A1  - Bürki-Foschini, Audrey Damaris
A1  - Frossard, Jaromil
A1  - Renaud, Olivier
T1  - Accounting for stimulus and participant effects in event-related potential analyses to increase the replicability of studies
JF  - Journal of neuroscience methods
N2  - Background: Event-related potentials (ERPs) are increasingly used in cognitive science. With their high temporal resolution, they offer a unique window into cognitive processes and their time course. In this paper, we focus on ERP experiments whose designs involve selecting participants and stimuli amongst many. Recently, Westfall et al. (2017) highlighted the drastic consequences of not considering stimuli as a random variable in fMRI studies with such designs. Most ERP studies in cognitive psychology suffer from the same drawback. New method: We advocate the use of the Quasi-F or Mixed-effects models instead of the classical ANOVA/by-participant F1 statistic to analyze ERP datasets in which the dependent variable is reduced to one measure per trial (e.g., mean amplitude). We combine Quasi-F statistic and cluster mass tests to analyze datasets with multiple measures per trial. Doing so allows us to treat stimulus as a random variable while correcting for multiple comparisons. Results: Simulations show that the use of Quasi-F statistics with cluster mass tests allows maintaining the family wise error rates close to the nominal alpha level of 0.05. Comparison with existing methods: Simulations reveal that the classical ANOVA/F1 approach has an alarming FWER, demonstrating the superiority of models that treat both participant and stimulus as random variables, like the Quasi-F approach. Conclusions: Our simulations question the validity of studies in which stimulus is not treated as a random variable. Failure to change the current standards feeds the replicability crisis.
KW  - Cluster mass
KW  - ERP
KW  - Quasi-F
KW  - Mixed-effects model
KW  - Replicability crisis
KW  - Stimulus as fixed-effect fallacy
Y1  - 2018
U6  - https://doi.org/10.1016/j.jneumeth.2018.09.016
SN  - 0165-0270
SN  - 1872-678X
VL  - 309
SP  - 218
EP  - 227
PB  - Elsevier
CY  - Amsterdam
ER  -