Group "Language and Computation in Neural Systems" and by the Netherlands Organization for Scientific Research (grant 016.Vidi.188.029).

Baseline correction plays an important role in past and current methodological debates in ERP research (e.g., the Tanner vs. Maess debate in the Journal of Neuroscience Methods), serving as a potential alternative to strong high‐pass filtering. However, the very assumptions that underlie traditional baseline also undermine it, implying a reduction in the signal‐to‐noise ratio. In other words, traditional baseline correction is statistically unnecessary and even undesirable. Including the baseline interval as a predictor in a GLM‐based statistical approach allows the data to determine how much baseline correction is needed, including both full traditional and no baseline correction as special cases. This reduces the amount of variance in the residual error term and thus has the potential to increase statistical power.

Individual alpha frequency (IAF) is a promising electrophysiological marker of interindividual differences in cognitive function. IAF has been linked with trait-like differences in information processing and general intelligence, and provides an empirical basis for the definition of individualized frequency bands. Despite its widespread application, however, there is little consensus on the optimal method for estimating IAF, and many common approaches are prone to bias and inconsistency. Here, we describe an automated strategy for deriving two of the most prevalent IAF estimators in the literature: peak alpha frequency (PAF) and center of gravity (CoG). These indices are calculated from resting-state power spectra that have been smoothed using a Savitzky-Golay filter (SGF). We evaluate the performance characteristics of this analysis procedure in both empirical and simulated EEG data sets. Applying the SGF technique to resting-state data from n = 63 healthy adults furnished 61 PAF and 62 CoG estimates. The statistical properties of these estimates were consistent with previous reports. Simulation analyses revealed that the SGF routine was able to reliably extract target alpha components, even under relatively noisy spectral conditions. The routine consistently outperformed a simpler method of automated peak detection that did not involve spectral smoothing. The SGF technique is fast, open source, and available in two popular programming languages (MATLAB, Python), and thus can easily be integrated within the most popular M/EEG toolsets (EEGLAB, FieldTrip, MNE-Python). As such, it affords a convenient tool for improving the reliability and replicability of future IAF-related research.

In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig 2004; Lopez-Calderon and Luck 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

Experimental research on behavior and cognition frequently rests on stimulus or subject selection where not all characteristics can be fully controlled, even when attempting strict matching. For example, when contrasting patients to controls, variables such as intelligence or socioeconomic status are often correlated with patient status. Similarly, when presenting word stimuli, variables such as word frequency are often correlated with primary variables of interest. One procedure very commonly employed to control for such nuisance effects is conducting inferential tests on confounding stimulus or subject characteristics. For example, if word length is not significantly different for two stimulus sets, they are considered as matched for word length. Such a test has high error rates and is conceptually misguided. It reflects a common misunderstanding of statistical tests: interpreting significance not to refer to inference about a particular population parameter, but about 1. the sample in question, 2. the practical relevance of a sample difference (so that a nonsignificant test is taken to indicate evidence for the absence of relevant differences). We show inferential testing for assessing nuisance effects to be inappropriate both pragmatically and philosophically, present a survey showing its high prevalence, and briefly discuss an alternative in the form of regression including nuisance variables.

The recent trend away from ANOVA-based analyses places experimental investigations into the neurobiology of cognition in more naturalistic and ecologically valid designs within reach. Using mixed-effects models for epoch-based regression, we demonstrate the feasibility of examining event-related potentials (ERPs), and in particular the N400, to study the neural dynamics of human auditory language processing in a naturalistic setting. Despite the large variability between trials during naturalistic stimulation, we replicated previous findings from the literature: the effects of frequency, animacy, and word order and find previously unexplored interaction effects. This suggests a new perspective on ERPs, namely, as a continuous modulation reflecting continuous stimulation instead of a series of discrete and essentially sequential processes locked to discrete events.

M/EEG research using naturally spoken stories as stimuli has focused largely on speech and not language processing. The temporal resolution of M/EEG is a two-edged sword, allowing for the study of the fine acoustic structure of speech, yet easily overwhelmed by the temporal noise of variation in constituent length. Recent theories on the neural encoding of linguistic structure require the temporal resolution of M/EEG, yet suffer from confounds when studied on traditional, heavily controlled stimuli. Recent methodological advances allow for synthesising naturalistic designs and traditional, controlled designs into effective M/EEG research on naturalistic language. In this review, we highlight common threads throughout the at-times distinct research traditions of speech and language processing. We conclude by examining the tradeoffs and successes of three M/EEG studies on fully naturalistic language paradigms and the future directions they suggest.

Previous work found that single-session focused attention meditation (FAM) enhanced motor sequence learning through increased cognitive control as a mechanistic action, although electrophysiological correlates of sequence learning performance following FAM were not investigated. We measured the persistent frontal N2 event-related potential (ERP) that is closely related to cognitive control processes and its ability to predict behavioural measures. Twenty-nine participants were randomised to one of three conditions reflecting the level of FAM experienced prior to a serial reaction time task (SRTT): 21 sessions of FAM (FAM21, N= 12), a single FAM session (FAM1, N= 9) or no preceding FAM control (Control, N= 8). Continuous 64-channel EEG were recorded during SRTT and N2 amplitudes for correct trials were extracted. Component amplitude, regions of interests, and behavioural outcomes were compared using mixed effects regression models between groups. FAM21 exhibited faster reaction time performances in majority of the learning blocks compared to FAM1 and Control. FAM21 also demonstrated a significantly more pronounced N2 over majority of anterior and central regions of interests during SRTT compared to the other groups. When N2 amplitudes were modelled against general learning performance, FAM21 showed the greatest rate of amplitude decline over anterior and central regions. The combined results suggest that FAM training provided greater cognitive control enhancement for improved sequence learning performance compared to the other groups. Importantly, FAM training facilitates dynamic modulation of cognitive control: lower levels of general learning performance was supported by greater levels of activation, whilst higher levels of general learning required less activation.