*Correspondence:
This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology.
Edited by: F-Xavier Alario, Centre National de la Recherche Scientifique and Aix-Marseille Université, France
Reviewed by: Lesya Ganushchak, Max Planck Institute, Netherlands; Jan Kujala, Aalto University, Finland; Guillaume A. Rousselet, University of Glasgow, UK
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Recently, the field of spoken-word production has seen an increasing interest in the use of the electroencephalogram (EEG), mainly for event-related potentials (ERPs). These are exciting times to be a language production researcher. However, no matter how much we would like our results to speak to our theories, they can only do so if our methods are formally correct and valid, and reported in ways that allow replicability. Inappropriate practices in signal processing and statistical testing, when applied to our investigations, may render our conclusions invalid or non-generalizable. Here, we first present some issues in signal processing and statistical testing that we think deserve more attention when analysing data, reporting results, and making inferences. These issues are not new to electrophysiology, so our sole contribution is to reiterate them in order to provide pointers to literature where they have been discussed in more detail and solutions have been proposed. We then discuss other issues pertinent to our investigations of overt word-production because of the effects (and potential confounds) that speaking will have on the signal. Although we cannot provide answers to some of the issues raised, we invite researchers in the field to jointly work on solutions so that the topic of the electrophysiology of word production can thrive on solid grounds.
A common step in ERP analysis is filtering. In many studies, all we can find regarding the filtering procedure are cut-off values. However, this is incomplete information since a filter has other important parameters that affect the outcome of the filtering procedure. Different software will vary in their default values for these parameters. Researchers should not only try to understand how different filter parameters affect the signal studied (e.g., Widmann et al.,
Another common step is to define a pre-stimulus baseline period, which can then be used to normalize the rest of the signal. This pre-stimulus baseline provides a good indication of the signal-to-noise ratio (SNR) in the data. If an ERP difference post-stimulus is similar in magnitude as pre-stimulus differences, the post-stimulus difference is likely noise, not an effect induced by our manipulation (e.g., Woodman,
Results that cannot be explained by mere chance are highly informative for our theories. However, by using statistical tests inappropriately, we may make incorrect inferences regarding the probability of our results. An example is the well-known increased family-wise error rate (FWER, the probability of false positives amongst all multiple tests performed at some alpha-level) associated with the common practice of testing multiple time windows for significance (see Supplementary Material for an example). Alternatively, certain time points/windows may be selected for statistical testing on the basis of some criterion. However, a biased selection of this criterion also results in an inflation of false positives (Kilner,
For many years the dominant notion was that muscle activity associated with overt production would contaminate the EEG signal. Recently, that view has changed and the increasing number of ERP studies employing overt production is claimed to support the feasibility of combining EEG with overt speech. However, an increasing number in ERP studies employing overt production does not confirm that measuring ERPs with overt production is unproblematic. Moreover, even though it has been argued that “artifact-free brain responses can be measured up to at least 400 ms post-stimulus presentation” (Ganushchak et al.,
Artifacts aside, another problem is physiological in nature. We are interested in which (and when) differences emerge in the waveforms time-locked to a stimulus as a function of our experimental manipulation. We know that our manipulation elicits a difference between conditions—an effect—in vocal response times (RTs). However, breathing and articulation are functions controlled by the brain. So RT differences are likely to be accompanied by systematic differences between the conditions in the relative timing of speech-related artifacts and of brain activity related to the control of speech that are independent of linguistic effects. This problem is well-known and has been mentioned, for example, by Luck (
Researchers have measured movement-related cortical potentials preceding mouth opening (Deecke et al.,
Electromyogenic (EMG) activity recorded from mouth muscles provides valuable information on this issue. EMG activity, either directly related to the articulation of the response or merely preparatory, can start as early as 250 ms after stimulus onset (Riès et al.,
Figure
Of course one could argue that RTs are shorter or longer for some cognitive reason, so the differences shown in Figure
One may ask whether early effects are problem-free in this respect. However, the answer is complicated by the physiological issues described above in combination with technical issues. One of them may be caused by acausal filters (i.e., the filter applied forwards and then backwards). Due to this procedure, later slow components (possibly speech-related potentials and artifacts) can affect earlier parts of the signal (Acunzo et al.,
In conclusion, we need to consider that the ERP differences observed in conditions differing in RT are partly reflecting the relative difference in the timing of brain activity related to speaking (breathing and mouth movements) and other speech-related artifacts, in addition to our cognitive manipulation. We should also consider the possibility that our cognitive manipulation is
The Associate Editor, Dr. F-Xavier Alario, declares that despite having collaborated with author Dr. Stephanie K. Ries, the review process was handled objectively. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors are truly indebted to Kristoffer Dahlslätt, Natalia Shitova, and Andreas Widmann for helpful discussions. The authors are funded by grants from the Netherlands Organization for Scientific Research (#446-13-009, to Vitória Piai), NIDCD Grant (F32DC013245, to Stéphanie K. Riès), NINDS Grant (R37 NS21135, to Robert T. Knight). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The Supplementary Material for this article can be found online at: