Abstract
Overt articulation produces strong artifacts in the electroencephalogram and in event-related potentials (ERPs), posing a serious problem for investigating language production with these variables. Here we describe the properties of articulation-related artifacts and propose a novel correction procedure. Experiment 1 co-recorded ERPs and trajectories of the articulators with an electromagnetic articulograph from a single participant. The generalization of the findings from the single participant to standard picture naming was investigated in Experiment 2. Both experiments provided evidence that articulation-induced artifacts may start up to 300 ms or more prior to voice onset or voice key onset—depending on the specific measure; they are highly similar in topography across many different phoneme patterns and differ mainly in their time course and amplitude. ERPs were separated from articulation-related artifacts with residue iteration decomposition (RIDE). After obtaining the artifact-free ERPs, their correlations with the articulatory trajectories dropped near to zero. Artifact removal with independent component analysis was less successful; while correlations with the articulatory movements remained substantial, early components prior to voice onset were attenuated in reconstructed ERPs. These findings offer new insights into the nature of articulation artifacts; together with RIDE as method for artifact removal the present report offers a fresh perspective for ERP studies requiring overt articulation.
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Acknowledgments
This work was partially supported by Hong Kong Baptist University (HKBU) Strategic Development Fund, the HKBU Faculty Research Grant (FRG2/13-14/022), the HKBU Matching Proof-of-Concept Fund and Germany-Hong Kong Joint Research Scheme (G-HK012/12), the National Natural Science Foundation of China (Grant No. 11275027) to G.O. and C.Z., the Germany-Hong Kong Joint Research Scheme (PPP 56062391) to W.S, and grant AB 277 4 from the German Research Council to RAR. This research was conducted using the resources of the High Performance Cluster Computing Centre, Hong Kong Baptist University, which receives funding from RGC, University Grant Committee of the HKSAR and HKBU.
We thank Susanne Fuchs for generously supporting us with her expertise on phonetics, Joerg Dreyer and Guido Kiecker for their technical support during the EMA-EEG recording session, and Leonardo Lancia for preprocessing the EMA data and post-synchronizing the EMA-EEG data.
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Guang Ouyang and Werner Sommer have contributed equally to this study.
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Appendices
Appendix
Appendix I. Matlab Script for Experiment 1
For Experiment 1 the epoched single trial ERP data after standard artifact rejection was prepared as a three-dimensional matrix for each participant and each condition (named data in Matlab workspace). The first dimension is the data sampling points containing points ranging from −200 to 2000 ms. The data was down-sampled to 200 Hz to increase computational speed. The second dimension is electrodes and the third is single trials. The script for parameter setup and calling RIDE toolbox for the two different schemes (S + C, S + R) are as follows:
Separating the speech ERP data into S + C:
Separating the speech ERP data into S + R:
The time windows for each component were chosen to safely cover each component by visual inspection of the original ERP waveform. The time window is always relative to stimulus onset but for the R component (when the component name was named ‘r’ in the program), the time window is relative to response time (here the voice onset). That is why there is a negative value for the R time window [−500, 800 ms]. The variable ‘rt’ is the one dimension vector for response times. More details can be found in the manual from http://cns.hkbu.edu.hk/RIDE.htm.
Matlab Script for Experiment 2
For Experiment 2, the data was also down-sampled to 200 Hz to increase computational speed. The script for parameter setup and calling RIDE toolbox is as follows:
Appendix II
See Table 2.
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Ouyang, G., Sommer, W., Zhou, C. et al. Articulation Artifacts During Overt Language Production in Event-Related Brain Potentials: Description and Correction. Brain Topogr 29, 791–813 (2016). https://doi.org/10.1007/s10548-016-0515-1
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DOI: https://doi.org/10.1007/s10548-016-0515-1