Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent.

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Interventions to improve working memory, e.g. by combining task rehearsal and non-invasive brain stimulation, are gaining popularity. Many factors, however, affect the outcome of these interventions. We hypothesize that working memory capacity at baseline predicts how an individual performs on a working memory task, by setting limits on the benefit derived from tDCS when combined with strategy instructions; specifically, we hypothesize that individuals with low capacity will benefit the most. Eighty-four participants underwent two sessions of an adaptive working memory task (n-back) on two consecutive days. Participants were split into four independent groups (SHAM vs ACTIVE stimulation and STRATEGY vs no STRATEGY instructions). For the purpose of analysis, individuals were divided based on their baseline working memory capacity. Results support our prediction that the combination of tDCS and strategy instructions is particularly beneficial in low capacity individuals. Our findings contribute to a better understanding of factors affecting the outcome of tDCS when used in conjunction with cognitive training to improve working memory. Moreover, our results have implications for training regimens, e.g., by designing interventions predicated on baseline cognitive abilities, or focusing on strategy development for specific attentional skills.

This improved compact setup may be relevant to MR laboratories interested in reducing the sensitivity of their EEG-fMRI experimental setup to external noise sources, setting up an EEG-fMRI workplace for the first time, or for creating a more reproducible configuration of equipment and cables. Implications for safety and ergonomics are discussed.