Mobile EEG as a Valid Alternative to High-Resolution Laboratory EEG Measures

• Mobile EEG reliably detected canonical early and late ERP components in an empathy-for-pain paradigm. • Condition-related effects were absent for early components but robustly expressed in the late positive potential (LPP). • Signal-to-noise ratio and scalp topographies for late components were comparable between mobile and research-grade EEG systems. • Source-level analyses showed similar cortical patterns for early components but divergent patterns for late components across EEG systems. • Multivariate decoding showed low and unreliable classification accuracy for both EEG systems. Mobile electroencephalography (EEG) systems offer a portable and cost-effective alternative to laboratory-based setups, yet their capacity to reliably reproduce established event-related potential (ERP) markers remains an important methodological challenge. In the present study, we assessed the reproducibility of empathy-related ERP effects across a 32-channel mobile EEG system (Emotiv EPOC FLEX) and a 64-channel high-density laboratory system (BioSemi). Participants completed two versions of an empathy-for-pain paradigm in which painful and neutral scenes were presented while EEG activity was recorded. Analyses were conducted across multiple complementary levels, including signal quality metrics, ERP amplitudes and scalp topographies, mass univariate analyses, cross-version comparisons of the paradigm, exploratory source-level analyses, and multivariate decoding of neural representations. Both systems captured canonical ERP components, including early sensory responses (N1 and N2) and later evaluative components (P3 and the late positive potential, LPP). Critically, the most robust condition effect, enhanced LPP amplitudes for painful relative to neutral stimuli, was consistently observed in both systems. In contrast, early condition-related modulations (N1/N2) were not reliably differentiated, and multivariate decoding performance remained close to chance and was largely restricted to late post-stimulus intervals. At the source level, both systems showed broadly similar spatial patterns for early components, whereas later components, particularly the LPP, exhibited partially divergent source distributions. Supplementary sensor-level analyses indicated comparable epoch retention across systems, but noise-related metrics (e.g., baseline variability and signal-to-noise ratio) revealed systematic differences in broadband signal properties and data quality. Overall, these findings indicate that mobile EEG can reliably capture robust late ERP markers of socio-emotional processing in empathy for pain paradigms. However, differences in signal characteristics, spatial resolution, and multivariate sensitivity limit conclusions regarding full equivalence between systems. Mobile EEG therefore represents a viable and accessible tool for capturing stable late neural responses, while high-density laboratory systems remain advantageous for analyses requiring greater temporal precision, spatial resolution, and sensitivity to distributed neural patterns.