A current goal of affective neuroscience is to reveal the relationship between emotion and dynamic brain activity in specific neural circuits. In humans, noninvasive neuroimaging measures are of primary interest in this endeavor. However, methodological issues, unique to each neuroimaging method, have important implications for the design of studies, interpretation of findings, and comparison across studies. With regard to event-related brain potentials, we discuss the need for dense sensor arrays to achieve reference-independent characterization of field potentials and improved estimate of cortical brain sources. Furthermore, limitations and caveats regarding sparse sensor sampling are discussed. With regard to event-related magnetic field (ERF) recordings, we outline a method to achieve magnetoencephalography (MEG) sensor standardization, which improves effects' sizes in typical neuroscientific investigations, avoids the finding of ghost effects, and facilitates comparison of MEG waveforms across studies. Focusing on functional magnetic resonance imaging (fMRI), we question the unjustified application of proportional global signal scaling in emotion research, which can greatly distort statistical findings in key structures implicated in emotional processing and possibly contributing to conflicting results in affective neuroscience fMRI studies, in particular with respect to limbic and paralimbic structures. Finally, a distributed EEG/MEG source analysis with statistical parametric mapping is outlined providing a common software platform for hemodynamic and electromagnetic neuroimaging measures. Taken together, to achieve consistent and replicable patterns of the relationship between emotion and neuroimaging measures, methodological aspects associated with the various neuroimaging techniques may be of similar importance as the definition of emotional cues and task context used to study emotion.