Physiological synchrony (PS) refers to the similarity in physiological responses of two or more individuals and may be an informative source of information in the field of affective computing. Up to now, PS has been assessed using either autonomic measures or neural measures. While in literature multiple physiological channels have already been combined into one composite index for PS assessment, multimodal PS, i.e., using a combination of autonomic and neural channels in a single composite index (‘A-N’ multimodal), has remained unexplored. A-N multimodal PS is promising for the robust detection of emotionally or cognitively relevant events, as both autonomic and neural activity are sensitive to these events. The aim of this study is (i) to review analytic approaches that have been used to combine multiple physiological channels into one composite index for PS, and (ii) to view these approaches in the light of their potential applicability to A-N multimodal PS. A literature search was conducted to find studies assessing PS based on a composite index of multiple autonomic channels or multiple channels in electroencephalographic (EEG) recordings. Four studies were found that assessed PS based on a composite index using multiple autonomic channels and 12 studies assessed PS based on a composite index using multiple EEG channels. We found that analytic approaches varied between studies. Some averaged over multiple channels after assessing PS separately per channel (N=4), or averaged over channels before assessing PS (N=1), while others used different linear combinations of channels based on spatio-spectral decomposition (N=1) or correlated component analysis (CCA,N=8). CCA finds linear combinations of channels that are maximally correlated between subjects and has up to now been used to assess neural PS. We suggest that this method may be most appropriate for the exploration of multimodal PS assessment.