Fig. 1: Overview diagram of the spectrum formation approach (SFA).

A The SFA is devised to extract information that is prioritized in the SFC. For a given dataset, the GC and SFC are constructed and evaluated the feature importance separately. The differences between GC and SFC was focused on. B SFA enables knowledge-guided analysis by allowing researchers to define conceptual relationships between states (e.g., through overlap weights), in contrast to conventional classification analyses, which are entirely data-driven.