Common Spatial Patterns (CSP) are widely used in motor imagery (MI)-based braincomputer interfaces (BCIs). While classification pipelines combine multiple components to optimize performance, MI-based neurofeedback (MI-NF) requires selecting a single spatial filter to drive feedback based on physiologically interpretable sensorimotor activity. This critical step is often implicit and relies on expert judgment. We quantified inter-expert variability in CSP selection using data from 20 subjects in a right-hand MI-NF experiment. Twenty-three BCI or neurophysiology experts independently selected the most physiologically relevant component among six CSP candidates per subject. Inter-expert agreement was fair overall (Fleiss’ κ = 0.256) but varied substantially across subjects. Consensus did not systematically favor the top-ranked component, and selections spanned all six candidates. Although higher-ranked components elicited stronger agreement, substantial variability persisted. These findings identify single-component CSP selection as an overlooked source of methodological heterogeneity in MI-NF systems, highlighting the need for explicit and reproducible selection criteria.