Abstract The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization in brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation timeseries at the brain source level due to modeling complexities and insufficient geometric/anatomical data.