Independent Component Analysis Outperforms Seed-Based Approach in Detecting fNIRS-based Resting-State Functional Connectivity
This study demonstrates that Independent Component Analysis (ICA) outperforms seed-based approaches in detecting fNIRS-based resting-state functional connectivity by providing higher accuracy and greater consistency between oxygenated and deoxygenated hemoglobin signals across the motor network.