Dynamic time warping for enhanced fNIRS-based motor assessment during lower limb kinematics

Abstract

Understanding cortical activation during lower limb movement is crucial for advancing neurorehabilitation, motor control research, and brain-computer interface (BCI) applications. Functional near-infrared spectroscopy (fNIRS) offers a non-invasive approach to monitoring hemodynamic changes associated with movement-related brain activity. This study investigates fNIRS channel activation patterns during lower limb kinematics to identify key motor regions involved in movement execution, utilizing both Pearson Correlation (PC) and Dynamic Time Warping (DTW) for signal analysis. Nine participants performed six controlled leg movements in a single session, and then repeated the same movements in a subsequent session after a short break, while fNIRS data were recorded from the motor cortex. Signal processing involved motion artifact correction, normalization, and statistical analysis to assess activation consistency. PC and DTW were employed to compare reference and observed signal variations. While DTW exhibited lower average reproducibility than PC, it was chosen for final analysis due to its sensitivity to temporal dynamics and non-linear relationships in the fNIRS signals. The results highlight that fNIRS channels 33, 34, and 37 consistently exhibit reproducible activation patterns associated with lower limb movement. These findings support the effectiveness of fNIRS in capturing neural dynamics related to lower limb kinematics, and demonstrate the utility of DTW for identifying subtle but significant task-related activations. Future research should include larger sample sizes and more varied movement tasks to further validate the reliability of fNIRS-based motor assessments and explore the potential of DTW in real-time motor control applications.