Near Infrared Spectroscopy (NIRS)-based Brain Computer Interfaces (NIRS-BCI) rely mainly on the mean concentration changes and slope of the hemodynamic responses in separate recording channels to detect the mental-task related brain activity. Nevertheless, spatial patterns across the measurement channels are also present and should be taken into account for reliable evaluation of the aforementioned detection. In this work the Dirichlet Energy of NIRS signals over a graph is considered for the definition of a measure that would take into account the spatial NIRS features and would integrate the activity of multiple NIRS channels for robust mental task related activity detection. The application of the proposed measure on a real NIRS dataset demonstrates the efficiency of the proposed measure.