Online brain imaging by QR factorization of normalized regressor functions

This paper presents an online framework for an effective real-time brain imaging application. The idea is to estimate the activity parameters as coefficients of a linear model by QR factorization of normalized regressor functions. The recursive form of the normalization process makes the proposed strategy computationally efficient and applicable for online utilization. The t-statistics is used to compute the statistical significance of the estimated activity parameters. The results obtained by performing online physiological experiments verify the effectiveness of the proposed methodology by providing the brain activity for the range of measuring optodes. The technique has potential applications in real-time brain imaging. Furthermore, this method can be extended to real-time brain computer and brain machine interface systems.