Due to its non-invasive nature and low cost, diffuse optical imaging (DOI) is becoming a commonly used technique to assess functional activation in the brain. When imaging with DOI, two major issues arise in the data analysis: (i) the separation of noise of physiological origin and the recovery of the functional response; (ii) the tomographic image reconstruction problem. This paper focuses on the first issue. Although the general linear model (GLM) has been extensively used in functional magnetic resonance imaging (fMRI), DOI has mostly relied on filtering and averaging of raw data to recover brain functional activation. This is mainly due to the high temporal resolution of DOI which implies a new design of the drift basis modelling physiology. In this paper, we provide (i) a filtering method based on cosine functions that is more adapted than standard averaging techniques for DOI specifically; (ii) a new mode-locking technique to recover small signals and locate them temporally with high precision (shift method). Results on real data show the capability of the shift method to retrieve HbR and HbO(2) peak locations.