Separating brain imaging signals by maximizing their autocorrelations is an important component of blind source separation (BSS). Canonical correlation analysis (CCA), one of leading BSS techniques, has been widely used for analyzing optical imaging (OI) and functional magnetic resonance imaging (fMRI) data. However, because of the need to reduce dimensionality and ignore spatial autocorrelation, CCA is problematic for separating temporal signal sources. To solve the problems of CCA, "straightforward image projection" (SIP) has been incorporated into temporal BSS. This novel method, termed low-dimensional canonical correlation analysis (LD-CCA), relies on the spatial and temporal autocorrelations of all genuine signals of interest. Incorporating both spatial and temporal information, here we introduce a "generalized timecourse" technique in which data are artificially reorganized prior to separation. The quantity of spatial plus temporal autocorrelations can then be defined. By maximizing temporal and spatial autocorrelations in combination, LD-CCA is able to obtain expected "real" signal sources. Generalized timecourses are low-dimensional, eliminating the need for dimension reduction. This removes the risk of discarding useful information. The new method is compared with temporal CCA and temporal independent component analysis (tICA). Comparison of simulated data showed that LD-CCA was more effective for recovering signal sources. Comparisons using real intrinsic OI and fMRI data also supported the validity of LD-CCA.