The development of parallel imaging technology has made possible the acquisition of multiple T<sub>2</sub> <sup>*</sup>-weighted MRI images after a single excitation. This has opened new possibilities for functional MRI using the blood oxygenation level dependent (BOLD) contrast mechanism, which has conventionally acquired a single image at a fixed echo time TE. Regarding the multi-echo functional magnetic resonance imaging (fMRI) time-series at each voxel as a simultaneously sampled multichannel signal facilitates the application of established multichannel source extraction methods, which could provide improved estimates of the underlying signal component reflecting task-related BOLD. This work considers ten methods reflecting three different source extraction approaches in which either the TE dependence of the BOLD contrast is exploited, the correlation with an expected response (or design matrix) is maximized, or a maximally task-related component is selected from a statistical signal decomposition. The performance of these methods in extracting task-related BOLD activation minimally contaminated by head motion artifacts is examined in the context of an fMRI experiment in which the multi-echo data are systematically corrupted with varying degrees of artificially induced head motion. The best results were obtained with least-squares methods applied to log-transformed data, namely, adaptive beamforming using only the echo-times, and Wiener filtering using the design matrix.