There are potential problems with the input options, and then there is the actual error you are running into.

If the EPI and anatomical datasets roughly overlay, then you may not need the giant_move option. In any case, that option overrides some of the other options you are using like -Allineate_opts and -partial_coronal. Also deoblique is on by default. If you want to incorporate the equivalent of the giant_move options with a rigid body alignment, use these options instead:

-Allineate_opts "-weight_frac 1.0 -maxrot 45 -maxshf 40 -VERB -warp shift_rotate " -big_move -cmass cmass

The partial_coronal option is not really necessary in combination with allowing for the "giant move". Also the epi_base of mean may not be the best choice; often an early individual epi sub-brick works better.

As far as the actual error is concerned, the problem seems to be with the skull-stripping, and this may apply to either the anatomical or epi datasets. You can examine the output to see which it might be. Based on your dataset's name, I am afraid you may be trying to align a scaled dataset rather than the original EPI dataset. Because of the lack of spatial contrast in that kind of dataset, skull-stripping and alignment will fail. If you are working with the original EPI dataset, you can try doing the "skull-stripping" with 3dAutomask instead -by using "-epi_strip 3dAutomask" as an option for align_epi_anat.py.

You can also do the skull-stripping of both the anatomical and EPI datasets separately before alignment with 3dSkullStrip and 3dAutomask for the EPI and then use those datasets as input to the align_epi_anat.py script. Here is an example:

3dSkullStrip -prefix anat_ns -input anat+orig
3dAutomask -apply_prefix epi_am+orig
align_epi_anat.py -anat anat_ns+orig -epi epi_am+orig -epi_base 0 -anat_has_skull no -epi_strip None ...

Note the 3dAutomask command uses a recent addition of the apply_prefix instead of the usual prefix that simply creates the mask. The "apply_prefix" tells the program to apply the automask to the input dataset and save it to the apply_prefix name.