We introduce new spatial filtering methods in the spherical harmonics domain for constraining magnetoencephalographic (MEG) multichannel measurements to user-specified spherical regions of interests (ROI) inside the head. The main idea of the spatial filtering is to emphasize those signals arising from an ROI, while suppressing the signals coming from outside the ROI. We exploit a well-known method called the signal space separation (SSS), which can decompose MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. The novel methods presented in this work, expanded SSS (exSSS) and generalized expanded SSS (genexSSS) utilize a beamspace optimization criterion in order to linearly transform the inner signal SSS coefficients to represent the sources belonging to the ROI. The filters mainly depend on the radius and the center of the ROI. The simplicity of the derived formulations of our methods stems from the natural appropriateness to spherical domain and orthogonality properties of the SSS basis functions that are intimately related to the vector spherical harmonics. Thus, unlike the traditional MEG spatial filtering techniques, exSSS and genexSSS do not need any numerical computation procedures on discretized headspace. The validation and performance of the algorithms are demonstrated by experiments utilizing both simulated and real MEG data.