Independent component analysis (ICA) of electroencephalographic (EEG) and magnetoencephalographic (MEG) data is usually performed over the temporal dimension: each channel is one row of the data matrix, and a linear transformation maximizing the independence of component time courses is sought. In functional magnetic resonance imaging (fMRI), by contrast, most studies use spatial ICA: each time point constitutes a row of the data matrix, and independence of the spatial patterns is maximized. Here, we show the utility of spatial ICA in characterizing oscillatory neuromagnetic signals. We project the sensor data into cortical space using a standard minimum-norm estimate and apply a sparsifying transform to focus on oscillatory signals. The resulting method, spatial Fourier-ICA, provides a concise summary of the spatiotemporal and spectral content of spontaneous neuromagnetic oscillations in cortical source space over time scales of minutes. Spatial Fourier-ICA applied to resting-state and naturalistic stimulation MEG data from nine healthy subjects revealed consistent components covering the early visual, somatosensory and motor cortices with spectral peaks at ∼10 and ∼20 Hz. The proposed method seems valuable for inferring functional connectivity, stimulus-related modulation of rhythmic activity, and their commonalities across subjects from nonaveraged MEG data. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.