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The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability. For ten healthy subjects, our …