Local field potentials (LFPs) arise from dendritic currents that are summed by the brain tissue's impedance. By assuming that the rhythms existing in the LFPs result from the coordinated neural activity of sparse and transient neural assemblies transformed by the neural tissue, we propose to recover these neural assemblies sources using an independent component analysis on segments of a single LFP channel. The corresponding source signals and the set of temporal filters that operate on them constitute an efficient time-frequency decomposition of the LFP. This decomposition has the potential to identify sources that are more statistically dependent with stimuli or single-cell activity than the raw signal. In this work we show preliminary results on a synthetic dataset and a real dataset recorded from a rats nucleus accumbens during a reward administering experiment. When compared with the standard time-frequency analysis, this computational model for LFP analysis is totally data-driven because the filters, which form the basis for the decomposition, are estimated directly from the data.