Brain computer interfacing (BCI) is the practice of establishing a direct communication between a human and a computer by self-regulation of some electrical activity in the brain. Electroencephalography (EEG) based BCI is one of the most promising form of this application where a subject learns to control brain waves of type μ-rhythm (8–14 Hz) or β-rhythm (14–30 Hz) or P300 event related potential or slow cortical potentials (SCPs). Intents of the person is extracted using signal processing techniques. In this study self-regulation of SCPs is studied using wavelet packet analysis (WPA), rather than traditional time or frequency domain methods. WPA, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high frequency resolution than Wavelet analysis. WPA subimages are analyzed further by log energy entropy. These feature vectors are fed into a multilayer perceptron (MLP) for classification. We use the BCI Competition 2003 datasets Ia and Ib to test our approach. Performance of the MLP is compared with that of k-NN and SVM. Our method achieves accuracies of 92.8% and 63.3% on datasets Ia and Ib respectively. This outperforms the other reported results on the same datasets including the winners of the competition. Overall, WPA followed by Log Energy Entropy and MLP provides an efficient tool for the aforementioned kind of BCI oriented EEG analysis.