Movement direction for Brain Machine Interface (BMI) can be decoded successfully using Local Field Potentials (LFP) and Single Unit Activity (SUA). A major challenge when dealing with the intra-cortical recordings is to develop decoders that are robust in time. In this paper we present for the first time a technique that uses the qualitative information derived from multiple LFP channels rather than the absolute power of the recorded signals. In this novel method, we use a power based inter-channel ranking system to define the quality of a channel in multi-channel LFP. This representation enables us to bypass the problems associated with the dynamic ranges of absolute power. We also introduce a parameter based ranking system that provides the same rank to channels that have comparable powers. We show that using our algorithms, we can develop models that provide stable decoding of eight movement directions with an average efficiency of above 56% over a period of two weeks. Moreover, the decoding power using this method is 46% at the end of two weeks versus the 13% using the traditional approaches. We also applied these models to decoding movements performed in a force field and again achieved significantly higher decoding power than the existing methods.