Classification based on dynamic mode decomposition applied to brain recognition of context

Local Field Potentials (LFPs) are easy to access electrical signals of the brain that represent the summation in the extracellular space, of currents originated within the neurons. As such, LFPs could contain information about ongoing computations in neuronal circuits and could potentially be used to design brain machine interface algorithms. However how brain computations could be decoded from LFPs is not clear. Within this context, a methodology for signal classification is proposed in this study, particularly based on the Dynamic Mode Decomposition method, in conjunction with binary clustering routines based on supervised learning. Note that, although the classification methodology is presented here in the context of a biological problem, it can be applied to a broad range of applications. Then, as a case-study, the proposed method is validated with the classification of LFP-based brain cognitive states. All the analysis, signals, and results shown in this study consider real data measured in the hippocampus, in rats performing exploration tasks. Consequently, it is shown that, using the measured LFP, the method infers which context was the animal exploring. Thus, evidence on the spatial codification in LFP signals is consequently provided, which still is an open question in neuroscience.