The comprehension of brain activity presents significant challenges in the field of neuroscience. Contrary to spikes, Local Field Potentials (LFPs) present improved stability acquisition in chronic implant scenarios and potential reductions in sampling and processing rates. While existing electrophysiology acquisition systems focus predominantly on spike detection and sorting, there is a lack of real-time tools for exploiting LFPs. To address this gap, we present a Resistive-RAM (RRAM) based approach to process LFP traces. Our method follows an improved Memristive Integrating Sensor (MIS) protocol to effectively detect LFP events recorded from the deep-brain of an awake rat, while externally stimulated by a tone. Experimental results demonstrate the feasibility of real-time neural activity processing, offering insights into detecting meaningful external stimuli and facilitating efficient neural state estimation.