A fast-growing technique in human neuroscience is electrocorticography (ECOG), the only technique that allows the activity of small population of neurons in the human brain to be directly recorded. We use the term ECOG to refer to the entire range of invasive recording techniques (from subdural strips and grids to penetrating electrodes) that share the common attribute of recording neural activity from the human brain with high spatial and temporal resolution. While this ability has resulted in many high-impact advances in understanding fundamental mechanisms of brain function in health and disease, it generates staggering amounts of data as a single patient can be implanted with hundreds of electrodes, each sampled thousands of times a second for hours or even days. The difficulty of exploring these vast datasets is the rate-limiting step in using them to improve human health. We propose to overcome this obstacle by creating an easy-to-use, powerful platform designed from the ground up for the unique properties of ECOG. We dub this software tool RAVE (?R Analysis and Visualization of Electrocorticography data?). The first goal of Aim 1 is to release RAVE 1.0 to the entire ECOG community by month 6 of the first funding period. This will maximize transformative impact by putting the new tools in the hands of users as quickly as possible, facilitating rapid adoption. The design philosophy of RAVE is driven by three imperatives. The first is to keep users close to the data so that users may make discoveries about the brain without being misled by artifacts. The second imperative is rigorous statistical methodology. The final imperative is play well with others. As described in Aim 2, our approach will make it easy to seamlessly incorporate new and existing analysis tools written in Matlab, C++, Python or R into RAVE, giving users the best of both worlds: advanced but easy-to-use visualization of results from ECOG experiments, whether they are analyzed with the off-the- shelf tools routines provided with RAVE or novel tools developed by others.
A fast-growing technique in human neuroscience is electrocorticography (ECOG), the only technique that allow the activity of small population of neurons in the human brain to be directly recorded with high spatial and temporal resolution. ECOG generates staggering amounts of data, and the rate-limiting step in generating new insights about the human brain is the difficulty in exploring this vast quantity of data. We propose to remove this obstacle by creating an easy-to-use, powerful platform designed from the ground up for the analysis and visualization of ECOG data, known as RAVE (?R Analysis and Visualization of Electrocorticography data?).