# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 4 Discussion  
The use of neurostimulation for the treatment of epilepsy has markedly increased in the past decade, especially in the USA where the closed-loop RNS system and the most recent open-loop DBS Percept Medtronic device have both been approved by the American Food and Drug Administration (FDA) for use in epilepsy patients and have been evaluated to be refractory to both anti-epileptic medication and traditional resective surgery [8; 9; 10; 12; 13]. The fact that the use of these devices in clinical trials has been reliably shown to improve seizure control and the quality of life of epilepsy patients [29; 30; 31; 32], has drawn interest in advancing their technology and extending their potential [33]. Nevertheless, these devices are facing an important issue of short battery life-cycle, assessed to last between 3 and 5 years for DBS [34] and about 3.5 years for the RNS system [32]. As the battery change in all implanted neurostimulation devices requires the patient to undergo a brief but non-trivial surgical procedure under general anesthesia, the power consumption of these devices becomes a key factor that affects both the healthcare costs and the patient's well being. Our study investigated and implemented low power strategies targeted for neurostimulation systems in the field of epilepsy that incorporate EEG recording and transmission in either open (DBS Percept Medtronic) or closed-loop (RNS) schemes. The wireless data encoding and transmission system was implemented by means of MATLAB modelling, VHDL coding for integrated systems, and BLE simulation using an Arduino-based system. Furthermore, three systems configurations were examined in a development board embedding a Xilinx ZYNQ-7000 device to measure the power consumption of the proposed system. We used EEG datasets from epilepsy patients, and fed the data through our system. Our implementation resulted in an overall reduction of 0-to-1 transitions up to 23%, with the system's autonomy increasing as a result of Delta encoding. Our results show that the strategies we employed are directly affecting the energy consumption during BLE transmission; the core concept of this work is focusing on this. However, it should be noted that power dissipation is not reduced exclusively by this process but additionally from the data encoding procedures in the system. The Delta encoding is requiring the least processing \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & **LL** & & & **Encoder** & & & **Memory** & & & & **Complex** & & & **BLE** & **Total** \\ \cline{2-13}  & **FF** & **LUT** & **FF** & **DSP** & **BRAM** & **Power** & **BRAM** & **Power** & **LUT** & **FF** & **DSP** & **BRAM** & **Power** & **Power** & **Power** & **Power** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(W)** & **(\%)** & **(W)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(W)** & **(W)** & **(W)** \\ \hline
**1** & 32 & - & - & - & - & - & - & 40 & 0.072 & - & - & - & - & - & 0.18 & 0.252 \\ \hline
**2** & 32 & - & - & - & - & - & 40 & 0.072 & 42 & 20 & 7 & 30 & 0.221 & 0.14 & 0.433 \\ \hline
**3** & 32 & 16 & 32 & 0 & 0 & 0.004 & 40 & 0.072 & - & - & - & - & - & 0.16 & 0.236 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of three configurations in Xilinx ZYNQ-7000 device. power, as supported by the literature described in the Methods section, and only its effect on data transmission is considered in this work for extending the system's autonomy. The development and implementation of low power techniques to minimise power consumption in neurostimulation systems for the treatment of epilepsy is an important step towards the design of more sustainable neurostimulators. Energy efficiency implanted devices such as these will save the healthcare system from repeated and costly battery replacement procedures, as well as the patients from being subjected to regular invasive operations under general anesthesia.