# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 3 Results  
The essential criteria for assessing the specific study is the management of the system's functioning in terms of its design parameter, i.e., the low energy consumption. The system takes a proportion of energy to operate, and the primary purpose is to conserve as much as possible so that battery replacement and recharges are taking place significantly less often. The power and how it is consumed in the system are considered for this reason. However, the question of how the evaluation would be conducted emerges. As described in earlier sections, the EEG signal transmitter was implemented in three fundamental ways: modelling, integrated circuit programming, and Arduino programming. Energy consumption may be calculated both at the modelling and data transmission levels using Arduino. In the first one, the energy reduction between encoded and non-coded channels is computed using a virtual technique, while in the other, a reasonably simple method is to connect the Arduino to a power bank and monitor the time it takes to transmit a signal before and after processing. No energy evaluation is undertaken in the second level because the VHDL programming level concerns memory structure. Power consumption, in integrated systems, is calculated as a percentage of energy consumed per unit of time and can take two forms: dynamic consumption and static consumption. The equation for dynamic consumption is:  
\[P_{D}=C_{L}\times V_{DD}^{2}\times F_{clk}\times a \tag{1}\]  
which is related to the proportion of transitions (switching power _a_) from zero to one, the capacitance (\(C\)), the voltage (\(V_{CC}\)), and the frequency (\(F_{clk}\)) of the circuit. Various parameters may be addressed to obtain the energy reductions necessary to power circuits. The transition factor influences the transition frequency. In general, the lower this rate, the fewer transitions required and less energy spent because the switches 0-to-1 are fewer. The second type is static power consumption, which occurs while the system is idle. This type of consumption depends on the leakage currents that occur when the device is not operating. It is determined by the following equation:  
\[P_{S}=V_{CC}^{2}\times V_{CC} \tag{2}\]  
where \(I_{CC}\) and \(V_{CC}\) correspond to the average current circulating at the circuit and the voltage. All transitions affect static consumption almost as much as dynamic consumption. Voltage and capacitance, for example, are circuit properties, so what can be easily monitored since it depends on the signal are the many forms of transitions. The total number of transitions was multiplied by an estimated weight to evaluate their contribution to consumption. The weight for 0-to-1 transitions was calculated by adding the number of different types of transitions (0-to-1, 1-to-1, 0-to-0, 1-to-0) and the contribution of static consumption. As a result, the weight for the 0-to-1 transitions was five times greater than the weight for the other transitions. In Table 1, the columns correspond to the following:  
* A0: The channels being studied. * A1: Transitions from 0 to 1 prior to Delta coding (calculated by multiplying all transitions). * A2: The transitions from 0 to 1 following Delta coding (estimated by multiplying the total transitions by weight). Comparison results are given in Figure 3.
* A3: Total number of transitions prior to coding ((0-0) + (1-1) + (1-0) + 5
* A4: The total number of transitions following coding ((0-0) + (1-1) + (1-0) + 5
* (0-1)). Comparison results are given in Figure 4.
* A5: The percentage of reduction of total transitions. * A6: The percentage of reduction of transitions 0-1. As a result of the implementation, the 23 EEG channel consumption presented with an overall reduction of transitions from 0 to 1, ranging from 5.9114% to almost 23.2% between non-coded and encoded signals (Figure 5). The reduction was not significant in some channels (channels 12 and 16), but this can be explained by the fact that power reduction is also dependent on the signal characteristics from specific leads.