# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 4 Results and Discussion  
Table 2 lists the compression rates for all four functions: linear, slope, adaptive trending and the proposed fuzzy and dynamic voting predictors operating with Huffman and Golomb-Rice encoders. The compression rates of the EEG signals are presented using the average compression rate using all 23 channels of the EEG signals. It is apparent that the voting prediction with entropy coding design proposed in this study has significantly improved the compression rates. Moreover, it drastically decreased the entropy coding size. The design of the prediction methodology is crucial since the prediction optimizes the frequency distribution of the values by making the values closer to a certain value that is usually 0. In addition, the prediction minimizes the absolute values which significantly improves the performance of the entropy coding by assigning the length of the codes according to the values of the input data. Table 3 shows the specification of the area, power and the gate-count of the previous lossless compression designs in [6; 17] and this work. The advantage of the proposed design in terms of area is the use of a combinational logic that occupied most of the cell area which resulted to a reduction in the hardware cost to at least 37.3% compared to previous designs. The proposed design of this work has a total power consumption of 2.5867 mW at a global operating voltage of 1.62 V and a clock frequency of 100 MHz. Even though the combinational logic occupied most of the cell area, it consumed only one-tenth of the power with the registers consuming 89.26%. Based on the results listed in Table 4, a limitation of the proposed design is its power consumption. Thus, minimizing the power consumed by the registers is a major concern in future improvement. Looking into the cell report of this design, the gate count resulted to 8405 NAND-equivalent gates for the entire design with the voting prediction component contributing 68.48% of the total gate count. This is mainly due to the fact that the voting prediction component is the most complex step in the compression algorithm. Previous studies in [6; 17] showed a gate count of 13,400 and 53,900, respectively. Compared with the previous studies, this work showed a 37.3% reduction in terms of gate count. Moreover, this work showed an 84.4% reduction in hardware cost. The proposed lossless EEG encoding algorithm was tested using the CHB-MIT Scalp EEG Database and conceded an average of 2.35 compression rate. Previous studies in [6; 17] showed an average compression rate of 2.05 and 1.37, respectively. This work showed a 14.6% and 58.30% increase in the compression rate compared to [6; 17], respectively. The performance of the proposed VLSI architecture was also verified. Synthesis results recorded that the submitted encoder design contains 8405 NAND-equivalent gate counts, which is 37.3% and 84.4% less than the previous studies in [6; 12], respectively. The core area in this design is 578,000 \(\upmu\)m\({}^{2}\) while its power consumption is 2.58 mW operating at 100 MHz. The power saving of this study reaches 54.77% which is better than 51.21% and 27% in previous studies in [6; 17], respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Channels** & **Linear [6]** & **Slope [18]** & **Adaptive Trending [20]** & **This Study** \\ \hline
1 & 1.72 & 1.69 & 1.73 & 1.98 \\
2 & 2.00 & 1.88 & 1.96 & 2.27 \\
3 & 2.01 & 1.89 & 1.97 & 2.31 \\
4 & 2.32 & 2.13 & 2.27 & 2.65 \\
5 & 1.64 &  
Table 4 lists the energy savings of the original data and the compressed data using both the Bluetooth and Zigbee wireless transmitters. The _Ecomp_ is derived using the power consumption with a clock frequency of 100 MHz for the input data rate used in circuit simulation per bit divided. In addition, the power has a high-positive correlation to the amount of data. Consequently, the power consumption is reduced relative to the compression rate.