## 5 Discussion  
Considering the results on detection performance and power consumption together, we can see that by performing data reduction on the sensor node prior to the transmission, substantial reductions in both the microcontroller and transmitter power consumption can be achieved. In particular, by moving the feature extraction step from the data server to the sensor, the overall system power consumption is significantly reduced, yet without affecting the seizure detection performance. Our BER analysis further shows that the transmission of EEG features is more robust to imperfect channel conditions compared to transmitting raw EEG or compressed EEG.  
Nonetheless, it should be noted that for some EEG-based applications, the transmission of the entire EEG signal may be required. In such cases, an external data storage unit on the sensor node can be used to store the EEG data. Alternatively, the EEG data may be transmitted to the server in the compressed form. There are two issues with the first approach. First, logging EEG data onto an external data storage is slow and energy-consuming [25]. Second, although the complete signals can be downloaded from the data storage unit periodically (e.g., when the sensor unit is recharged at night), for time-critical biomedical applications, such as seizure detection, it is imperative to have real-time or near real-time data streaming in order for healthcare providers to provide an immediate response. On the other hand, the transmission of compressed EEG allows the real-time streaming of the data, while providing a substantial reduction in power consumption at the sensor node compared to the transmission of the raw EEG. However, when a high quality reconstruction is required for the intended application, a low compression ratio will be used, and hence, the amount of savings in the system power consumption may be greatly reduced. Fortunately,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature Extraction** & **Addition** & **Assignment** & **Comparison** & **Multiplication** \\
**Technique** & **Operations** & **Operations** & **Operations** & **Operations** \\ \hline Energy & \(N\) & \(N+1\) & 0 & \(N+1\) \\ Line Length & \(4N-3\) & \(N\) & \(N-1\) & \(1\) \\ Nonlinear Autocorr & \(2N_{S}\) & \(2N+9 Ns-4\) & \(2N+2N_{S}-4\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of operations performed by each feature extraction technique. Nonlinear Autocorr, nonlinear autocorrelation.  
in many biomedical applications (e.g., seizure detection and brain computer interface), the first step of the data analysis involves feature extraction from the raw data. Generally, the extracted features are much smaller than the raw or compressed data. If this step can be performed (or partially) on the sensor node prior to transmission, a substantial amount of energy can be conserved by transmitting features instead of the raw or compressed data.  
In this study, we only considered time-domain, univariate features, due to their simplicity. These techniques focus on identifying high amplitude spikes or rhythmic patterns in the EEG signals during seizure periods. However, the presence of high amplitude artifacts, such as electrode movement or muscle artifacts, will increase the number of false positives. Conversely, seizures that are focal or low amplitude may not be detected. To address the variability in the morphology of seizure activity, the use of combinations of different feature types has been explored in the literature [26; 27; 28; 29]. By strategically combining features with complementary information, it has been shown that better seizure detection performance can be achieved [26; 27; 28; 29]. However, the computation and transmission of multiple feature types require additional power consumption at the sensor node. Thus, the tradeoffs must be carefully examined and will be left for future research.