# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 3 Results
### Validation of Pipeline  
To validate the scenario of parallel execution for multiple tasks within the pipeline, we constructed multiple distinct task sequences in offline mode, as depicted in Figure 5. Figure 5 provides an overview of the pipeline, enabling users to create new task sequences or monitor tasks' real-time execution status. Each task, initially identified by an automatically generated universally unique identifier (UUID), is subsequently associated with the currently processed file name and the method involved. To visually convey task status, we use green for a successfully executed task, black for an ongoing task, and gray for a \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Methods** & **Time(s)** & **Details** \\ \hline \multirow{4}{*}{Preprocessing} & Filter & \(<\)1 & Band-pass (1–40 Hz) \\  & Independent Component Analysis & \(\approx\)90 & FastICA \\  & Resampling & \(<\)1 & Downsampling (100 Hz) \\  & Rereferencing & \(<\)1 & Average Reference \\ \hline \multirow{4}{*}{Feature Extraction} & Power Spectral Density & \(<\) 3 & \\  & Differential Entropy & \(<\)1 & \\ \cline{1-1}  & Frequency & \(<\)5 & \\ \cline{1-1}  & Time–Frequency & \(\approx\)100 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Execution time of methods. pending task. Failed tasks are highlighted in red. Figure 5b provides a detailed view of the selected task, including the file list, current execution progress, user-selected channels, and method-specific parameter configurations. Additionally, This interface permits users to manage files and tasks, as well as annotate and communicate about experiments. Each sequence processes multiple EEG data files. Specifically, we constructed a practical application case for emotion classification to demonstrate the adaptability of MBPPE in real EEG processing scenarios. Initially, we employed resampling to decrease the sampling rate to 100 Hz, aiming to minimize data volume in the succeeding processing. Subsequently, we preprocessed the data using a band-pass filter (I-40 Hz) to discard potential power line interference and cardiac artifacts [31]. Ultimately, we derived differential entropy features from the preprocessed data as the foundation for emotion classification. The flexibility of MBPPE is embodied in the task sequence creation process, where processing methods can be freely combined. For instance, when differential entropy features prove inapplicable in certain instances, frequency-domain analysis may be selected. Simultaneously, we crafted a task sequence incorporating plugins to assess the pipeline's compatibility. Furthermore, we constructed two dedicated sequences for re-referencing and time-frequency analysis, respectively, to determine the pipeline's operational capability and whether it could bypass stages when specific task units are absent during the preprocessing or analysis phase. To attest to the reliability of the pipeline, we applied the wavelet packet transform to the EEG data, transitioning from the time domain to the frequency domain. Figure 6a displays the frequency response chart for a specific channel after this transformation. In another pipeline, the EEG data first undergoes a low-pass filter with a cutoff frequency of 50 Hz, followed by a continuous wavelet transform for time-frequency analysis. The corresponding results are presented in Figure 6b. Due to the application of a 50 Hz low-pass filter, the truncation of frequency en  
Figure 5: Pipeline overview: (**a**) Comprehensive overview of pipeline tasks; (**b**) Detailed examination of the selected task. Meanwhile, according to the feature extraction method of the SEED dataset, we utilized MBPPE to extract differential entropy from the preprocessed datasets. Subsequently, we used support vector classification as the baseline classification function and performed ten-fold cross-validation on both the SEED feature-extracted data and the MBPPE-extracted data.