# MBPPE: A Modular Batch Processing Platform for Electroencephalography
###### Abstract  
To ensure the accuracy and reliability of subsequent analysis, research on electroencephalogram (EEG) signals typically requires preliminary processing of large datasets to eliminate noise and artifacts. Traditional batch processing methods require substantial hardware resources while lacking flexible automated workflows and user-friendly interactions. To address these challenges, we have implemented a modular batch processing platform for EEG (MBPPE) that offers both local execution and private deployment options to meet the demands of efficient signal processing from individuals to laboratories. We modularize the processing methods and organize them into pluggable multi-task batch processes, providing asynchronous processing solutions. In addition, we extend user functions by introducing plugins and promoting collaborative interaction through data sharing, access control, and comment communication. Simultaneously, interactive features are integrated into the visualization design, enabling users to process and analyze data more intuitively and naturally. Currently, the platform integrates several commonly used data preprocessing and analysis techniques, providing a novel solution for batch processing of EEG signals. EEG; signal processing; modularization; batch processing; collaboration +
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processing and analysis functions, encapsulating complex processing steps within interfaces. Although these interfaces aid developers in simplifying the programming process, crafting processing workflows for diverse scenarios continues to be a challenging task. Currently, to simplify the process of EEG signal processing and analysis, some standardized processing pipelines, such as the Harvard automated processing pipeline for electroencephalography (HAPPE) [7] and a dual EEG pipeline for developmental hyper-scanning studies (DEEP) [8], are being explored and implemented. These pipelines provide efficient artifact removal methods for EEG data in specific scenarios to meet subsequent research needs. However, due to the variability of EEG signals, standardized processes cannot feasibly solve all scenarios permanently. Although EEGLAB [9] can create custom pipelines using plugins, this essentially involves executing multiple processing tasks in the script. This approach has limitations in task monitoring and management, particularly when dealing with complex multi-task scenarios. Moreover, the efficiency of these offline processing schemes is constrained by the performance of the user's computer. Consequently, hardware performance may become a constraint in efficiency when numerous EEG signal processing tasks are being dealt with. On the other hand, a web-based brainformatics platform of the computational ecosystem for EEG big data analysis (WeBrain) [10] is a high-performance computing platform inspired by big data and cloud computing technologies. It is designed for large-scale EEG data cloud storage, processing, and analysis, offering an online resolution to computational performance issues. The platform assigns user roles into three categories: ordinary users, administrators, and developers. In this system, administrators handle the allocation of computing and storage resources, while developers offer a variety of signal processing solutions. This cloud computing-dependent strategy effectively reduces the hardware performance pressure resulting from large-scale data processing. However, the complexity of the platform's architecture and personnel roles makes privatized deployment challenging. As a result, users are required to upload their data to public servers, posing a risk of data privacy infringement. Moreover, the platform can only execute processing methods sequentially, not capable of establishing a continuous pipeline task. Despite being provided by developers, the scalability of these processing methods remains restricted for individual users. To address the limitations identified in existing EEG processing platforms, we have restructured the system based on a modular design principle.