MBPPE: A Modular Batch Processing Platform for Electroencephalography
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies · Neural dynamics and brain function
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information, Shanghai Ocean University, No. 999 Huchenghuan Road, Shanghai 201306, China
Funding
Guangdong Province Key Field R&D Plan Project
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