A Brain-Computer Interface Data Persistence System for Multi-Scenario and Multi-Modal Data: NeuroStore

2025-03-17

AI Summary

To address the storage heterogeneity and scenario adaptability challenges arising from the rapid proliferation of multimodal brain–computer interface (BCI) data, this paper proposes NeuroStore—a unified, scalable data management system. NeuroStore introduces a generic, extensible data model and a flexible distributed architecture, enabling, for the first time, adaptive multimodal classification storage and scenario-driven dynamic policy optimization. It supports standardized APIs, multimodal data modeling, and context-aware scheduling, ensuring high scalability and deployment flexibility. As the core data infrastructure for the BCI-controlled robot competition at the World Robot Contest, NeuroStore significantly improves data management efficiency and algorithm validation reliability. It establishes a unified, robust, and persistent data infrastructure for BCI research, bridging the gap between heterogeneous data sources and application-specific requirements.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

With the rapid advancement of brain-computer interface (BCI) technology, the volume of physiological data generated in related research and applications has grown significantly. Data is a critical resource in BCI research and a key factor in the development of BCI technology, making efficient storage and management of this data increasingly vital. In the realm of research, ample data can facilitate the development of novel algorithms, which can be more accurately validated. In terms of applications, well-organized data can foster the emergence of new business opportunities, thereby maximizing the commercial value of the data. Currently, there are two major challenges in the storage and management of BCI data: providing different classification storage modes for multi-modal data, and adapting to varying application scenarios while improving storage strategies. To address these challenges, this study has developed the NeuroStore BCI data persistence system, which provides a general and easily scalable data model and can effectively handle multiple types of data storage. The system has a flexible distributed framework and can be widely applied to various scenarios. It has been utilized as the core support platform for efficient data storage and management services in the"BCI Controlled Robot Contest in World Robot Contest."

Problem

• Efficient storage and management of BCI data
• Handling multi-modal data classification storage
• Adapting storage strategies to various scenarios

Innovation

• NeuroStore system for BCI data storage
• Flexible distributed framework for scalability
• Supports multi-modal and multi-scenario data