Designing an Architecture for a Brain-Computer Interface Operating System: Addressing Neural Input Complexity

The unique complexities of neural inputs, such as signal variability, noise sensitivity, and multimodal integration, demand a specialized architecture for Brain-Computer Interface (BCI) Operating Systems. This paper proposes a modular, scalable, and real-time BCI OS designed to address these challen...

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Bibliographic Details
Published in:The ... International Winter Conference on Brain-Computer Interface pp. 1 - 6
Main Author: Choi, Yunseok
Format: Conference Proceeding
Language:English
Published: IEEE 24.02.2025
Subjects:
adaptive functionality
Adaptive systems
BCI OS
Brain-computer interfaces
Complexity theory
Computer architecture
edge computing
lightweight AI
Medical services
modular design
multimodal input integration
Multisensory integration
Operating systems
real-time processing
Real-time systems
Synchronization
Virtual reality
ISSN:2572-7672
Online Access:Get full text
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Summary:The unique complexities of neural inputs, such as signal variability, noise sensitivity, and multimodal integration, demand a specialized architecture for Brain-Computer Interface (BCI) Operating Systems. This paper proposes a modular, scalable, and real-time BCI OS designed to address these challenges. By incorporating advanced signal preprocessing, adaptive AI-based interpretation, and synchronized multimodal input fusion, the system ensures accurate and efficient handling of neural inputs. The architecture supports the separation of processing layers, enabling flexibility for various hardware configurations and application domains. This design bridges the gap between traditional input devices and neural interfaces, paving the way for seamless interaction in healthcare, virtual reality, and smart environments [2].
ISSN:2572-7672
DOI:10.1109/BCI65088.2025.10931583