Modeling and Implementing a Signal Persistence Manager for Shared Biosignal Storage and Processing

Biosignal recording is gaining growing attention in several biomedical application, for example Brain Computer Interface (BCI).

The availability of systems providing shared management and processing of signals is still lacking.

A centralized system for signal persistence management and processing, with the ability to provide predefined analysis algorithms, would hence be an useful asset.

In this work, we designed and implemented a platform to support the management and analysis of signals for different neurophysiological applications, including BCI, that could be also expanded to other applications.

It is composed mainly by a client framework (Matlab compliant), and a web interface to manage the platform itself, privileging a centralized approach.

To validate the platform, and to test it, we performed an experiment during which, in normal subjects, we recorded signals during real movements (right hand, left hand, and feet) and during motor imagery. Following the literature on BCI, we developed specific algorithms for signal analysis and uploaded them on the platform.

Recorded signals were uploaded to the platform and the analysis packages available were used to extract Event related desynchronization (ERD)/Event related synchronization (ERS) on

μ

and ß bands.

The validation showed that our platform can be easily adopted to share signals and analysis chains to design BCI applications.