The Unlock Project: A Python-based framework for practical brain-computer interface communication “app” development
Identifieur interne : 002130 ( Main/Exploration ); précédent : 002129; suivant : 002131The Unlock Project: A Python-based framework for practical brain-computer interface communication “app” development
Auteurs : Jonathan S. Brumberg ; Sean D. Lorenz ; Byron V. Galbraith ; Frank H. GuentherSource :
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference [ 1557-170X ] ; 2012.
Abstract
In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software “app” development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50–60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis.
Url:
DOI: 10.1109/EMBC.2012.6346473
PubMed: 23366434
PubMed Central: 3694612
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p id="P1">In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software “app” development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50–60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis.</p>
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