Literature DB >> 23366434

The Unlock Project: a Python-based framework for practical brain-computer interface communication "app" development.

Jonathan S Brumberg1, Sean D Lorenz, Byron V Galbraith, Frank H Guenther.   

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.

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Mesh:

Year:  2012        PMID: 23366434      PMCID: PMC3694612          DOI: 10.1109/EMBC.2012.6346473

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  7 in total

1.  Design and implementation of a brain-computer interface with high transfer rates.

Authors:  Ming Cheng; Xiaorong Gao; Shangkai Gao; Dingfeng Xu
Journal:  IEEE Trans Biomed Eng       Date:  2002-10       Impact factor: 4.538

2.  BCI2000: a general-purpose brain-computer interface (BCI) system.

Authors:  Gerwin Schalk; Dennis J McFarland; Thilo Hinterberger; Niels Birbaumer; Jonathan R Wolpaw
Journal:  IEEE Trans Biomed Eng       Date:  2004-06       Impact factor: 4.538

3.  Phase synchronization for the recognition of mental tasks in a brain-computer interface.

Authors:  Elly Gysels; Patrick Celka
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2004-12       Impact factor: 3.802

4.  Selective attention to stimulus location modulates the steady-state visual evoked potential.

Authors:  S T Morgan; J C Hansen; S A Hillyard
Journal:  Proc Natl Acad Sci U S A       Date:  1996-05-14       Impact factor: 11.205

5.  A high-speed BCI based on code modulation VEP.

Authors:  Guangyu Bin; Xiaorong Gao; Yijun Wang; Yun Li; Bo Hong; Shangkai Gao
Journal:  J Neural Eng       Date:  2011-03-24       Impact factor: 5.379

6.  A robust and self-paced BCI system based on a four class SSVEP paradigm: algorithms and protocols for a high-transfer-rate direct brain communication.

Authors:  Sergio Parini; Luca Maggi; Anna C Turconi; Giuseppe Andreoni
Journal:  Comput Intell Neurosci       Date:  2009-04-28

7.  Context-based filtering for assisted brain-actuated wheelchair driving.

Authors:  Gerolf Vanacker; José del R Millán; Eileen Lew; Pierre W Ferrez; Ferran Galán Moles; Johan Philips; Hendrik Van Brussel; Marnix Nuttin
Journal:  Comput Intell Neurosci       Date:  2007
  7 in total
  1 in total

Review 1.  Past, Present, and Future of EEG-Based BCI Applications.

Authors:  Kaido Värbu; Naveed Muhammad; Yar Muhammad
Journal:  Sensors (Basel)       Date:  2022-04-26       Impact factor: 3.847

  1 in total

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