Literature DB >> 21436536

Current trends in hardware and software for brain-computer interfaces (BCIs).

P Brunner1, L Bianchi, C Guger, F Cincotti, G Schalk.   

Abstract

A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

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Year:  2011        PMID: 21436536     DOI: 10.1088/1741-2560/8/2/025001

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  17 in total

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2.  Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.

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Journal:  J Neural Eng       Date:  2015-01-14       Impact factor: 5.379

3.  Command detection and classification in tongue drive assistive technology.

Authors:  Elnaz Banan Sadeghian; Xueliang Huo; Maysam Ghovanloo
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

4.  Low cost electroencephalographic acquisition amplifier to serve as teaching and research tool.

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5.  Offline analysis of context contribution to ERP-based typing BCI performance.

Authors:  Umut Orhan; Deniz Erdogmus; Brian Roark; Barry Oken; Melanie Fried-Oken
Journal:  J Neural Eng       Date:  2013-10-08       Impact factor: 5.379

6.  Simultaneous real-time monitoring of multiple cortical systems.

Authors:  Disha Gupta; N Jeremy Hill; Peter Brunner; Aysegul Gunduz; Anthony L Ritaccio; Gerwin Schalk
Journal:  J Neural Eng       Date:  2014-07-31       Impact factor: 5.379

7.  A training platform for many-dimensional prosthetic devices using a virtual reality environment.

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8.  Evaluation of artifact-corrected electroencephalographic (EEG) training: a pilot study.

Authors:  Jeffry P La Marca; Daniel Cruz; Jennifer Fandino; Fabiana R Cacciaguerra; Joseph J Fresco; Austin T Guerra
Journal:  J Neural Transm (Vienna)       Date:  2018-03-26       Impact factor: 3.575

9.  Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI.

Authors:  Gernot R Müller-Putz; Christian Breitwieser; Febo Cincotti; Robert Leeb; Martijn Schreuder; Francesco Leotta; Michele Tavella; Luigi Bianchi; Alex Kreilinger; Andrew Ramsay; Martin Rohm; Max Sagebaum; Luca Tonin; Christa Neuper; José Del R Millán
Journal:  Front Neuroinform       Date:  2011-11-24       Impact factor: 4.081

10.  An open-source and cross-platform framework for Brain Computer Interface-guided robotic arm control.

Authors:  Pieter L Kubben; Nader Pouratian
Journal:  Surg Neurol Int       Date:  2012-12-14
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