Literature DB >> 21096893

Toward BCI Wizard - best BCI approach for each user.

Ivan Volosyak1, Christoph Guger, Axel Graser.   

Abstract

Modern brain-computer interface (BCI) systems use different types of neural activity for control. Most BCI systems only allow the customization of very few parameters and focus only on one type of BCI approach. Many articles reported that a certain BCI did not work for some users (so called BCI illiteracy). We are introducing the BCI wizard as a system that automatically identifies key parameters to customize the best BCI paradigm for each user. With a BCI wizard it is possible to develop an interface that relies on the best mental strategy for each user and therefore makes the difference between an ineffective system and a working BCI. This work presents a preliminary study that aims to develop a BCI wizard exploring the two most effective BCI approaches (SSVEP and P300). These types of non-invasive BCIs were tested and evaluated in a group of 14 healthy subjects. During online tests all subjects were asked to spell three words with two spelling applications and at the end of the experiment they chose their preferred approach. Results showed that all subjects could communicate with the P300-based BCI with an accuracy above 69% (5 reached 100% accuracy), 10 out of 14 subjects could effectively use the SSVEP-based BCI (2 reached 100% accuracy). These promising results confirm that BCI wizard will enable BCIs customized to each user with considerably greater flexibility and independence than present systems allow.

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Mesh:

Year:  2010        PMID: 21096893     DOI: 10.1109/IEMBS.2010.5627390

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  5 in total

1.  A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.

Authors:  Álvaro Costa; Enrique Hortal; Eduardo Iáñez; José M Azorín
Journal:  PLoS One       Date:  2014-11-12       Impact factor: 3.240

2.  A comparison of two spelling Brain-Computer Interfaces based on visual P3 and SSVEP in Locked-In Syndrome.

Authors:  Adrien Combaz; Camille Chatelle; Arne Robben; Gertie Vanhoof; Ann Goeleven; Vincent Thijs; Marc M Van Hulle; Steven Laureys
Journal:  PLoS One       Date:  2013-09-25       Impact factor: 3.240

3.  Gamma band activity associated with BCI performance: simultaneous MEG/EEG study.

Authors:  Minkyu Ahn; Sangtae Ahn; Jun H Hong; Hohyun Cho; Kiwoong Kim; Bong S Kim; Jin W Chang; Sung C Jun
Journal:  Front Hum Neurosci       Date:  2013-12-06       Impact factor: 3.169

4.  High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery.

Authors:  Minkyu Ahn; Hohyun Cho; Sangtae Ahn; Sung Chan Jun
Journal:  PLoS One       Date:  2013-11-22       Impact factor: 3.240

5.  Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard.

Authors:  Felix Gembler; Piotr Stawicki; Ivan Volosyak
Journal:  Front Neurosci       Date:  2015-12-22       Impact factor: 4.677

  5 in total

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