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An efficient P300-based brain–computer interface for disabled subjects

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Title: An efficient P300-based brain–computer interface for disabled subjects
Authors: Hoffmann, Ulrich1 ulrich.hoffmann@epfl.ch, Vesin, Jean-Marc1, Ebrahimi, Touradj1, Diserens, Karin2
Source: Journal of Neuroscience Methods. Jan2008, Vol. 167 Issue 1, p115-125. 11p.
Subjects: Computer input-output equipment, Computer interfaces, Brain, Artificial intelligence
Abstract: Abstract: A brain–computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed. [Copyright &y& Elsevier]
Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 27742255
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: An efficient P300-based brain–computer interface for disabled subjects
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  Data: <searchLink fieldCode="AR" term="%22Hoffmann%2C+Ulrich%22">Hoffmann, Ulrich</searchLink><relatesTo>1</relatesTo><i> ulrich.hoffmann@epfl.ch</i><br /><searchLink fieldCode="AR" term="%22Vesin%2C+Jean-Marc%22">Vesin, Jean-Marc</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ebrahimi%2C+Touradj%22">Ebrahimi, Touradj</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Diserens%2C+Karin%22">Diserens, Karin</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Neuroscience+Methods%22">Journal of Neuroscience Methods</searchLink>. Jan2008, Vol. 167 Issue 1, p115-125. 11p.
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  Data: Abstract: A brain–computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.jneumeth.2007.03.005
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        Text: English
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      – SubjectFull: Computer input-output equipment
        Type: general
      – SubjectFull: Computer interfaces
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      – SubjectFull: Brain
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      – TitleFull: An efficient P300-based brain–computer interface for disabled subjects
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              Text: Jan2008
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              Y: 2008
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