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Using mental tasks transitions detection to improve spontaneous mental activity classification.

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Title: Using mental tasks transitions detection to improve spontaneous mental activity classification.
Authors: Galán, Ferran1 Ferran.Galan@idiap.ch, Oliva, Francesc2, Guàrdia, Joan3, Galán, Ferran4 (AUTHOR), Guàrdia, Joan (AUTHOR)
Source: Medical & Biological Engineering & Computing. Jun2007, Vol. 45 Issue 6, p603-609. 7p. 1 Chart, 3 Graphs.
Subjects: Algorithms, Contact transformations, Discriminant analysis, Multivariate analysis, Computer interfaces
Abstract: This paper presents an algorithm based on canonical variates transformation (CVT) and distance based discriminant analysis (DBDA) combined with a mental tasks transitions detector (MTTD) to classify spontaneous mental activities in order to operate a brain-computer interface working under an asynchronous protocol. The algorithm won the BCI Competition III--Data Set V: Multiclass Problem, Continuous EEG--achieving an averaged classification accuracy over three subjects of 68.65% (79.60, 70.31 and 56.02%, respectively) in a three-class problem. [ABSTRACT FROM AUTHOR]
Copyright of Medical & Biological Engineering & Computing is the property of Springer Nature 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.)
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  Data: This paper presents an algorithm based on canonical variates transformation (CVT) and distance based discriminant analysis (DBDA) combined with a mental tasks transitions detector (MTTD) to classify spontaneous mental activities in order to operate a brain-computer interface working under an asynchronous protocol. The algorithm won the BCI Competition III--Data Set V: Multiclass Problem, Continuous EEG--achieving an averaged classification accuracy over three subjects of 68.65% (79.60, 70.31 and 56.02%, respectively) in a three-class problem. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical & Biological Engineering & Computing is the property of Springer Nature 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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              Text: Jun2007
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