A subject-independent pattern-based Brain-Computer Interface

Simple item page
dc.contributor.authorRay, Andreas M.
dc.contributor.authorSitaram, Ranganatha
dc.contributor.authorRana, Mohit
dc.contributor.authorPasqualotto, Emanuele
dc.contributor.authorBuyukturkoglu, Korhan
dc.contributor.authorGuan, Cuntai
dc.contributor.authorAng, Kai-Keng
dc.contributor.authorTejos, Cristián
dc.contributor.authorZamorano, Francisco
dc.contributor.authorAboitiz, Francisco
dc.contributor.authorBirbaumer, Niels
dc.contributor.authorRuiz, Sergio
dc.date.accessioned2016-01-28T13:08:58Z
dc.date.available2016-01-28T13:08:58Z
dc.date.issued2015
dc.description.abstractWhile earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to "match" their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders.
dc.identifier.citationRay AM, Sitaram R, Rana M, Pasqualotto E, Buyukturkoglu K, Guan C, Ang KK, Tejos C, Zamorano F, Aboitiz F, Birbaumer N, Ruiz S. A subject-independent pattern-based Brain-Computer Interface. Front Behav Neurosci. 2015 Oct 20;9:269
dc.identifier.urihttp://hdl.handle.net/11447/215
dc.identifier.urihttp://dx.doi.org/10.3389/fnbeh.2015.00269
dc.language.isoen_US
dc.subjectneurofeedback
dc.subjectBCI
dc.subjectsubject-independent classification
dc.subjectemotion imagery
dc.subjectcommon spatial patterns
dc.titleA subject-independent pattern-based Brain-Computer Interface
dc.typeArtículo

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A subject-independent pattern-based brain-computer interface.pdf
Size:
3.83 MB
Format:
Adobe Portable Document Format
Description:
Texto completo
Download

Collections

Artículos Gobierno

Implementado por OpenGeek Services