A subject-independent pattern-based Brain-Computer Interface
While 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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-10-01
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| Series: | Frontiers in Behavioral Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnbeh.2015.00269/full |
| _version_ | 1818450495865356288 |
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| author | Andreas Markus Ray Ranganatha eSitaram Ranganatha eSitaram Mohit eRana Mohit eRana Emanuele ePasqualotto Korhan eBuyukturkoglu Cuntai eGuan Kai Keng eAng Cristián eTejos Francisco Javier Zamorano Francisco Javier Zamorano Francisco eAboitiz Niels eBirbaumer Niels eBirbaumer Sergio eRuiz Sergio eRuiz |
| author_facet | Andreas Markus Ray Ranganatha eSitaram Ranganatha eSitaram Mohit eRana Mohit eRana Emanuele ePasqualotto Korhan eBuyukturkoglu Cuntai eGuan Kai Keng eAng Cristián eTejos Francisco Javier Zamorano Francisco Javier Zamorano Francisco eAboitiz Niels eBirbaumer Niels eBirbaumer Sergio eRuiz Sergio eRuiz |
| author_sort | Andreas Markus Ray |
| collection | DOAJ |
| description | While 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. |
| first_indexed | 2024-12-14T20:52:13Z |
| format | Article |
| id | doaj.art-8b84f2a5d99d4eecbc0759ce2cf92126 |
| institution | Directory Open Access Journal |
| issn | 1662-5153 |
| language | English |
| last_indexed | 2024-12-14T20:52:13Z |
| publishDate | 2015-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Behavioral Neuroscience |
| spelling | doaj.art-8b84f2a5d99d4eecbc0759ce2cf921262022-12-21T22:47:47ZengFrontiers Media S.A.Frontiers in Behavioral Neuroscience1662-51532015-10-01910.3389/fnbeh.2015.00269125969A subject-independent pattern-based Brain-Computer InterfaceAndreas Markus Ray0Ranganatha eSitaram1Ranganatha eSitaram2Mohit eRana3Mohit eRana4Emanuele ePasqualotto5Korhan eBuyukturkoglu6Cuntai eGuan7Kai Keng eAng8Cristián eTejos9Francisco Javier Zamorano10Francisco Javier Zamorano11Francisco eAboitiz12Niels eBirbaumer13Niels eBirbaumer14Sergio eRuiz15Sergio eRuiz16University of TuebingenUniversity of TuebingenPontificia Universidad Catolica de ChileUniversity of TuebingenGraduate School of Neural & Behavioral SciencesUniversité Catholique de LouvainUniversity of TuebingenInstitute for Infocomm ResearchInstitute for Infocomm ResearchPontificia Universidad Católica de ChileUniversidad del DesarrolloUniversidad del DesarrolloPontificia Universidad Católica de ChileUniversity of TuebingenOspedale San CamilloUniversity of TuebingenPontificia Universidad Católica de ChileWhile 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.http://journal.frontiersin.org/Journal/10.3389/fnbeh.2015.00269/fullNeurofeedbackBCICommon Spatial PatternsSubject-independent classificationEmotion imagery |
| spellingShingle | Andreas Markus Ray Ranganatha eSitaram Ranganatha eSitaram Mohit eRana Mohit eRana Emanuele ePasqualotto Korhan eBuyukturkoglu Cuntai eGuan Kai Keng eAng Cristián eTejos Francisco Javier Zamorano Francisco Javier Zamorano Francisco eAboitiz Niels eBirbaumer Niels eBirbaumer Sergio eRuiz Sergio eRuiz A subject-independent pattern-based Brain-Computer Interface Frontiers in Behavioral Neuroscience Neurofeedback BCI Common Spatial Patterns Subject-independent classification Emotion imagery |
| title | A subject-independent pattern-based Brain-Computer Interface |
| title_full | A subject-independent pattern-based Brain-Computer Interface |
| title_fullStr | A subject-independent pattern-based Brain-Computer Interface |
| title_full_unstemmed | A subject-independent pattern-based Brain-Computer Interface |
| title_short | A subject-independent pattern-based Brain-Computer Interface |
| title_sort | subject independent pattern based brain computer interface |
| topic | Neurofeedback BCI Common Spatial Patterns Subject-independent classification Emotion imagery |
| url | http://journal.frontiersin.org/Journal/10.3389/fnbeh.2015.00269/full |
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