EEG source localization for brain-computer-interfaces

Moritz Grosse Wentrup; Klaus Gramann; Edmund Wascher; Martin Buss

doi:10.1109/CNE.2005.1419570

EEG source localization for brain-computer-interfaces

Moritz Grosse Wentrup, Klaus Gramann, Edmund Wascher, Martin Buss

Chair of Automatic Control Engineering

Research output: Contribution to conference › Paper › peer-review

19 Scopus citations

Abstract

While most EEG based Brain-Computer-Interfaces (BCIs) employ machine learning algorithms for classification, we propose to utilize source localization procedures for this purpose. Although the computational demand is considerably higher, this approach could allow the simultaneous classification of a multitude of conditions. We present an extension of Independent Component Analysis (ICA) - based source localization that is fully automatic, and apply this method to the classification of EEG data generated by imaginary movements of the right and left index finger. The results demonstrate that source localization provides a viable alternative to machine learning algorithms for BCIs.

Original language	English
Pages	128-131
Number of pages	4
DOIs	https://doi.org/10.1109/CNE.2005.1419570
State	Published - 2005
Event	2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States Duration: 16 Mar 2005 → 19 Mar 2005

Conference

Conference	2nd International IEEE EMBS Conference on Neural Engineering, 2005
Country/Territory	United States
City	Arlington, VA
Period	16/03/05 → 19/03/05

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10.1109/CNE.2005.1419570

Cite this

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title = "EEG source localization for brain-computer-interfaces",

abstract = "While most EEG based Brain-Computer-Interfaces (BCIs) employ machine learning algorithms for classification, we propose to utilize source localization procedures for this purpose. Although the computational demand is considerably higher, this approach could allow the simultaneous classification of a multitude of conditions. We present an extension of Independent Component Analysis (ICA) - based source localization that is fully automatic, and apply this method to the classification of EEG data generated by imaginary movements of the right and left index finger. The results demonstrate that source localization provides a viable alternative to machine learning algorithms for BCIs.",

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AU - Wentrup, Moritz Grosse

AU - Gramann, Klaus

AU - Wascher, Edmund

AU - Buss, Martin

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N2 - While most EEG based Brain-Computer-Interfaces (BCIs) employ machine learning algorithms for classification, we propose to utilize source localization procedures for this purpose. Although the computational demand is considerably higher, this approach could allow the simultaneous classification of a multitude of conditions. We present an extension of Independent Component Analysis (ICA) - based source localization that is fully automatic, and apply this method to the classification of EEG data generated by imaginary movements of the right and left index finger. The results demonstrate that source localization provides a viable alternative to machine learning algorithms for BCIs.

AB - While most EEG based Brain-Computer-Interfaces (BCIs) employ machine learning algorithms for classification, we propose to utilize source localization procedures for this purpose. Although the computational demand is considerably higher, this approach could allow the simultaneous classification of a multitude of conditions. We present an extension of Independent Component Analysis (ICA) - based source localization that is fully automatic, and apply this method to the classification of EEG data generated by imaginary movements of the right and left index finger. The results demonstrate that source localization provides a viable alternative to machine learning algorithms for BCIs.

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T2 - 2nd International IEEE EMBS Conference on Neural Engineering, 2005

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ER -

EEG source localization for brain-computer-interfaces

Abstract

Conference

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