Optimizing spatial filters for the extraction of envelope-coupled neural oscillations

Sven Dahne; Vadim Nikulin; David Ramirez; Peter J. Schreier; Klaus Muller; Stefan Haufer

doi:10.1109/PRNI.2014.6858514

Optimizing spatial filters for the extraction of envelope-coupled neural oscillations

Sven Dahne, Vadim Nikulin, David Ramirez, Peter J. Schreier, Klaus Muller, Stefan Haufer

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Amplitude-to-Amplitude interactions between neural oscillations are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-Amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low.

Original language	English
Title of host publication	Proceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
Publisher	IEEE Computer Society
ISBN (Print)	9781479941506
DOIs	https://doi.org/10.1109/PRNI.2014.6858514
Publication status	Published - 2014 Jan 1
Event	4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 - Tubingen, Germany Duration: 2014 Jun 4 → 2014 Jun 6

Other

Other	4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
Country/Territory	Germany
City	Tubingen
Period	14/6/4 → 14/6/6

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Biomedical Engineering

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10.1109/PRNI.2014.6858514

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Dahne, S, Nikulin, V, Ramirez, D, Schreier, PJ, Muller, K & Haufer, S 2014, Optimizing spatial filters for the extraction of envelope-coupled neural oscillations. in Proceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014., 6858514, IEEE Computer Society, 4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014, Tubingen, Germany, 14/6/4. https://doi.org/10.1109/PRNI.2014.6858514

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abstract = "Amplitude-to-Amplitude interactions between neural oscillations are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-Amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low.",

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AU - Nikulin, Vadim

AU - Ramirez, David

AU - Schreier, Peter J.

AU - Muller, Klaus

AU - Haufer, Stefan

PY - 2014/1/1

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N2 - Amplitude-to-Amplitude interactions between neural oscillations are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-Amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low.

AB - Amplitude-to-Amplitude interactions between neural oscillations are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-Amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low.

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Optimizing spatial filters for the extraction of envelope-coupled neural oscillations

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