Markus Ullsperger
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195372731
- eISBN:
- 9780199776283
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195372731.003.0010
- Subject:
- Neuroscience, Techniques
This chapter gives an overview of data integration methods for simultaneous EEG-fMRI, in which EEG features are extracted and used to parametrically model the fMRI data. Up to now, variants of ...
More
This chapter gives an overview of data integration methods for simultaneous EEG-fMRI, in which EEG features are extracted and used to parametrically model the fMRI data. Up to now, variants of EEG-informed fMRI analysis have been most widely and successfully applied. After a brief discussion of the rationale of this approach, its variants for ongoing and event-related EEG phenomena are explained. Studies applying EEG-informed fMRI are reviewed. The advantage of denoising methods such as independent component analysis allowing single-trial quantifications of the EEG phenomena of interest is discussed. To allow clear interpretations of covariations between electrophysiological and hemodynamic measures, further dependent variables such as behavioral data should be taken into account. The chapter closes with an outlook on future questions and ongoing methodological developments.Less
This chapter gives an overview of data integration methods for simultaneous EEG-fMRI, in which EEG features are extracted and used to parametrically model the fMRI data. Up to now, variants of EEG-informed fMRI analysis have been most widely and successfully applied. After a brief discussion of the rationale of this approach, its variants for ongoing and event-related EEG phenomena are explained. Studies applying EEG-informed fMRI are reviewed. The advantage of denoising methods such as independent component analysis allowing single-trial quantifications of the EEG phenomena of interest is discussed. To allow clear interpretations of covariations between electrophysiological and hemodynamic measures, further dependent variables such as behavioral data should be taken into account. The chapter closes with an outlook on future questions and ongoing methodological developments.
Stefan Debener, Jeremy Thorne, Till R. Schneider, and Filipa Campos Viola
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195372731
- eISBN:
- 9780199776283
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195372731.003.0008
- Subject:
- Neuroscience, Techniques
Independent component analysis (ICA) is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals. The EEG signal consists of a mixture of various brain ...
More
Independent component analysis (ICA) is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals. The EEG signal consists of a mixture of various brain and non-brain contributions. Accordingly, a valid and powerful unmixing tool promises a better, more accessible representation of the statistical sources contributing to the mixed recorded signal. ICA, being potentially such a tool, may help in the detection of signal sources that cannot be identified on the raw data level alone using other, more conventional techniques. The application of ICA to EEG signals has become popular, as it provides two key features: it is a powerful way to remove artifacts from EEG data, and it helps to disentangle otherwise mixed brain signals. This chapter is concerned with evaluating and optimizing EEG decompositions by means of ICA. First, it discusses typical ICA results with reference to artifact- and brain-related components. Then, it elaborates on different EEG pre-processing steps, considered in light of the statistical assumptions underlying ICA. As such, the motivation for the chapter is to provide some practical guidelines for those researchers who wish to successfully decompose multi-channel EEG recordings.Less
Independent component analysis (ICA) is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals. The EEG signal consists of a mixture of various brain and non-brain contributions. Accordingly, a valid and powerful unmixing tool promises a better, more accessible representation of the statistical sources contributing to the mixed recorded signal. ICA, being potentially such a tool, may help in the detection of signal sources that cannot be identified on the raw data level alone using other, more conventional techniques. The application of ICA to EEG signals has become popular, as it provides two key features: it is a powerful way to remove artifacts from EEG data, and it helps to disentangle otherwise mixed brain signals. This chapter is concerned with evaluating and optimizing EEG decompositions by means of ICA. First, it discusses typical ICA results with reference to artifact- and brain-related components. Then, it elaborates on different EEG pre-processing steps, considered in light of the statistical assumptions underlying ICA. As such, the motivation for the chapter is to provide some practical guidelines for those researchers who wish to successfully decompose multi-channel EEG recordings.
