title: Dimensionality reduction for the analysis of brain oscillations.
abstract: Neuronal oscillations have been shown to be associated with perceptual, motor and cognitive brain operations. While complex spatio-temporal dynamics are a hallmark of neuronal oscillations, they also represent a formidable challenge for the proper extraction and quantification of oscillatory activity with non-invasive recording techniques such as EEG and MEG. In order to facilitate the study of neuronal oscillations we present a general-purpose pre-processing approach, which can be applied for a wide range of analyses including but not restricted to inverse modeling and multivariate single-trial classification. The idea is to use dimensionality reduction with spatio-spectral decomposition (SSD) instead of the commonly and almost exclusively used principal component analysis (PCA). The key advantage of SSD lies in selecting components explaining oscillations-related variance instead of just any variance as in the case of PCA. For the validation of SSD pre-processing we performed extensive simulations with different inverse modeling algorithms and signal-to-noise ratios. In all these simulations SSD invariably outperformed PCA often by a large margin. Moreover, using a database of multichannel EEG recordings from 80 subjects we show that pre-processing with SSD significantly increases the performance of single-trial classification of imagined movements, compared to the classification with PCA pre-processing or without any dimensionality reduction. Our simulations and analysis of real EEG experiments show that, while not being supervised, the SSD algorithm is capable of extracting components primarily relating to the signal of interest often using as little as 20% of the data variance, instead of > 90% variance as in case of PCA. Given its ease of use, absence of supervision, and capability to efficiently reduce the dimensionality of multivariate EEG/MEG data, we advocate the application of SSD pre-processing for the analysis of spontaneous and induced neuronal oscillations in normal subjects and patients.
authors.affiliation: Neural Engineering Group, Department of Biomedical Engineering, The City College of New York, New York City, NY, USA; Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Focus Neurotechnology, Berlin, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Haufe
authors.affiliation: Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
authors.forename: Sven
authors.initials: S
authors.lastname: Dähne
authors.affiliation: Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité University Medicine Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia. Electronic address: vadim.nikulin@charite.de.
authors.forename: Vadim V
authors.initials: VV
authors.lastname: Nikulin
mesh_terms: D000328:Adult; D001921:Brain; D058256:Brain Waves / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted
keywords: Brain oscillations; Dimensionality reduction; Principal component analysis; Spatio-spectral decomposition