title: Decoding intracranial EEG data with multiple kernel learning method.
abstract: BACKGROUND
  Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their application to electrophysiological data has been less common especially in the analysis of human intracranial electroencephalography (iEEG, also known as electrocorticography or ECoG) data, which contains a rich spectrum of signals recorded from a relatively high number of recording sites.
  
  
  METHODS
  In the present work, we introduce a novel approach to determine the contribution of different bandwidths of EEG signal in different recording sites across different experimental conditions using the Multiple Kernel Learning (MKL) method.
  
  
  CONCLUSIONS
  To validate and compare the usefulness of our approach, we applied this method to an ECoG dataset that was previously analysed and published with univariate methods.
  
  
  RESULTS
  Our findings proved the usefulness of the MKL method in detecting changes in the power of various frequency bands during a given task and selecting automatically the most contributory signal in the most contributory site(s) of recording.
  
  
  CONCLUSIONS
  With a single computation, the contribution of each frequency band in each recording site in the estimated multivariate model can be highlighted, which then allows formulation of hypotheses that can be tested a posteriori with univariate methods if needed.
authors.affiliation: Laboratory of Behavioral & Cognitive Neuroscience, Stanford Human Intracranial Cognitive Electrophysiology Program (SHICEP), Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USA; Department of Computer Science, University College London, United Kingdom.
authors.forename: Jessica
authors.initials: J
authors.lastname: Schrouff
authors.affiliation: Department of Computer Science, University College London, United Kingdom.
authors.forename: Janaina
authors.initials: J
authors.lastname: Mourão-Miranda
authors.affiliation: Cyclotron Research Centre, University of Liège, Belgium.
authors.forename: Christophe
authors.initials: C
authors.lastname: Phillips
authors.affiliation: Laboratory of Behavioral & Cognitive Neuroscience, Stanford Human Intracranial Cognitive Electrophysiology Program (SHICEP), Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USA. Electronic address: jparvizi@stanford.edu.
authors.forename: Josef
authors.initials: J
authors.lastname: Parvizi
mesh_terms: D001921:Brain / Q000503:physiopathology / Q000601:surgery; D066264:Datasets as Topic; D000069279:Drug Resistant Epilepsy / Q000503:physiopathology / Q000601:surgery; D000069280:Electrocorticography / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007600:Judgment / Q000502:physiology; D000069550:Machine Learning; D055641:Mathematical Concepts; D061212:Memory, Episodic; D009483:Neuropsychological Tests; D010363:Pattern Recognition, Automated / Q000379:methods; D010364:Pattern Recognition, Visual / Q000502:physiology; D012146:Rest; D012649:Self Concept; D012660:Semantics; D012815:Signal Processing, Computer-Assisted
keywords: Feature selection; Intracranial EEG; Machine learning; Multiple kernel learning