title: Automated identification of neural correlates of continuous variables.
abstract: BACKGROUND
  The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables.
  
  
  METHODS
  A method is presented for the automated identification of features that differentiate two or more groups in neurological datasets based upon a spectral decomposition of the feature set. Furthermore, the method is able to identify features that relate to continuous independent variables.
  
  
  RESULTS
  The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally, the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions.
  
  
  METHODS
  The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases.
  
  
  CONCLUSIONS
  The proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK. Electronic address: i.daly@reading.ac.uk.
authors.forename: Ian
authors.initials: I
authors.lastname: Daly
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: Faustina
authors.initials: F
authors.lastname: Hwang
authors.affiliation: Interdisciplinary Centre for Computer Music Research, Plymouth University, Plymouth, UK.
authors.forename: Alexis
authors.initials: A
authors.lastname: Kirke
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: Asad
authors.initials: A
authors.lastname: Malik
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: James
authors.initials: J
authors.lastname: Weaver
authors.affiliation: Interdisciplinary Centre for Computer Music Research, Plymouth University, Plymouth, UK.
authors.forename: Duncan
authors.initials: D
authors.lastname: Williams
authors.affiliation: Interdisciplinary Centre for Computer Music Research, Plymouth University, Plymouth, UK.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Miranda
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: Slawomir J
authors.initials: SJ
authors.lastname: Nasuto
mesh_terms: D000161:Acoustic Stimulation; D001307:Auditory Perception / Q000502:physiology; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004644:Emotions / Q000502:physiology; D005071:Evoked Potentials; D006801:Humans; D008959:Models, Neurological; D009146:Music; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Eigen-decomposition; Electroencephalogram (EEG); Feature selection; Neural correlates