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J Neurosci Methods. 2015 Mar 15;242:65-71. doi: 10.1016/j.jneumeth.2014.12.012. Epub 2014 Dec 26.

Automated identification of neural correlates of continuous variables.

Author information

1
Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK. Electronic address: i.daly@reading.ac.uk.
2
Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
3
Interdisciplinary Centre for Computer Music Research, Plymouth University, Plymouth, UK.

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.

NEW METHOD:

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.

COMPARISON WITH EXISTING 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.

KEYWORDS:

Eigen-decomposition; Electroencephalogram (EEG); Feature selection; Neural correlates

PMID:
25546485
DOI:
10.1016/j.jneumeth.2014.12.012
[Indexed for MEDLINE]

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