Automated identification of neural correlates of continuous variables.
J Neurosci Methods
; 242: 65-71, 2015 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-25546485
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. NEWMETHOD:
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 EXISTINGMETHODS:
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Automatizado de Padrão
/
Eletroencefalografia
Tipo de estudo:
Diagnostic_studies
/
Evaluation_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Neurosci Methods
Ano de publicação:
2015
Tipo de documento:
Article