Classification of EEG signals from musicians and non-musicians by neural networks

Sheng-Fu Liang, Tsung Hao Hsieh, Wei Hong Chen, Kuei Ju Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Long-term training will change the brain activity due to plasticity of the human brain. In this paper, an EEG-based neural network was proposed to assess neuroplasticity induced by musical training. A musical interval perception experiment was designed to acquire and compare the behavioral and neural responses of musicians and non-musicians. The auditory event related potentials (AEP) elicited by the consonant and dissonant intervals were combined and the PCA was used to extract discriminable features to classify the EEG recordings. Various linear and nonlinear classifiers were utilized for EEG classification and the results were also compared. The average accuracies of LDA, RBFSVM and BPNN are 94.6 %(PCs = 8), 95.9 %(PCs = 6), and 97.2 %(PCs = 20). ANOVA analysis of the classification results shows that the performance of BPNN is significantly better than the results of LDA (p<0.05). But there is no significantly difference with RBFSVM. The RBFSVM performs better stability if redundant principle components were included in the feature vector. The experimental results demonstrate the feasibility of assessing effects of musical training by AEP signals elicited by musical chord perception.

Original languageEnglish
Title of host publicationWCICA 2011 - 2011 World Congress on Intelligent Control and Automation, Conference Digest
Pages865-869
Number of pages5
DOIs
Publication statusPublished - 2011 Sep 9
Event2011 World Congress on Intelligent Control and Automation, WCICA 2011 - Taipei, Taiwan
Duration: 2011 Jun 212011 Jun 25

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)

Other

Other2011 World Congress on Intelligent Control and Automation, WCICA 2011
CountryTaiwan
CityTaipei
Period11-06-2111-06-25

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Classification of EEG signals from musicians and non-musicians by neural networks'. Together they form a unique fingerprint.

Cite this