An eigenvalues-based covariance matrix bootstrap model integrated with support vector machines for multichannel eeg signals analysis

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• who: Mohammed Diykh from the School of Mathematics Physics and Computing, University of Southern, Australia, USQ have published the research: An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated with Support Vector Machines for Multichannel EEG Signals Analysis, in the Journal: (JOURNAL)
• what: EEG-rhythmsbased features for automatic identification of alcohol EEG signals were also proposed by the study of Taran and Bajaj ; in that study, an extreme_learning_machine (ELM) and a least squares SVM classifiers were used to detect nonalcoholic and alcoholic EEG signals, with the investigators' techniques showing an accuracy of 97.92%. The study . . .
  

   

  

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