EEG Signal Analysis and Classification

Electroencephalogram (EEG) and its background.- Significance of EEG signals in medical and health research.- Objectives and structures of the book.- Random sampling in the detection of epileptic EEG signals.- A novel clustering technique for the detection of epileptic seizures.- A statistical framework for classifying epileptic seizure from multi-category EEG signals.- Injecting principal component analysis with the OA scheme in the epileptic EEG signal classification.- Cross-correlation aided logistic regression model for the identification of motor imagery EEG signals in BCI applications.- Modified CC-LR Algorithm for identification of MI based EEG signals.- Improving prospective performance in the MI recognition: LS-SVM with tuning hyper parameters.- Comparative study: Motor area EEG and All-channels EEG.- Optimum allocation aided Naive Bayes based learning process for the detection of MI tasks.- Summary discussions on the methods, future directions and conclusions.

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• Engenharia biomédica
• Tecnologia da informação: aspectos gerais
• Engenharia electrónica
• Processamento de sinais
• Inteligência artificial
• Visão por computador
• Equipamento e técnicas médicas

Electroencephalogram (EEG);Epileptic seizure;Feature extraction;Classification;Brain computer interface (BCI);Motor imagery (MI);Clustering technique (CT);Simple random sampling (SRS);Cross-correlation (CC) technique;Optimum allocation technique;Least square supper vector machine (LS-SVM);Logistic regression (LR);Kernal logistic regression (KLR);Optimum allocation sampling;k-NN;Multinomial logistic regression with a ridge estimator;Support vector machine (SVM);Naive Bayes method