Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique

Complex <span>modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For diagnosis seizures, approaches that are simple and precise may be preferable rather than difficult time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) Adaptive learning technique proposed. The database signals subjected temporal spatial filtering remove unwanted noise increase detection accuracy classifier by selecting specific bands in which most data present. neural network based SVM classify test with respect training data. cost-sensitive proposed classifies where adaptive probability function helps prediction future samples leads improving time. algorithm compared existing shows can more </span>effectively.