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EEG Data Fusion for Improving Accuracy of Binary Classification
Inna Skarga-Bandurova, Tetiana Biloborodova, Illia Skarha-Bandurov, Natalia Zagorodna, Larisa Shumova
130 - 134
10.3233/978-1-61499-959-1-130
Studies in Health Technology and Informatics
Volume 258: ICT for Health Science Research
Abstract

The paper refers to the problem of classification for multiple medical data. The proposed methodology for EEG data processing consists of seven stages and assumes different variations of the Dempster-Shafer technique as a base instrument for data fusion. Attained accuracy is comparable to other more popular algorithms and can be a promising further basis for real-time data classification.

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