Journal Article

EEG data fusion for improving accuracy of binary classification

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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Authors

Skarga-Bandurova, Inna
Biloborodova,Tetiana
Skarha-Bandurov, Illia
Zagorodna, Natalia
Shumova, Larisa

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2019
Date of RADAR deposit: 2022-07-01

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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