BRAIN DATA ANALYSIS AND MANAGEMENT

Roumiana Ilieva, Petia Georgieva, Stanislava Petrova

Abstract


Machine Learning (ML) techniques have been extensively applied in bioinformatics. In this paper, we chose RapidMiner software to analyze brain data (EEG signals) in order to discriminate human emotions while subjects were observing images. Five ML classification algorithms were studied: k-Nearest Neighbor (kNN), Naive Bayes, Support Vector Machine, Artificial Neural Networks and Decision Tree. kNN and ensemble classifiers achieved above 80% accuracy with test data. This is a very encouraging result taking into account the fact that brain signals are highly non-stationary and noisy and therefore it is quite challenging data for analysis and management. 


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References


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DOI: http://dx.doi.org/10.18007/gisap:msp.v0i7.1071

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