Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification

• Autorzy: Wójcik Grzegorz M. , Kawiak Andrzej , Kwasniewicz Lukasz , Schneider Piotr , Masiak Jolanta

Identyfikatory

DOI

10.1515/bams-2019-0031

• Języki publikacji: EN

Abstrakty

EN

The Event-Related Potentials were investigated on a group of 70 participants using the dense array electroencephalographic amplifier with photogrammetry geodesic station. The source localisation was computed for each participant. The activity of brodmann areas (BAs) involved in the brain cortical activity of each participant was measured. Then the mean electric charge flowing through particular areas was calculated. The five different machine learning tools (logistic regression, boosted decision tree, Bayes point machine, classic neural network and averaged perceptron classifier) from the Azure ecosystem were trained, and their accuracy was tested in the task of distinguishing standard and target responses in the experiment. The efficiency of each tool was compared, and it was found out that the best tool was logistic regression and the boosted decision tree in our task. Such an approach can be useful in eliminating somatosensory responses in experimental psychology or even in establishing new communication protocols with mildly mentally disabled subjects.

Słowa kluczowe

EN

Azure Machine Learning; classifiers; P300; quantitative EEG; source localisation

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; De Gruyter
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2019
• Tom: Vol. 15, no. 3
• Strony: art. no. 20190031
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Wójcik Grzegorz M.

• Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland

autor

Kawiak Andrzej

• Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland

autor

Kwasniewicz Lukasz

• Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland

autor

Schneider Piotr

• Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland

autor

Masiak Jolanta

• Neurophysiological Independent Unit of the Department of Psychiatry, Medical University of Lublin, ul. Gluska 2, 20-439 Lublin, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).