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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Abstrakt

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
SENSORS nr 20,
ISSN: 1424-8220
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Browarczyk J., Kurowski A., Kostek B.: Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning// SENSORS -Vol. 20,iss. 8 (2020), s.2403-
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s20082403
Weryfikacja:
Politechnika Gdańska

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