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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
Abstract
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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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s20082403
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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SENSORS
no. 20,
ISSN: 1424-8220 - Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- 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:
- Digital Object Identifier (open in new tab) 10.3390/s20082403
- Verified by:
- Gdańsk University of Technology
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