Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
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 sta...
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doaj-fbfbeeb5c6be4a0aaaee4514a33a3c492020-11-25T01:44:06ZengMDPI AGSensors1424-82202020-04-01202403240310.3390/s20082403Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine LearningJakub Browarczyk0Adam Kurowski1Bozena Kostek2Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, PolandMultimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, PolandAudio Acoustics Laboratory, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, PolandThe 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. <i>k</i>-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.https://www.mdpi.com/1424-8220/20/8/2403electroencephalography (EEG)brain–computer interface (BCI)feature extractionautomatic classificationdeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jakub Browarczyk Adam Kurowski Bozena Kostek |
spellingShingle |
Jakub Browarczyk Adam Kurowski Bozena Kostek Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning Sensors electroencephalography (EEG) brain–computer interface (BCI) feature extraction automatic classification deep learning |
author_facet |
Jakub Browarczyk Adam Kurowski Bozena Kostek |
author_sort |
Jakub Browarczyk |
title |
Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning |
title_short |
Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning |
title_full |
Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning |
title_fullStr |
Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning |
title_full_unstemmed |
Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning |
title_sort |
analyzing the effectiveness of the brain–computer interface for task discerning based on machine learning |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-04-01 |
description |
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. <i>k</i>-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. |
topic |
electroencephalography (EEG) brain–computer interface (BCI) feature extraction automatic classification deep learning |
url |
https://www.mdpi.com/1424-8220/20/8/2403 |
work_keys_str_mv |
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