Evaluation of Machine Learning Algorithms for Classification of EEG Signals
In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN)...
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MDPI AG
2022-06-01
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| Online Access: | https://www.mdpi.com/2227-7080/10/4/79 |
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| author | Francisco Javier Ramírez-Arias Enrique Efren García-Guerrero Esteban Tlelo-Cuautle Juan Miguel Colores-Vargas Eloisa García-Canseco Oscar Roberto López-Bonilla Gilberto Manuel Galindo-Aldana Everardo Inzunza-González |
| author_facet | Francisco Javier Ramírez-Arias Enrique Efren García-Guerrero Esteban Tlelo-Cuautle Juan Miguel Colores-Vargas Eloisa García-Canseco Oscar Roberto López-Bonilla Gilberto Manuel Galindo-Aldana Everardo Inzunza-González |
| author_sort | Francisco Javier Ramírez-Arias |
| collection | DOAJ |
| description | In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices. |
| first_indexed | 2024-03-09T12:26:18Z |
| format | Article |
| id | doaj.art-f2cf375428da4424a22c506346e9d9d2 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-09T12:26:18Z |
| publishDate | 2022-06-01 |
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| series | Technologies |
| spelling | doaj.art-f2cf375428da4424a22c506346e9d9d22023-11-30T22:34:54ZengMDPI AGTechnologies2227-70802022-06-011047910.3390/technologies10040079Evaluation of Machine Learning Algorithms for Classification of EEG SignalsFrancisco Javier Ramírez-Arias0Enrique Efren García-Guerrero1Esteban Tlelo-Cuautle2Juan Miguel Colores-Vargas3Eloisa García-Canseco4Oscar Roberto López-Bonilla5Gilberto Manuel Galindo-Aldana6Everardo Inzunza-González7Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada 22860, MexicoFacultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada 22860, MexicoDepartamento de Electrónica, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No. 1, Santa María Tonanzintla, Puebla 72840, MexicoFacultad de Ciencias de la Ingeniería y Tecnología, Universidad Autónoma de Baja California, Blvd. Universitario No. 1000, Valle de las Palmas, Tijuana 21500, MexicoFacultad de Ciencias, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada 22860, MexicoFacultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada 22860, MexicoFacultad de Ingeniería y Negocios, Guadalupe Victoria, Universidad Autónoma de Baja California, Carretera Estatal No. 3, Gutiérrez, Mexicali 21720, MexicoFacultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada 22860, MexicoIn brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.https://www.mdpi.com/2227-7080/10/4/79EEGBCIfeature extractionartificial intelligencemachine learningdeep learning |
| spellingShingle | Francisco Javier Ramírez-Arias Enrique Efren García-Guerrero Esteban Tlelo-Cuautle Juan Miguel Colores-Vargas Eloisa García-Canseco Oscar Roberto López-Bonilla Gilberto Manuel Galindo-Aldana Everardo Inzunza-González Evaluation of Machine Learning Algorithms for Classification of EEG Signals Technologies EEG BCI feature extraction artificial intelligence machine learning deep learning |
| title | Evaluation of Machine Learning Algorithms for Classification of EEG Signals |
| title_full | Evaluation of Machine Learning Algorithms for Classification of EEG Signals |
| title_fullStr | Evaluation of Machine Learning Algorithms for Classification of EEG Signals |
| title_full_unstemmed | Evaluation of Machine Learning Algorithms for Classification of EEG Signals |
| title_short | Evaluation of Machine Learning Algorithms for Classification of EEG Signals |
| title_sort | evaluation of machine learning algorithms for classification of eeg signals |
| topic | EEG BCI feature extraction artificial intelligence machine learning deep learning |
| url | https://www.mdpi.com/2227-7080/10/4/79 |
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