Open AccessAlgorithms for Classification of Signals Derived From Human Brain
Author(s) -
Георги Димитров,
Galina Panayotova,
Boyan Jekov,
Pavel Petrov,
Iva Kostadinova,
Snejana Petrova,
Olexiy S. Bychkov,
V. P. Martsenyuk,
Aleksandar Parvanov
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.164
Subject(s) - support vector machine , adaboost , random forest , computer science , brain–computer interface , pattern recognition (psychology) , artificial intelligence , decision tree , statistical classification , field (mathematics) , k nearest neighbors algorithm , interface (matter) , machine learning , gaussian , electroencephalography , mathematics , psychology , physics , bubble , quantum mechanics , psychiatry , maximum bubble pressure method , parallel computing , pure mathematics
Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.