- Tytuł:
- Classification of EEG signal by methods of machine learning
- Autorzy:
-
Alyamani, Amina
Yasniy, Oleh - Powiązania:
- https://bibliotekanauki.pl/articles/1837774.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polskie Towarzystwo Promocji Wiedzy
- Tematy:
-
machine learning
EEG signal
classification
data balancing
feature extraction
uczenie maszynowe
sygnał EEG
klasyfikacja
równoważenie danych
ekstrakcja cech - Opis:
- Electroencephalogram (EEG) signal of two healthy subjects that was available from literature, was studied using the methods of machine learning, namely, decision trees (DT), multilayer perceptron (MLP), K-nearest neighbours (kNN), and support vector machines (SVM). Since the data were imbalanced, the appropriate balancing was performed by Kmeans clustering algorithm. The original and balanced data were classified by means of the mentioned above 4 methods. It was found, that SVM showed the best result for the both datasets in terms of accuracy. MLP and kNN produce the comparable results which are almost the same. DT accuracies are the lowest for the given dataset, with 83.82% for the original data and 61.48% for the balanced data.
- Źródło:
-
Applied Computer Science; 2020, 16, 4; 56-63
1895-3735 - Pojawia się w:
- Applied Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł