Authors:

Ni Putu Dewi Angreni, Agus Muliantara, Yuriko Christian

Abstract:

“In the pattern recognition field, features or object’s characteristics are one of the key points to recognizing them. The feature extraction process will see that objects have different features, where the features are obtained through the analysis process from the extractor, such as for data statistics, energy, power spectral, and so on. This study aims to enrich the point of view of EEG signal features by quantifying the signal. It will be analyzed whether the features obtained by quantization represent the EEG signal object from different viewpoints. This research uses the DEAP dataset, with the result being a feature vector that will be included in the artificial neural network classifier using the Keras library. The experiment carried out is to try to enter quantized and Non-quantized feature vectors into the classifier. As a result, the accuracy of the classification process with the quantization vector was 75%, and the accuracy in the Non-quantized vector classification process was only 58%. These results indicate the EEG signal quantization feature can represent the EEG signal object. Keywords: EEG signal, quantization, DEAP, feature extraction, pattern recognition”

Keywords

EEG signal, quantization, DEAP, feature extraction, pattern recognition

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PDF:

https://jurnal.harianregional.com/jlk/full-64454

Published

2020-11-22

How To Cite

ANGRENI, Ni Putu Dewi; MULIANTARA, Agus; CHRISTIAN, Yuriko. Quantization-Based Novel Extraction Method Of EEG Signal For Classification.JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 9, n. 2, p. 169-176, nov. 2020. ISSN 2654-5101. Available at: https://ojs.unud.ac.id/index.php/JLK/article/view/64454. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/JLK.2020.v09.i02.p02.

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Issue

Vol 9 No 2 (2020): JELIKU Volume 9 No 2, November 2020

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License