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ORIGINAL ARTICLE
Year : 2022  |  Volume : 9  |  Issue : 3  |  Page : 90-95

Paralyzed patients-oriented electroencephalogram signals processing using convolutional neural network through python


1 Marmara University, Vocational School of Technical Sciences, Kartal, Turkey
2 Marmara University, Faculty of Technology, Department of Computer Engineering, Maltepe, Istanbul, Turkey

Correspondence Address:
Vedat Topuz
Vocational School of Technical Sciences, Marmara University Mehmet Genç Kulliyesi, Dragos, Kartal, Istanbul 34865
Turkey
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jnbs.jnbs_33_22

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Aim: Some of the systems that use brain–computer interfaces (BCIs) that translate brain activity patterns into commands for an interactive application make use of samples produced by motor imagery. This study focuses on processing electroencephalogram (EEG) signals using convolutional neural network (CNN). It is aimed to analyze EEG signals using Python, convert data to spectrogram, and classify them with CNN in this article. Materials and Methods: EEG data used were sampled at a sampling frequency of 128 Hz, in the range of 0.5–50 Hz. The EEG file is processed using Python programming language. Spectrogram images of the channels were obtained with the Python YASA library. Results: The success of the CNN model applied to dataset was found to be 89.58%. Conclusion: EEG signals make it possible to detect diseases using various machine learning methods. Deep learning-based CNN algorithms can also be used for this purpose.


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