Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform

Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform

Brain–computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the n...

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Bibliographic Details
Main Authors: Patryk Zych, Kacper Filipek, Agata Mrozek-Czajkowska, Piotr Kuwałek
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Sensors
Subjects:
brain–computer interface
electroencephalography
motor execution
hand gesture
empirical wavelet transform
machine learning
Online Access:https://www.mdpi.com/1424-8220/25/11/3284
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