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

Zych, Patryk; Filipek, Kacper; Mrozek-Czajkowska, Agata; Kuwałek, Piotr

doi:10.3390/s25113284

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

¹

Institute of Communication and Computer Networks, Poznan University of Technology, 60-965 Poznan, Poland

²

Faculty of Mechanical Engineering, Poznan University of Technology, 60-965 Poznan, Poland

³

Institute of Material Technology, Poznan University of Technology, 60-965 Poznan, Poland

⁴

Institute of Electrical Engineering and Electronics, Poznan University of Technology, 60-965 Poznan, Poland

^*

Author to whom correspondence should be addressed.

Sensors 2025, 25(11), 3284; https://doi.org/10.3390/s25113284

Submission received: 24 April 2025 / Revised: 20 May 2025 / Accepted: 22 May 2025 / Published: 23 May 2025

(This article belongs to the Special Issue Signal Processing and Machine Learning Approaches for Processing Biomedical Sensor Signals)

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Abstract

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 need for physical interaction with external devices. This study investigates the performance of traditional classifiers—specifically, linear discriminant analysis (LDA) and support vector machines (SVMs)—in comparison with a hybrid neural network model for EEG-based gesture classification. The dataset comprised EEG recordings of seven distinct gestures performed by 33 participants. Binary classification tasks were conducted using both raw windowed EEG signals and features extracted via bandpower and the empirical wavelet transform (EWT). The hybrid neural network architecture demonstrated higher classification accuracy compared to the standard classifiers. These findings suggest that combining featuring extraction with deep learning models offers a promising approach for improving EEG gesture recognition in BCI systems.

Keywords: brain–computer interface; electroencephalography; motor execution; hand gesture; empirical wavelet transform; machine learning; classification

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MDPI and ACS Style

Zych, P.; Filipek, K.; Mrozek-Czajkowska, A.; Kuwałek, P. Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform. Sensors 2025, 25, 3284. https://doi.org/10.3390/s25113284

AMA Style

Zych P, Filipek K, Mrozek-Czajkowska A, Kuwałek P. Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform. Sensors. 2025; 25(11):3284. https://doi.org/10.3390/s25113284

Chicago/Turabian Style

Zych, Patryk, Kacper Filipek, Agata Mrozek-Czajkowska, and Piotr Kuwałek. 2025. "Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform" Sensors 25, no. 11: 3284. https://doi.org/10.3390/s25113284

APA Style

Zych, P., Filipek, K., Mrozek-Czajkowska, A., & Kuwałek, P. (2025). Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform. Sensors, 25(11), 3284. https://doi.org/10.3390/s25113284

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

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