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. 2024 Jan 29;24(3):877. doi: 10.3390/s24030877

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© 2024 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Hilbert transform of a set of real-filtered EEGs: (a) The analyzed time series; (b) The signal power plotted in the time-frequency domain [125] Once the data has been processed and transformed into numerical features, it can serve as input for machine learning models. By choosing the right feature extraction method, researchers can derive informative and meaningful features from EEG signals, thereby enhancing the accuracy and efficiency of machine learning algorithms when applied to these signals.

Hilbert transform of a set of real-filtered EEGs: (a) The analyzed time series; (b) The signal power plotted in the time-frequency domain [125] Once the data has been processed and transformed into numerical features, it can serve as input for machine learning models. By choosing the right feature extraction method, researchers can derive informative and meaningful features from EEG signals, thereby enhancing the accuracy and efficiency of machine learning algorithms when applied to these signals.