02256nas a2200217   4500000000100000000000100001008004100002100001600043700001700059700001600076700001500092700002500107700001800132700001900150700001900169245012100188856007200309490000700381520163600388022001402024       2023                            d1 aAsmaa Maher1 aSaeed Qaisar1 aN. Salankar1 aFeng Jiang1 aRyszard Tadeusiewicz1 aPawel Plawiak1 aAhmed EL-Latif1 aMohamed Hammad00aHybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning  uhttps://www.sciencedirect.com/science/article/pii/S02085216230002560 v433 a<p>The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and “Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are “Fractal Dimension” (FD), “Higher Order Spectra” (HOS), “Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the “Naïve Bayes” (NB), “Support Vector Machine” (SVM), “Random Forest” (RF), and “K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.</p>
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