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Correction to: SleepExpertNet: high‑performance and class‑balanced deep learning approach inspired from the expert neurologists for sleep stage classification.

, , , , , and . J. Ambient Intell. Humaniz. Comput., 14 (6): 8085 (2023)

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Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification., , , , , and . Inf. Sci., (2019)Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control., , , and . SMC, page 511-514. IEEE, (2018)Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms., , , , , , and . Inf. Sci., (2018)Finite element analysis for normal pressure hydrocephalus: The effects of the integration of sulci., , , , , , , and . Medical Image Anal., 24 (1): 235-244 (2015)Reduced Burden of Individual Calibration Process in Brain-Computer Interface by Clustering the Subjects based on Brain Activation., , , , , and . SMC, page 2139-2143. IEEE, (2019)Classification of computed tomography scanner manufacturer using support vector machine., , , and . BCI, page 85-87. IEEE, (2017)Classification of Upper Limb Movements Using Convolutional Neural Network with 3D Inception Block., , , and . BCI, page 1-5. IEEE, (2020)Estimation on the development of cerebral edema from computed tomography preliminary studies for pediatric traumatic brain injury patients., , and . BCI, page 69-71. IEEE, (2013)Robust arterial blood pressure onset detection method from signal artifacts., , , and . BCI, page 1-3. IEEE, (2018)Hybrid Zero-Training BCI based on Convolutional Neural Network for Lower-limb Motor-Imagery., , and . BCI, page 1-4. IEEE, (2021)