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Label-free microfluidic cell sorting and detection for rapid blood analysis
Blood tests are considered as standard clinical procedures to screen for markers of diseases
and health conditions. However, the complex cellular background (> 99.9% RBCs) and …
and health conditions. However, the complex cellular background (> 99.9% RBCs) and …
Deep learning in EEG-based BCIs: A comprehensive review of transformer models, advantages, challenges, and applications
Brain-computer interfaces (BCIs) have undergone significant advancements in recent years.
The integration of deep learning techniques, specifically transformers, has shown promising …
The integration of deep learning techniques, specifically transformers, has shown promising …
[HTML][HTML] An adversarial discriminative temporal convolutional network for EEG-based cross-domain emotion recognition
Abstract Domain adaptation (DA) tackles the problem where data from the source domain
and target domain have different underlying distributions. In cross-domain (cross-subject or …
and target domain have different underlying distributions. In cross-domain (cross-subject or …
Exploring new horizons in neuroscience disease detection through innovative visual signal analysis
Brain disorders pose a substantial global health challenge, persisting as a leading cause of
mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain …
mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain …
[HTML][HTML] CNN-LSTM and post-processing for EMG-based hand gesture recognition
Abstract Hand Gesture Recognition (HGR) using electromyography (EMG) signals is a
challenging problem due to the variability and noise in the signals across individuals. This …
challenging problem due to the variability and noise in the signals across individuals. This …
[کتاب][B] Computational methods for deep learning
WQ Yan - 2021 - Springer
This book has been drafted based on my lectures and seminars from recent years for
postgraduate students at Auckland University of Technology (AUT), New Zealand. We have …
postgraduate students at Auckland University of Technology (AUT), New Zealand. We have …
Toward robust, adaptiveand reliable upper-limb motion estimation using machine learning and deep learning–a survey in myoelectric control
To develop multi-functionalhuman-machine interfaces that can help disabled people
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
A generative model to synthesize EEG data for epileptic seizure prediction
Objective: Scarcity of good quality electroencephalography (EEG) data is one of the
roadblocks for accurate seizure prediction. This work proposes a deep convolutional …
roadblocks for accurate seizure prediction. This work proposes a deep convolutional …
Trahgr: Transformer for hand gesture recognition via electromyography
Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG)
signals have recently shown considerable potential for development of advanced …
signals have recently shown considerable potential for development of advanced …
E2CNN: An efficient concatenated CNN for classification of surface EMG extracted from upper limb
Surface electromyography is a bioelectrical indicator that emerges during muscle
contraction and has been widely used in a variety of clinical applications. Several prosthetic …
contraction and has been widely used in a variety of clinical applications. Several prosthetic …