Deep learning for motor imagery EEG-based classification: A review
Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …
rapidly advances and inventions in deep learning techniques, and highly powerful and …
Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …
through the utilization of brain waves. It is worth noting that the application of BCI is not …
A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images
The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR)
have been an important imaging modality for assisting in the diagnosis and management of …
have been an important imaging modality for assisting in the diagnosis and management of …
[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …
human–system/robot interactions have been actively explored, especially in brain robotics …
A novel explainable machine learning approach for EEG-based brain-computer interface systems
Electroencephalographic (EEG) recordings can be of great help in decoding the open/close
hand's motion preparation. To this end, cortical EEG source signals in the motor cortex …
hand's motion preparation. To this end, cortical EEG source signals in the motor cortex …
AutoEncoder filter bank common spatial patterns to decode motor imagery from EEG
The present paper introduces a novel method, named AutoEncoder-Filter Bank Common
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
Brain functional and effective connectivity based on electroencephalography recordings: A review
Functional connectivity and effective connectivity of the human brain, representing statistical
dependence and directed information flow between cortical regions, significantly contribute …
dependence and directed information flow between cortical regions, significantly contribute …
[HTML][HTML] LMDA-Net: A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability
Z Miao, M Zhao, X Zhang, D Ming - NeuroImage, 2023 - Elsevier
Abstract Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a
challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically …
challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically …
Detection of atrial fibrillation using a machine learning approach
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …