Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
Machine learning-guided anesthesiology: A review of recent advances and clinical applications
: Anesthesia is the process of inducing and experiencing various conditions, such as
painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures …
painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures …
DSCNN-CAU: deep-learning-based mental activity classification for IoT implementation toward portable BCI
Mental activity classification (MAC) based on electroencephalogram (EEG) is used in the
brain–computer interface (BCI) and neurofeedback applications. For this purpose, machine …
brain–computer interface (BCI) and neurofeedback applications. For this purpose, machine …
A Wearable Brain-Computer Interface with Fewer EEG Channels for Online Motor Imagery Detection
Motor imagery-based brain-computer interfaces (MI-BCIs) have significant potential for
neurorehabilitation and motor recovery. However, most BCI systems employ multi-channel …
neurorehabilitation and motor recovery. However, most BCI systems employ multi-channel …
[HTML][HTML] Meta-eeg: Meta-learning-based class-relevant eeg representation learning for zero-calibration brain–computer interfaces
Transfer learning for motor imagery-based brain–computer interfaces (MI-BCIs) struggles
with inter-subject variability, hindering its generalization to new users. This paper proposes …
with inter-subject variability, hindering its generalization to new users. This paper proposes …
EEG-based emergency braking intention detection during simulated driving
X Liang, Y Yu, Y Liu, K Liu, Y Liu, Z Zhou - BioMedical Engineering OnLine, 2023 - Springer
Background Current research related to electroencephalogram (EEG)-based driver's
emergency braking intention detection focuses on recognizing emergency braking from …
emergency braking intention detection focuses on recognizing emergency braking from …
Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces
X Liang, Y Liu, Y Yu, K Liu, Y Liu, Z Zhou - Brain sciences, 2023 - mdpi.com
Convolutional neural networks (CNNs) have shown great potential in the field of brain–
computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram …
computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram …
State-of-the-art mental tasks classification based on electroencephalograms: a review
Electroencephalograms (EEGs) play an important role in analyzing different mental tasks
and neurological disorders. Hence, they are a critical component for designing various …
and neurological disorders. Hence, they are a critical component for designing various …
[HTML][HTML] An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model
Electroencephalography (EEG) is a non-invasive technique with high temporal resolution
and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data …
and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data …
Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification
X Zhang, Y Wang, Y Tang, Z Wang - Health Information Science and …, 2024 - Springer
Purpose In the brain-computer interface (BCI), motor imagery (MI) could be defined as the
Electroencephalogram (EEG) signals through imagined movements, and ultimately enabling …
Electroencephalogram (EEG) signals through imagined movements, and ultimately enabling …