An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces
AM Roy - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification
G Dai, J Zhou, J Huang, N Wang - Journal of neural engineering, 2020 - iopscience.iop.org
Objective. Electroencephalography (EEG) motor imagery classification has been widely
used in healthcare applications such as mobile assistive robots and post-stroke …
used in healthcare applications such as mobile assistive robots and post-stroke …
EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
EEG-Based driver Fatigue Detection using Spatio-Temporal Fusion network with brain region partitioning strategy
F Hu, L Zhang, X Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Detecting driver fatigue is critical for ensuring traffic safety. Electroencephalography (EEG) is
the golden standard for brain activity measurement and is considered a good indicator of …
the golden standard for brain activity measurement and is considered a good indicator of …
Motor imagery EEG decoding method based on a discriminative feature learning strategy
L Yang, Y Song, K Ma, L Xie - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
With the rapid development of deep learning, more and more deep learning-based motor
imagery electroencephalograph (EEG) decoding methods have emerged in recent years …
imagery electroencephalograph (EEG) decoding methods have emerged in recent years …
Review of classical dimensionality reduction and sample selection methods for large-scale data processing
X Xu, T Liang, J Zhu, D Zheng, T Sun - Neurocomputing, 2019 - Elsevier
In the era of big data, all types of data with increasing samples and high-dimensional
attributes are demonstrating their important roles in various fields, such as data mining …
attributes are demonstrating their important roles in various fields, such as data mining …
A new multi-objective wrapper method for feature selection–accuracy and stability analysis for BCI
Feature selection is an important step in building classifiers for high-dimensional data
problems, such as EEG classification for BCI applications. This paper proposes a new …
problems, such as EEG classification for BCI applications. This paper proposes a new …
Brain wave classification using long short-term memory network based OPTICAL predictor
Brain-computer interface (BCI) systems having the ability to classify brain waves with greater
accuracy are highly desirable. To this end, a number of techniques have been proposed …
accuracy are highly desirable. To this end, a number of techniques have been proposed …
Global research on artificial intelligence-enhanced human electroencephalogram analysis
The application of artificial intelligence (AI) technologies in assisting human
electroencephalogram (EEG) analysis has become an active scientific field. This study aims …
electroencephalogram (EEG) analysis has become an active scientific field. This study aims …
A deep learning approach for motor imagery EEG signal classification
Over the last few decades, the use of electroencephalography (EEG) signals for motor
imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep …
imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep …