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 …
A review on Virtual Reality and Augmented Reality use-cases of Brain Computer Interface based applications for smart cities
Abstract Brain Computer Interfaces (BCIs) and Extended Reality (XR) have seen significant
advances as independent disciplines over the past 50 years. XR has been developed as an …
advances as independent disciplines over the past 50 years. XR has been developed as an …
Internal feature selection method of CSP based on L1-norm and Dempster–Shafer theory
The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for
feature extraction in motor imagery (MI)-based brain–computer interfaces (BCIs). However …
feature extraction in motor imagery (MI)-based brain–computer interfaces (BCIs). However …
Learning temporal information for brain-computer interface using convolutional neural networks
Deep learning (DL) methods and architectures have been the state-of-the-art classification
algorithms for computer vision and natural language processing problems. However, the …
algorithms for computer vision and natural language processing problems. However, the …
A review of channel selection algorithms for EEG signal processing
T Alotaiby, FEA El-Samie, SA Alshebeili… - EURASIP Journal on …, 2015 - Springer
Digital processing of electroencephalography (EEG) signals has now been popularly used
in a wide variety of applications such as seizure detection/prediction, motor imagery …
in a wide variety of applications such as seizure detection/prediction, motor imagery …
Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces
One of the most important issues for the development of a motor-imagery based brain-
computer interface (BCI) is how to design a powerful classifier with strong generalization …
computer interface (BCI) is how to design a powerful classifier with strong generalization …
Brain–computer interfaces using sensorimotor rhythms: current state and future perspectives
Many studies over the past two decades have shown that people can use brain signals to
convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract …
convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract …
An EEG channel selection method for motor imagery based brain–computer interface and neurofeedback using Granger causality
H Varsehi, SMP Firoozabadi - Neural Networks, 2021 - Elsevier
Motor imagery (MI) brain–computer interface (BCI) and neurofeedback (NF) with
electroencephalogram (EEG) signals are commonly used for motor function improvement in …
electroencephalogram (EEG) signals are commonly used for motor function improvement in …
Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review
Background Hand rehabilitation is core to helping stroke survivors regain activities of daily
living. Recent studies have suggested that the use of electroencephalography-based brain …
living. Recent studies have suggested that the use of electroencephalography-based brain …
Graph convolution neural network based end-to-end channel selection and classification for motor imagery brain–computer interfaces
B Sun, Z Liu, Z Wu, C Mu, T Li - IEEE transactions on industrial …, 2022 - ieeexplore.ieee.org
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in
brain–computer interface (BCI). EEG signals require a large number of channels in the …
brain–computer interface (BCI). EEG signals require a large number of channels in the …