Intra-and inter-subject variability in EEG-based sensorimotor brain computer interface: a review
Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit
sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the …
sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the …
Toward open-world electroencephalogram decoding via deep learning: A comprehensive survey
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and
cognitive content of neural processing based on noninvasively measured brain activity …
cognitive content of neural processing based on noninvasively measured brain activity …
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 …
Correlation-based channel selection and regularized feature optimization for MI-based BCI
Multi-channel EEG data are usually necessary for spatial pattern identification in motor
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …
Temporally constrained sparse group spatial patterns for motor imagery BCI
Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
In this study, three popular signal processing techniques (Empirical Mode Decomposition,
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Convolutional neural network based approach towards motor imagery tasks EEG signals classification
This paper introduces a methodology based on deep convolutional neural networks (DCNN)
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …
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 …
Algorithm supported induction for building theory: How can we use prediction models to theorize?
Across many fields of social science, machine learning (ML) algorithms are rapidly
advancing research as tools to support traditional hypothesis testing research (eg, through …
advancing research as tools to support traditional hypothesis testing research (eg, through …
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
Background: Analysis and classification of extensive medical data (eg
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …