Review of machine learning techniques for EEG based brain computer interface
S Aggarwal, N Chugh - Archives of Computational Methods in …, 2022 - Springer
A brain computer interface (BCI) framework uses computer algorithms to detect mental
activity patterns and manipulate external devices. Because of its simplicity and non …
activity patterns and manipulate external devices. Because of its simplicity and non …
EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
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 …
Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
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 …
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 …
Bispectrum-based channel selection for motor imagery based brain-computer interfacing
The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily
affected by noise and redundant information that exists in the multi-channel …
affected by noise and redundant information that exists in the multi-channel …
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
MT Sadiq, X Yu, Z Yuan - Expert Systems with Applications, 2021 - Elsevier
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 …
A brain–computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli
Goal: Traditional visual brain–computer interfaces (BCIs) preferred to use large-size stimuli
to attract the user's attention and elicit distinct electroencephalography (EEG) features …
to attract the user's attention and elicit distinct electroencephalography (EEG) features …