[HTML][HTML] 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 …
A review of critical challenges in MI-BCI: From conventional to deep learning methods
Brain-computer interfaces (BCIs) have achieved significant success in controlling external
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …
A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals
Abstract In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is
converted into a control signal through processing a specific pattern in brain signals …
converted into a control signal through processing a specific pattern in brain signals …
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 …
Deep representation-based domain adaptation for nonstationary EEG classification
In the context of motor imagery, electroencephalography (EEG) data vary from subject to
subject such that the performance of a classifier trained on data of multiple subjects from a …
subject such that the performance of a classifier trained on data of multiple subjects from a …
[HTML][HTML] A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …
communication through the utilization of neural activity generated due to kinesthetic …
[HTML][HTML] Signal processing techniques for motor imagery brain computer interface: A review
S Aggarwal, N Chugh - Array, 2019 - Elsevier
Abstract Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel
for communication to those who are suffering from neuronal disorders. The designing of an …
for communication to those who are suffering from neuronal disorders. The designing of an …
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 …
MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
P Autthasan, R Chaisaen… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …
control of several applications by decoding neurophysiological phenomena, which are …
Learning common time-frequency-spatial patterns for motor imagery classification
The common spatial patterns (CSP) algorithm is the most popular spatial filtering method
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain …
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain …