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 …
LSTM-based EEG classification in motor imagery tasks
P Wang, A Jiang, X Liu, J Shang… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Classification of motor imagery electroencephalograph signals is a fundamental problem in
brain–computer interface (BCI) systems. We propose in this paper a classification framework …
brain–computer interface (BCI) systems. We propose in this paper a classification framework …
A deep learning scheme for motor imagery classification based on restricted Boltzmann machines
Motor imagery classification is an important topic in brain-computer interface (BCI) research
that enables the recognition of a subject's intension to, eg, implement prosthesis control. The …
that enables the recognition of a subject's intension to, eg, implement prosthesis control. The …
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 …
Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework
Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity
We apply artificial neural network (ANN) for recognition and classification of
electroencephalographic (EEG) patterns associated with motor imagery in untrained …
electroencephalographic (EEG) patterns associated with motor imagery in untrained …
Improving multi-class motor imagery EEG classification using overlap** sliding window and deep learning model
Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems.
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …
Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs
In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on
Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the …
Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the …