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
VA Maksimenko, SA Kurkin, EN Pitsik, VY Musatov… - …, 2018 - Wiley Online Library
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 overlapping 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 …
Progress in EEG‐Based Brain Robot Interaction Systems
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the
electroencephalogram‐(EEG‐) based Brain Computer Interface (BCI), to serve as an …
electroencephalogram‐(EEG‐) based Brain Computer Interface (BCI), to serve as an …