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 …

EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges

N Padfield, J Zabalza, H Zhao, V Masero, J Ren - Sensors, 2019 - mdpi.com
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
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 …

A deep learning scheme for motor imagery classification based on restricted Boltzmann machines

N Lu, T Li, X Ren, H Miao - IEEE transactions on neural …, 2016 - ieeexplore.ieee.org
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 …

A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface

A Singh, AA Hussain, S Lal, HW Guesgen - Sensors, 2021 - mdpi.com
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 …

[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 …

Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework

MT Sadiq, MZ Aziz, A Almogren, A Yousaf… - Computers in Biology …, 2022 - Elsevier
Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to
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 …

Improving multi-class motor imagery EEG classification using overlapping sliding window and deep learning model

J Hwang, S Park, J Chi - Electronics, 2023 - mdpi.com
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 …

Progress in EEG‐Based Brain Robot Interaction Systems

X Mao, M Li, W Li, L Niu, B Xian… - Computational …, 2017 - Wiley Online Library
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the
electroencephalogram‐(EEG‐) based Brain Computer Interface (BCI), to serve as an …