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 …
Feature wise normalization: An effective way of normalizing data
This paper presents a novel Feature Wise Normalization approach for the effective
normalization of data. In this approach, each feature is normalized independently with one of …
normalization of data. In this approach, each feature is normalized independently with one of …
Brain-computer interface speller system for alternative communication: a review
Brain-computer interface (BCI) speller is a system that provides an alternative
communication for the disable people. The brain wave is translated into machine command …
communication for the disable people. The brain wave is translated into machine command …
Sign language translation using deep convolutional neural networks
Sign language is a natural, visually oriented and non-verbal communication channel
between people that facilitates communication through facial/bodily expressions, postures …
between people that facilitates communication through facial/bodily expressions, postures …
Enhancing P300 detection using a band-selective filter bank for a visual P300 speller
Background: An open challenge of P300-based BCI systems focuses on recognizing ERP
signals using a reduced number of trials with enough classification rate. Methods: Three …
signals using a reduced number of trials with enough classification rate. Methods: Three …
MsCNN: A deep learning framework for P300-based brain–computer interface speller
In this paper, a novel multiscale convolutional neural network (MsCNN) architecture is
proposed for P300 based BCI speller. Major limitation of BCI system is that it requires a large …
proposed for P300 based BCI speller. Major limitation of BCI system is that it requires a large …
A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm
A Rana, P Vaidya, G Gupta - Materials Today: Proceedings, 2022 - Elsevier
Quantum ML is a very fast-growing area of research with much theoretical variety &
applications. For picture identification, machine learning algorithms learn a desired input …
applications. For picture identification, machine learning algorithms learn a desired input …
The hybrid deep learning model for identification of attention-deficit/hyperactivity disorder using EEG
Common misbehavior among children that prevents them from paying attention to tasks and
interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder …
interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder …
How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review
J Kalra, P Mittal, N Mittal, A Arora… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention
in recent years due to their ability to help patients with disability using BCI-controlled …
in recent years due to their ability to help patients with disability using BCI-controlled …
An efficient deep learning framework for P300 evoked related potential detection in EEG signal
Background Incorporating the time-frequency localization properties of Gabor transform
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …