Multivariate fast iterative filtering based automated system for grasp motor imagery identification using EEG signals
One of the most crucial use of hands in daily life is grasping. Sometimes people with
neuromuscular disorders become incapable of moving their hands. This article proposes a …
neuromuscular disorders become incapable of moving their hands. This article proposes a …
Use of mobile EEG in decoding hand movement speed and position
N Robinson, TWJ Chester… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With the majority of brain–computer interface (BCI) research currently restricted to the
controlled settings in labs, there is a growing interest to study the feasibility of BCI …
controlled settings in labs, there is a growing interest to study the feasibility of BCI …
[PDF][PDF] Transfer Learning and Deep Neural Networks for Robust Intersubject Hand Movement Detection from EEG Signals
In this research, five systems were developed to classify four distinct motor functions—
forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV) …
forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV) …
Improving single-hand open/close motor imagery classification by error-related potentials correction
Y Lei, D Wang, W Wang, H Qu, J Wang, B Shi - Heliyon, 2023 - cell.com
Objective The ability of a brain-computer interface (BCI) to classify brain activity in
electroencephalograms (EEG) during motor imagery (MI) tasks is an important performance …
electroencephalograms (EEG) during motor imagery (MI) tasks is an important performance …
Effect of hand grip actions on object recognition process: a machine learning-based approach for improved motor rehabilitation
Brain–computer interface (BCI) is the current trend in technology expansion as it provides an
easy interface between human brain and machine. The demand for BCI-based applications …
easy interface between human brain and machine. The demand for BCI-based applications …
Classification of hand movements of stroke patients using combination of statistical features and artificial neural network
The data used in this study is collected using EEG and the movements are extracted from a
stroke subject. In this research, a stroke patient's brainwave activity with left and right hand …
stroke subject. In this research, a stroke patient's brainwave activity with left and right hand …
Electroencephalographic spectral analysis to help detect depressive disorder
The increasing prevalence of depressive disorder (also known as major depressive disorder
or MDD), especially in the younger generations, has brought urgency upon the importance …
or MDD), especially in the younger generations, has brought urgency upon the importance …
Analyzing and Decoding Natural Reach and Grasp Action Using Convolutional Neural Network
Grasping is the most vital component of human activities. Decoding reach and grasp action
from electroencephalography (EEG) is of great importance for the recognition of innate and …
from electroencephalography (EEG) is of great importance for the recognition of innate and …
[PDF][PDF] An interpretative fuzzy rule-based eeg classification system for discrimination of hand motor attempts in stroke patients
Stroke patients have symptoms of cerebral functional disturbance that could aggressively
impair patient's physical mobility, such as freezing of hand movements. Although …
impair patient's physical mobility, such as freezing of hand movements. Although …
A Rule-Based EEG Classification System for Discrimination of Hand Motor Attempts in Stroke Patients
X Gu, Z Cao - arXiv preprint arXiv:2001.11278, 2020 - arxiv.org
Stroke patients have symptoms of cerebral functional disturbance that could aggressively
impair patient's physical mobility, such as freezing of hand movements. Although …
impair patient's physical mobility, such as freezing of hand movements. Although …