[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …
human–system/robot interactions have been actively explored, especially in brain robotics …
Deep learning on computerized analysis of chronic obstructive pulmonary disease
Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the
world. Because COPD is an incurable disease and requires considerable time to be …
world. Because COPD is an incurable disease and requires considerable time to be …
EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation
Stroke is the second foremost cause of death worldwide and is one of the most common
causes of disability. Several approaches have been proposed to manage stroke patient …
causes of disability. Several approaches have been proposed to manage stroke patient …
EMG hand gesture classification using handcrafted and deep features
JM Fajardo, O Gomez, F Prieto - Biomedical Signal Processing and Control, 2021 - Elsevier
Currently, electromyographic (EMG) signal gesture recognition is performed with devices of
many channels. Each channel gives a signal that must be filtered and processed, which …
many channels. Each channel gives a signal that must be filtered and processed, which …
Human knee abnormality detection from imbalanced sEMG data
The classification of imbalanced datasets, especially in medicine, is a major problem in data
mining. Such a problem is evident in analyzing normal and abnormal subjects about knee …
mining. Such a problem is evident in analyzing normal and abnormal subjects about knee …
Discrete wavelet transform based data representation in deep neural network for gait abnormality detection
J Chakraborty, A Nandy - Biomedical Signal Processing and Control, 2020 - Elsevier
Detection of abnormal gait patterns using wearable sensors remains a major challenge in
clinical gait analysis and rehabilitation field. Despite the success of recent researches using …
clinical gait analysis and rehabilitation field. Despite the success of recent researches using …
A motor imagery EEG feature extraction method based on energy principal component analysis and deep belief networks
L Cheng, D Li, G Yu, Z Zhang, X Li, S Yu - IEEE Access, 2020 - ieeexplore.ieee.org
The motor imagery electroencephalography (MI-EEG) reflects the subjective motor intention,
which has received increasing attention in rehabilitation. How to extract the features of MI …
which has received increasing attention in rehabilitation. How to extract the features of MI …
Deep learning with ConvNet predicts imagery tasks through EEG
Deep learning with convolutional neural networks (ConvNets) has dramatically improved the
learning capabilities of computer vision applications just through considering raw data …
learning capabilities of computer vision applications just through considering raw data …
Noninvasive Brain Imaging and Stimulation in Post-Stroke Motor Rehabilitation: A Review
H Chang, Y Sheng, J Liu, H Yang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
This article provides a comprehensive review of the current state of noninvasive brain
imaging and brain stimulation in motor rehabilitation after stroke. The functional organization …
imaging and brain stimulation in motor rehabilitation after stroke. The functional organization …
Identification of post-stroke EEG signal using wavelet and convolutional neural networks
EC Djamal, RI Ramadhan, MI Mandasari… - Bulletin of Electrical …, 2020 - beei.org
Post-stroke patients need ongoing rehabilitation to restore dysfunction caused by an attack
so that a monitoring device is required. EEG signals reflect electrical activity in the brain …
so that a monitoring device is required. EEG signals reflect electrical activity in the brain …