Recent trends in EEG based Motor Imagery Signal Analysis and Recognition: A comprehensive review.
The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …
RDLINet: A novel lightweight inception network for respiratory disease classification using lung sounds
Respiratory diseases are the world's third leading cause of mortality. Early detection is
critical in dealing with respiratory diseases, as it improves the effectiveness of intervention …
critical in dealing with respiratory diseases, as it improves the effectiveness of intervention …
DSCNN-CAU: deep-learning-based mental activity classification for IoT implementation toward portable BCI
Mental activity classification (MAC) based on electroencephalogram (EEG) is used in the
brain–computer interface (BCI) and neurofeedback applications. For this purpose, machine …
brain–computer interface (BCI) and neurofeedback applications. For this purpose, machine …
A novel melspectrogram snippet representation learning framework for severity detection of chronic obstructive pulmonary diseases
A chronic obstructive pulmonary disease (COPD) is a major public health concern across
the world. Since it is an incurable disease, early detection and accurate diagnosis are very …
the world. Since it is an incurable disease, early detection and accurate diagnosis are very …
Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection
This article summarizes a systematic review of the electroencephalography (EEG)-based
cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate …
cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate …
EEG temporal information-based 1-D convolutional neural network for motor imagery classification
C Chu, Q Xiao, L Chang, J Shen, N Zhang, Y Du… - Multimedia Tools and …, 2023 - Springer
Abstract Brain-Computer Interface (BCI) enables human beings to interact with the outside
world through brain intention. Human-computer interaction (HCI) based on …
world through brain intention. Human-computer interaction (HCI) based on …
Discriminatory features based on wavelet energy for effective analysis of electroencephalogram during mental tasks
Mental task categorization using single/limited channel (s) electroencephalogram (EEG)
signals is crucial for designing portable brain–computer interface and neurofeedback …
signals is crucial for designing portable brain–computer interface and neurofeedback …
BiCurNet: Pre-movement EEG based neural decoder for biceps curl trajectory estimation
Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is
essential for positive augmentation using wearable robots. However, surface EEG-based …
essential for positive augmentation using wearable robots. However, surface EEG-based …
State-of-the-art mental tasks classification based on electroencephalograms: a review
Electroencephalograms (EEGs) play an important role in analyzing different mental tasks
and neurological disorders. Hence, they are a critical component for designing various …
and neurological disorders. Hence, they are a critical component for designing various …
AsTFSONN: A unified framework based on time-frequency domain self-operational neural network for asthmatic lung sound classification
Asthma is one of the most severe chronic respiratory diseases which can be diagnosed
using several modalities, such as lung function test or spirometric measures, peak flow …
using several modalities, such as lung function test or spirometric measures, peak flow …