Cognitive load detection using circulant singular spectrum analysis and Binary Harris Hawks Optimization based feature selection

J Yedukondalu, LD Sharma - Biomedical Signal Processing and Control, 2023 - Elsevier
Cognitive load detection during the mental assignment of neural activity is necessary
because it helps to understand the brain's response to stimuli. An electroencephalogram …

Efficient detection of myocardial infarction from single lead ECG signal

B Fatimah, P Singh, A Singhal, D Pramanick… - … Signal Processing and …, 2021 - Elsevier
Myocardial infarction (MI) is a heart condition arising due to partial or complete blockage of
blood flow to heart muscles. This can lead to permanent damage to the heart and can be …

Automated classification of mental arithmetic tasks using recurrent neural network and entropy features obtained from multi-channel EEG signals

A Varshney, SK Ghosh, S Padhy, RK Tripathy… - Electronics, 2021 - mdpi.com
The automated classification of cognitive workload tasks based on the analysis of multi-
channel EEG signals is vital for human–computer interface (HCI) applications. In this paper …

Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

LD Sharma, H Chhabra, U Chauhan… - International Journal of …, 2021 - Springer
Cognitive load recognition during mental arithmetic activity facilitates to observe and identify
the brain's response towards stress stimulus. As a result, an efficient mental load …

Graph signal processing based cross-subject mental task classification using multi-channel EEG signals

P Mathur, VK Chakka - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in
designing various brain-computer interface (BCI) applications. Most of the current …

CIS feature selection based dynamic ensemble selection model for human stress detection from EEG signals

L Malviya, S Mal - Cluster Computing, 2023 - Springer
Stress has an impact not only on a person's physical health but also on his or her ability to
perform at work, passion, and attitude in day-to-day life. It is one of the most difficult …

Stress classification by multimodal physiological signals using variational mode decomposition and machine learning

N Salankar, D Koundal… - Journal of healthcare …, 2021 - Wiley Online Library
In this pandemic situation, importance and awareness about mental health are getting more
attention. Stress recognition from multimodal sensor based physiological signals such as …

Automated attention deficit classification system from multimodal physiological signals

N Salankar, D Koundal, C Chakraborty… - Multimedia Tools and …, 2023 - Springer
Lack of attention, if it could not be taken care of and persists for a long time then may lead to
a severe issue. Analysis of Electroencephalogram (EEG) signals can effectively measure …

EEG based stress classification by using difference plots of variational modes and machine learning

N Salankar, SM Qaisar - Journal of Ambient Intelligence and Humanized …, 2023 - Springer
The recent trend in healthcare is to use the automated biomedical signals processing for an
augmented and precise diagnosis. In this context, an original approach is presented for …

Cognitive load detection using Binary salp swarm algorithm for feature selection

J Yedukondalu, LD Sharma - 2022 IEEE 6th Conference on …, 2022 - ieeexplore.ieee.org
Analyzing the brain's reaction to stimuli requires the detection of cognitive load during the
mental assignment of neuronal activity. It is possible to determine the cognitive load …