Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface

Cognitive workload is one of the widely invoked human factors in the areas of human–machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In t...

Full description

Bibliographic Details
Main Authors: Umer Asgher, Khurram Khalil, Muhammad Jawad Khan, Riaz Ahmad, Shahid Ikramullah Butt, Yasar Ayaz, Noman Naseer, Salman Nazir
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-06-01
Series:Frontiers in Neuroscience
Subjects:
convolutional neural network
long short-term memory
functional near-infrared spectroscopy
mental workload
brain–computer interface
deep neural networks
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2020.00584/full

Internet

https://www.frontiersin.org/article/10.3389/fnins.2020.00584/full

Similar Items