Year: 2025
Type: article
Abstract: This study presents a hybrid deep learning model for classifying mental workload (MWL) using electroencephalography (EEG) data.Leveraging the publicly available STEW dataset, the model integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network to capture temporal dynamics.Through a 5-fold cross-validation methodology, the system successfully classifies EEG signals into four distinct workload levels, achieving an average accuracy of 70.77% and a macroaveraged F1-score of 70.81%.The model demonstrates particular effectiveness in identifying extreme workload states (low and high), underscoring its potential for deployment in adaptive cognitive monitoring systems designed to enhance human performance and safety.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4413132435