Multivariate synchrosqueezing transform and time-frequency attention for mental workload classification from EEG signals

Mental workload (MWL) indicates cognitive effort during a task and is a key marker of mental state. Understanding MWL is vital in neuroergonomics, cognitive neuroscience, human-machine interaction, and intelligent systems. This paper introduces a new multi-stage approach for classifying mental workload using EEG signals. The method involves four key steps: preprocessing the signals to eliminate noise; applying the multivariate synchrosqueezing transform (MSST) for precise time-frequency analysis; extracting deep features with a new convolutional neural network (CNN) architecture that includes a time-frequency attention module (CNN-TFAN); and finally, reducing feature dimensionality for classification. The approach was tested on two public datasets, STEW and MAT. Results indicated that the optimized combination of semisupervised discriminant analysis (SDA) for feature reduction and support vector machine (SVM) for classification yielded the best results, with accuracy rates of 97.1% on STEW and 98.6% on MAT. Additional analysis revealed that MSST outperformed other time-frequency methods and that the deeper, attention-enhanced network architecture significantly improved classification accuracy. These findings demonstrate the effectiveness and robustness of the proposed framework for mental workload analysis.