Exploring Differential Entropy and Multifractal Cumulants for EEG-based Mental Workload Recognition

In the current research, two nonlinear features were utilized for the design of EEG-based mental workload recognition: one feature based on differential entropy and the other feature based on multifractal cumulants. Clean EEGs recorded from 36 healthy volunteers in both resting and task states were subjected to feature extraction via differential entropy and multifractal cumulants. Then, these nonlinear features were utilized as input for a fuzzy KNN classifier. Experimental results showed that the multifractal cumulants feature vector achieved an AUC of 0.951, which is larger than the differential entropy feature vector (AUC = 0.935). However, the combination of both feature sets resulted in added value in identifying these two mental workloads (AUC = 0.993). Furthermore, the multifractal cumulants feature vector (best classification accuracy = 94.76%) obtained better classification results than the differential entropy feature vector (best classification accuracy = 92.61%). However, the combination of these two feature vectors achieved the best classification results: accuracy of 96.52%, sensitivity of 97.68%, specificity of 95.58%, and F1-score of 96.61%. This shows that these two feature vectors are complementary in identifying different mental workloads.

@article{Lu2024,
  title     = {Exploring Differential Entropy and Multifractal Cumulants for EEG-based Mental Workload Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {5},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yan Lu},
  doi       = {10.14569/IJACSA.2024.0150515},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150515}
}