Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques
Recognition of mental workload is crucial to improve the safety of humanmachine system. A simulated process control task was used to elicit different levels of mental workload. Mental workload was assessed by using multichannel EEG signals. Locally linear embedding was used to reduce the dimensionality of EEG power features. Support vector clustering and support vector data description methods were combined to classify EEG data.
mental workload locally linear embedding support vector clustering support vector data description operator functional state