![]() Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. ![]() ![]() Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Many people suffer from high mental workload which may threaten human health and cause serious accidents. ![]()
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