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Sensors 2017, 17(10), 2315; doi:10.3390/s17102315

Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload

State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
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Received: 1 September 2017 / Revised: 2 October 2017 / Accepted: 3 October 2017 / Published: 12 October 2017
(This article belongs to the Special Issue Novel Sensors for Bioimaging)
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Abstract

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. 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. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and 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. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life. View Full-Text
Keywords: mental workload; signal fusion; n-back task; mutual information; heterogeneous bio-signals mental workload; signal fusion; n-back task; mutual information; heterogeneous bio-signals
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, P.; Wang, X.; Chen, J.; You, W. Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload. Sensors 2017, 17, 2315.

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