PMID- 29023364
OWN - NLM
STAT- MEDLINE
DCOM- 20180601
LR  - 20181202
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 17
IP  - 10
DP  - 2017 Oct 12
TI  - Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous 
      Bio-Signal Fusion to Estimate Mental Workload.
LID - 10.3390/s17102315 [doi]
LID - 2315
AB  - 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.
FAU - Zhang, Pengbo
AU  - Zhang P
AD  - State Key Laboratory of Precision Measurement Technology and Instruments, 
      Department of Precision Instrument, Tsinghua University, Beijing 100084, China. 
      zpb14@mails.tsinghua.edu.cn.
FAU - Wang, Xue
AU  - Wang X
AD  - State Key Laboratory of Precision Measurement Technology and Instruments, 
      Department of Precision Instrument, Tsinghua University, Beijing 100084, China. 
      wangxue@mail.tsinghua.edu.cn.
FAU - Chen, Junfeng
AU  - Chen J
AD  - State Key Laboratory of Precision Measurement Technology and Instruments, 
      Department of Precision Instrument, Tsinghua University, Beijing 100084, China. 
      chenjf17@mails.tsinghua.edu.cn.
FAU - You, Wei
AU  - You W
AD  - State Key Laboratory of Precision Measurement Technology and Instruments, 
      Department of Precision Instrument, Tsinghua University, Beijing 100084, China. 
      youw16@mails.tsinghua.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20171012
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Electrocardiography
MH  - Electroencephalography
MH  - Humans
MH  - *Models, Biological
MH  - Workload/*classification/*psychology
PMC - PMC5677372
OTO - NOTNLM
OT  - heterogeneous bio-signals
OT  - mental workload
OT  - mutual information
OT  - n-back task
OT  - signal fusion
COIS- The authors declare no conflict of interest.
EDAT- 2017/10/13 06:00
MHDA- 2018/06/02 06:00
PMCR- 2017/10/01
CRDT- 2017/10/13 06:00
PHST- 2017/09/01 00:00 [received]
PHST- 2017/10/02 00:00 [revised]
PHST- 2017/10/03 00:00 [accepted]
PHST- 2017/10/13 06:00 [entrez]
PHST- 2017/10/13 06:00 [pubmed]
PHST- 2018/06/02 06:00 [medline]
PHST- 2017/10/01 00:00 [pmc-release]
AID - s17102315 [pii]
AID - sensors-17-02315 [pii]
AID - 10.3390/s17102315 [doi]
PST - epublish
SO  - Sensors (Basel). 2017 Oct 12;17(10):2315. doi: 10.3390/s17102315.