Monitored Versus Non-Monitored Stimuli in Brain-Computer-Interface Methods for Classifying Workload States During Piloting Tasks
Applying wireless sensors for measurement of pilot mental workload using electroencephalography (EEG) in passive brain-computer interfaces (pBCI) has proven difficult due to weak workload signatures in artifact-laden environments. While active monitoring of task-irrelevant stimuli is used with pBCI...
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Published in | 2023 IEEE Sensors Applications Symposium (SAS) pp. 1 - 6 |
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Format | Conference Proceeding |
Language | English |
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18.07.2023
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Abstract | Applying wireless sensors for measurement of pilot mental workload using electroencephalography (EEG) in passive brain-computer interfaces (pBCI) has proven difficult due to weak workload signatures in artifact-laden environments. While active monitoring of task-irrelevant stimuli is used with pBCI designs to improve the signal to noise ratio (SNR), this method introduces non-piloting tasks, and thus adds to pilot workload. A fully implicit non-monitoring approach, where stimuli are presented but ignored decreases superfluous artifacts associated with monitoring and responding to the stimuli, but also paradoxically weakens the workload signal of interest. We compared the misclassification rates for a variety of pBCI paradigms in monitoring versus non-monitoring conditions where auditory stimuli were used in the classification of moderate versus high workload conditions. The pBCI models incorporated spectral features of the EEG recorded wirelessly during a virtual reality flight simulation with non-pilots. Classification of workload states, using an open source BCI toolbox, found similar misclassification rates between the monitoring versus non-monitoring datasets (Bayes Factors between 2.1 and 2.9 showed there was no effect of group on classification errors). The results show that reasonable measurement of adjacent workload states (moderate versus high) can be undertaken using EEG data from wireless sensors using non-monitoring pBCI methods. |
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AbstractList | Applying wireless sensors for measurement of pilot mental workload using electroencephalography (EEG) in passive brain-computer interfaces (pBCI) has proven difficult due to weak workload signatures in artifact-laden environments. While active monitoring of task-irrelevant stimuli is used with pBCI designs to improve the signal to noise ratio (SNR), this method introduces non-piloting tasks, and thus adds to pilot workload. A fully implicit non-monitoring approach, where stimuli are presented but ignored decreases superfluous artifacts associated with monitoring and responding to the stimuli, but also paradoxically weakens the workload signal of interest. We compared the misclassification rates for a variety of pBCI paradigms in monitoring versus non-monitoring conditions where auditory stimuli were used in the classification of moderate versus high workload conditions. The pBCI models incorporated spectral features of the EEG recorded wirelessly during a virtual reality flight simulation with non-pilots. Classification of workload states, using an open source BCI toolbox, found similar misclassification rates between the monitoring versus non-monitoring datasets (Bayes Factors between 2.1 and 2.9 showed there was no effect of group on classification errors). The results show that reasonable measurement of adjacent workload states (moderate versus high) can be undertaken using EEG data from wireless sensors using non-monitoring pBCI methods. |
Author | Van Benthem, Kathleen Herdman, Chris Gard, Stefanie |
Author_xml | – sequence: 1 givenname: Kathleen surname: Van Benthem fullname: Van Benthem, Kathleen email: kathy.vanbenthem@carleton.ca organization: Institute of Cognitive Science, Carleton University,Ottawa,Canada – sequence: 2 givenname: Stefanie surname: Gard fullname: Gard, Stefanie email: stefanie.gard@alumni.carleton.ca organization: Carleton University,Department of Psychology,Ottawa,Canada – sequence: 3 givenname: Chris surname: Herdman fullname: Herdman, Chris email: chris.herdman@carleton.ca organization: Institute of Cognitive Science, Carleton University,Ottawa,Canada |
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Snippet | Applying wireless sensors for measurement of pilot mental workload using electroencephalography (EEG) in passive brain-computer interfaces (pBCI) has proven... |
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SubjectTerms | aviation brain-computer interface Electroencephalography mental workload measurement Solid modeling Task analysis Time measurement Virtual reality Wireless communication Wireless sensor networks wireless sensors |
Title | Monitored Versus Non-Monitored Stimuli in Brain-Computer-Interface Methods for Classifying Workload States During Piloting Tasks |
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