Abstract
Workload in the human brain can be a useful marker of internal brain state. However, due to technical limitations, previous workload studies have been unable to record brain activity via conventional electroencephalography (EEG) and magnetoencephalography (MEG) devices in mobile participants. In this study, we used a wearable EEG system to estimate workload while participants walked in a naturalistic environment. Specifically, we used the auditory steady-state response (ASSR) which is an oscillatory brain activity evoked by repetitive auditory stimuli, as an estimation index of workload. Participants performed three types of N-back tasks, which were expected to command different workloads, while walking at a constant speed. We used a binaural 500 Hz pure tone with amplitude modulation at 40 Hz to evoke the ASSR. We found that the phase-locking index (PLI) of ASSR activity was significantly correlated with the degree of task difficulty, even for EEG data from few electrodes. Thus, ASSR appears to be an effective indicator of workload during walking in an ecologically valid environment.
Highlights
The workload of the human brain changes constantly throughout the day according to internal brain state
Subsequent multiple comparison tests revealed a significant difference in the reaction times for different tasks, such that the NL task was associated with a faster reaction time than the 1- and 2-back tasks (t(14) = 6.50; p < 0.0001, t(14) = 8.25; p < 0.0001), and the 1-back task was associated with a faster reaction time than the 2-back task (t(14) = 5.80; p < 0.0001)
Subsequent multiple comparison tests revealed a significant difference in the accuracy for the different tasks, such that the NL task was associated with greater accuracy than the 1- and 2-back tasks (Z = 3.41; p < 0.0001, Z = 3.41; p < 0.0001), and the 1-back task was associated with greater accuracy than the 2-back tasks (Z = 2.78; p < 0.01)
Summary
The workload of the human brain changes constantly throughout the day according to internal brain state. Many previous studies used conventional electroencephalography (EEG) or magnetoencephalography (MEG) devices that are not suitable (i.e., too large) for measuring brain activity in ecologically valid environments. Debener et al (2012) and De Vos et al (2014) were able to detect the P300 caused by odd-ball tasks without complex denoising methods during walking Artifacts such as muscle potentials and oscillatory noises caused by walking can contaminate EEG data. We developed a novel method for estimating brain workload from a small number of data channels recorded via a portable EEG system worn while walking in a naturalistic environment. We hypothesized that the ASSR changes according to the difficulty of a corresponding task
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