This study demonstrates a VR-based motion-sickness platform that comprises a
32-channel EEG and 2-channel ECG system, and a joystick with a continuous scale,
where by subjects can continuously report their level of motion-sickness during
experiments. All measurements, including the level of motion-sickness and the motion
of the platform, were synchronized with EEG and ECG recordings. The VR-based
platform simultaneously provides both visual and vestibular stimuli to generate a most
realistic experimental environment for studying motion-sickness.
This study first attempted to correlate the HRV indices with the MS severity; both
were recorded continuously using an ECG and a slide-type switch during a simulated
driving experiment. Experiments were conducted on a motion driving simulator
comprising a VR-based tunnel driving environment and a real vehicle mounted on a 6
degree-of-freedom motion platform (Lin et al., 2005a; 2005b). The temporal
correlation between changes in the MS severity and the HRV indices was assessed
and modeled by non-linear regressive algorithms — adaptive neural fuzzy inference
system (ANFIS). Some subjects who had autonomic responses opposite to those of
the majority of subjects during MS exposure. These subjects (n=6 among a total of 29
69
subjects) likely failed to respond to VR stimulation. These subjects reported that they
felt sickness immediately after the onset of the experiment. Such early onset sickness
could be induced by the unfamiliar with the VR-scene. The subjects were further
excluded from EEG data analysis.
The recorded EEG signals are analyzed using independent component analysis
(ICA), time-frequency analysis, and time-series cross-correlation to investigate
MS-related brain dynamics in a continuous driving task. Independent EEG activities
and their equivalent dipole source locations were isolated by independent component
analysis (ICA) to obtain the involved brain circuits during motion-sickness. The ICA
signals were then correlated to the MS level to investigate the changes before, during
and after motion-sickness induce session. The temporal relationship between the
reported sickness-level and the involved brain circuits were then determined by
cross-correlation analysis.
By combining independent component analysis, time-frequency analysis and cross
correlation analysis, this work evaluates changes in the EEG power spectrum that
accompanies the fluctuations in the level of motion sickness in a realistic driving task.
ICA separates the multi-channel EEG signals into independent brain processes, each
of which represents electrical neurophysiological activities from a tight cluster of
neurons. Components with similar scalp tomography, dipole location and baseline
70
power spectrum from multiple subjects were grouped into component clusters. The
sorting and correlation of power spectra with subjective MS levels reveal a monotonic
relationship between minute-scale changes in MS and the EEG spectra of distinct
component clusters (brain processes) in different frequency bands. In summary, this (1)
utilized both visual and vestibular stimuli to induce realistic motion-sickness, (2)
proposed a continuous rating mechanism using which subjects can report their MS
level without interrupting the experiment, and (3) evaluated reproducible spectral
changes in multiple brain areas that accompany fluctuations in the severity of motion
sickness.
71
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