Assessment of Motion-Sickness

Another important factor in motion-sickness experiments has been the degree of

sickness of the participants. Many scholars have adopted a motion-sickness

questionnaire by Kennedy et al., (1993) to measure susceptibility of subjects to MS. It

is a standard rating system for comparing MS states among subjects. However, it

demands interrupting the experiments and asking the subjects to answer few questions.

This approach may not be practical for a continuous performance task, in which

subjects must perform the task continuously. For example, in a long-term driving

experiment in which the subject’s cognitive states are monitored, interrupting the

experiment for the questionnaire may arouse the subjects. Moreover, such

intervention may influence human physiology which makes it very difficult or even

possible to correlate the measured physiological signals with the motion-sickness

level. Therefore, an easy-to-operate online rating mechanism is sought to record

continuously the level of motion-sickness in subjects.

The focus of early motion-sickness studies was on the physiological changes

related to motion-sickness. For instance, the electrogrstrography (EGG) signals (Hu et

al., 1991; Cheung & Vaitkus, 1998) have been employed to detect symptoms of

motion-sickness, such as vomiting, and galvanic skin responses (GSR) have been

used to detect sweating. Holmes & Griffin (2001) observed increased heart rate

11

variability (HRV) during nausea, indicating the modulation of the automatic nervous

system (ANS) in motion-sickness. Rapid advances in neuroimaging technology have

enabled the neural correlates of motion-sickness to be examined.

Electroencephalography (EEG) is one of the best methods for monitoring the brain

dynamics induced by motion-sickness because of its high temporal resolution and

portability.

1.4.1 Previous EEG Studies

Wu (1992) showed that theta power increases in the frontal and central areas when

subjects were placed in a moving parallel swing device. Wood et al. (1991, 1994) also

found increased EEG theta wave in the frontal areas during motion-sickness induced

by a rotating drum. Chelen et al. (1993) adopted cross-coupled angular stimulation to

induce motion-sickness and found increased delta- and theta-band power during

sickness but no significant change in alpha power. Hu et al. (1999) investigated MS

triggered by the viewing of an optokinetic rotating drum and found a higher net

percentage increase in EEG power in the 0.5-4 Hz band at electrode sites C3 and C4

than in the baseline spectra. Kim et al. (2005) found increases in both delta and beta

power in the frontal and temporal areas in an object-finding VR experiment. Min et al.

(2004) also found increases in delta power in a car-driving VR experiment. However,

12

they also found that theta power declined as the degree of motion-sickness increased.

Motion-sickness-induced EEG power changes are not consistent among all of the

cited studies. One reason may be the wide range of paradigms used to induce

motion-sickness. Most of the above-mentioned experiments involved a single

modality using either visual (Hu et al., 1999; Kim et al., 2005; Lo & So, 2001; Min et

al., 2004) or vestibular inputs (Wood et al., 1991; Wood et al., 1994; Wu, 1992;

Chelen et al., 1993). This single-modality scheme may be unrealistic and suboptimal

for reliably inducing motion-sickness in subjects and, leading to inconsistent results

concerning changes in EEG power.

1.4.2 Previous HRV Studies

The MS symptoms are associated with perturbed sympathovagal activities (Xu et

al., 1993; Jang et al., 2002; Gianaros et al., 2003). Specifically, heart rate (HR)

increases in response to exposure to nauseogenic bodily motions (Cowing et al., 1986

and 1990) or to optokinetic stimulation (Hu et al., 1991; Uijtdehaage et al., 1993).

Power spectral analysis of electrocardiographic (ECG) (R–R) intervals is considered a

reliable and sensitive measurement of MS-induced sympathovagal perturbation in

humans (Stys & Stys, 1998). For instance, MS severity changes linearly with changes

in the power spectral density (PSD) of the R-R interval time series (Doweck et al.,

13

1997). The following three spectral components are identified in the spectrum of R-R

interval time series (Kuo et al., 1999; Bolanos et al., 2006): very low frequency (VLF)

(0.003 – 0.04 Hz), low frequency (LF) (0.04 – 0.15 Hz) and high frequency (HF)

(0.15 – 0.4 Hz) components. The power distribution and center frequency of LF and

HF components reflect the autonomic neural modulations of heartbeats. The LF

component of HRV is mediated by both sympathetic and parasympathetic activities

(Goichot et al., 2004; Chen et al., 2005; Casu et al., 2005) and the parasympathetic

activity is recognized as a major contributor to the HF component (Beckers et al.,

2006; Stauss, 2003; Emoto et al., 2007 ).

The underlying physiological mechanism of the VLF component remains unclear

and its reliability is controversial (Camm et al., 1996; Kato et al., 2004; Pipraiya et al.,

2005). The LF/HF ratio is typically considered to reflect sympathetic/parasympathetic

balance at cardiac rhythms (Franchi et al., 2001; Wodey et al., 2003; Demaree &

Everhart, 2004). Previous studies that examined the relationship between the degree

of MS and the automatic nervous system (ANS) typically compared averaged heart

rate variability (HRV) indices before and during experimental motion exposure over a

period (e.g., Hu et al., 1991; Uijtdehaage et al., 1993). Therefore, short-term or

transient pattern of autonomic control of HRV may obscure important information

(Morrow et al., 2000). Only one study correlated MS with temporal changes in HRV

14

(Gianaros et al., 2003) and demonstrated that HF power decreased as MS severity

increased. In previous studies, the severity of induced MS was assessed subjectively

and intermittently. For example, MS symptoms were verbally reported at 1-min

intervals (Holmes & Griffin, 2001; Young, 2003; Ziavra et al., 2003). Such

intervention can unexpectedly introduce room for subjects to temporally reduce their

MS to an undetermined extent and adversely influence the interrelationship between

MS and HRV estimates (Forstberg et al., 1998; Sang et al., 2003). Studies using a

high temporal resolution and with minimal measurement interventions are required to

accurately correlate HRV indices with MS severity.