Chapter 2 Design of Experiments
2.4 Data Acquisition
2.4.2 Data Collection Sensors
According to the symptoms of motion sickness discussed in subsection 2.1.2, some physiological signals can be considered as important indices related to the motion sickness.
For instance, the galvanic skin response (GSR or skin conductance response) and the Electrogastrography (EGG) are highly related to the symptoms of sweating and vomiting, respectively. The EGG and EKG are the most popular ones used in the studies of motion sickness. Some researches indicated that EEG and EKG are related to the onset of motion sickness directly [7,8]. In this study four different physiological signals including EEG, EGG, EKG and GSR were recorded simultaneously for further analysis.
2.4.2.1 Electroencephalogram (EEG)
An electrode cap is mounted on the subject’s head for signal acquisitions on the scalp.
The 10-20 International System of Electrode Placement standard to place the EEG electrodes proposed by Jasper in 1958 [10], was used in this study. An illustration of the 10-20 system is shown in Fig. 2-14.
Fig. 2-14. The 10-20 international electrode placement system [10].
The letters F, C, T, P, and O represent the frontal, central, temporal, parietal, and occipital cortical regions on the scalp, respectively. The term “10-20” means 10% and 20% of the total distance between specified skull locations. The percentage-based system allows differences in skull locations. A total of 32 electrodes are used in this system and the top view of the scalp is shown in Fig. 2-15.
Fig. 2-15. The 32 EEG electrodes locations.
The NuAmps, manufactured by NeuroScan Company, is a high-quality 40-channel digital EEG amplifier that is capable of 22 bit sampling at 1000 Hz, measuring signals from
DC to 260Hz. Table 2-1 shows that the specifications of the NuAmps. The 500 Hz sampling frequency was uesd in this study. Photos of the NuAmps amplifier and the electrode cap are shown in Fig. 2-16.
Table 2-1: NuAmps Specifications
NuAmps Specifications
Analog inputs 40 unipolar (bipolar derivations can be computed) Sampling frequencies 125, 250, 500, 1000 Hz per channel
Input Range ±130mV
Input Impedance Not less than 80 MOhm
Input noise 1 µV RMS (6 µV peak-to-peak)
Bandwidth 3dB down from DC to 262.5 Hz, dependent upon sampling frequency selected
Fig. 2-16. The electrode cap and the EEG signal amplifier.
2.4.2.2 Electrocardiogram (EKG)
An electrocardiogram (EKG) is one of the simplest and fastest procedures used to assess the condition of heart. Some researches have demonstrated that EKG is highly related to the symptoms of motion sickness [7]. Two electrodes (small, plastic patches) are placed at certain locations on subject's chest, the placement locations are shown in Fig.2-17. The heart electrical activity of subjects is simultaneous recorded with the EEG signal through the NuAmps amplifier with sampling rate 500 Hz.
Fig. 2-17. Locations of the EKG electrodes on the subject’s chest.
2.4.2.3 Electrogastrography (EGG)
The electrogastrography (EGG) is a non-invasive measurement of stomach activity using surface electrodes positioned over the abdominal surface. Over the past few decades, EGG has been used as an objective measure of nausea experienced in visually induced sickness.
EGG has been reported to be a successful indication for the symptoms of motion sickness.
The EGG signals are roughly sinusoidal and typically identified by its slow frequency and low amplitude (between 100 and 500 mV).
Fig. 2-18. Locations of the EGG electrodes.
Bipolar Ag-AgCl electrodes were commonly used because they provide the greatest signal-to-noise ratio in most subjects. The stomach electrical activity in this study is also measured by the NuAmps amplifier with sampling rate 500 Hz. The electrode locations are shown in Fig. 2-18 that were used in Holmes’s study [11]. They suggested that these locations may get largest possible amplitude, lowest artifacts from EKG and body movements.
2.4.2.4 Galvanic skin response (GSR)
The GSR100C sensor and amplifier manufactured by BIOPAC Systems Instruments was used to detect galvanic skin response in this research. The unit is specified to have a gain of 20, 10, 5, or 2 micro-mhos / volt, and a DC excitation voltage of 0.5 V. It contains a hardware low pass filter with an upper cutoff frequency of 1 Hz or 10 Hz to prevent aliasing. It also
contains a hardware high pass filter whit a lower cutoff frequency of DC, 0.05 Hz, or 0.5 Hz to prevent aliasing. The specification of the amplifier is given in Table 2-2.
The 6mm Ag-AgCl electrodes were applied with an electrode paste of 0.5 % saline in a neutral base. The electrodes are mounted on the subject’s finger tips are shown in Fig. 2-19 in typical applications. The polyurethane glue is used between the electrodes and skin to improve conductance. However, we placed the GSR electrodes on subjects’ back of neck in our experiments for two reasons: (1) the movements of subjects’ hand while controlling the steering wheel can induce unexpected artifacts; (2) the skin conductance response on the back of neck is more sensitive than that on the finger tips in our experiments. The GSR electrodes setup in the motion sickness experiments is shown in Fig. 2-20. The sampling rate was 30 Hz in our experiment.
Table 2-2: The Specification of GSR Amplifier.
GSR 100C Specifications
Gain 20, 10, 5, 2 micro-mhos/volt
Output Range ±10V (analog)
Low Pass Filter 1Hz, 10 Hz
High Pass Filter DC, 0.05 Hz, 0.5 Hz
Sensitivity 0.7 nano-mhos-with MP system
Excitation Vex=0.5VDC (constant voltage)
Gain Range (mu-mhos)
Fig. 2-19. Typical set up of galvanic skin response electrodes [48].
Fig. 2-20. Galvanic skin response electrodes setup in our experiments.
Chapter 3
Assessment of Motion Sickness Symptoms
The symptoms of motion sickness are investigated with the motion sickness questionnaire (MSQ) and multi-stream physiological signals in this chapter. The assessment of both subjective evaluation and physiological responses guarantees the objectivity of motion sickness. The results of subjective evaluation are given in section 3.1, and the results of physiological response are given in section 3.2. Finally, the results of the assessment of both subjective evaluation and physiological responses are given in section 3.3.
3.1 Subjective Evaluation of Motion Sickness: Motion Sickness Questionnaire (MSQ) Design
The famous motion sickness questionnaire (MSQ) developed by Kennedy et al. [12] in 1993 is a commonly used MSQ in the related research field of study. In this study, we also designed a motion sickness questionnaire (MSQ) according to several references for subjective evaluation of motion sickness. Our MSQ composes of 10 items, and each of which has six score levels (0-‘not at all’, 5-‘very much’). The total motion sickness score was the aggregate score of these 10 items. The full range of total motion sickness score was within 0-50 points. The MSQ we designed in this study is shown in Fig. 3-1.
The MSQ results and the information of the subjects in our experiment are summarized in Table 3-1. Two subjects (subject 2 and 4) terminated the experiments because of the severe symptoms of motion sickness. Two subjects (subject 6 and 8) claimed they did not feel any symptom of motion sickness. The MSQ score of subject 3 and 9 indicated that they were in a severe motion sickness. The MSQ score served as a subjective method to appraise various states of motion sickness (high or low) in the experiments.
Motion Sickness Questionnaire
Experiment Data: __________ Subject No.: __________
1. Do you feel motion sickness?
0 Not At All 1 2 3 4 5 Very Much
2. Are your eyes feeling tired and sore?
0 Not At All 1 2 3 4 5 Very Much
3. Can you focus on the object?
0 Not At All 1 2 3 4 5 Very Much
4. Do you have any pain in your head at the moment?
0 Not At All 1 2 3 4 5 Very Much
5. Are you feeling tired or sleepy?
0 Not At All 1 2 3 4 5 Very Much
6. Are you feeling dizzy when you eyes open?
0 Not At All 1 2 3 4 5 Very Much
7. Are you feeling dizzy when you eyes close?
0 Not At All 1 2 3 4 5 Very Much
8. Do you feel off-balance?
0 Not At All 1 2 3 4 5 Very Much
9. Do you feel vomitous?
0 Not At All 1 2 3 4 5 Very Much
10.Do you feel queasy in your stomach?
0 Not At All 1 2 3 4 5 Very Much
Fig. 3-1. Motion Sickness Questionnaire (MSQ) designed in this study.
Table 3-1: The results of MSQ
Subject Date(yy/mm/dd) Age Gender MSQ Score Subject 1 2005/03/25 22 Female 17/50
3.2 Physiological Responses induced by Motion Sickness
In this study, the EEG changes correlated to motion sickness will not only refer to the MSQ score, but the objective indices should also be involved for systematic evaluation. The physiological signals recorded for objective motion sickness assessment included electrocardiogram (EKG), electrogastrography (EGG) and galvanic skin response (GSR).
3.2.1 EKG changes during Motion Sickness
The electrical activity of the heart can be recorded at the surface of the subject’s chest using an electrocardiogram (EKG). The EKG is simply a voltmeter that uses up to two electrode wires with gel patches placed on designated areas of the body. The EKG dominant frequency of a normal adult is around 1 to 1.6 Hz in a relax situation. However, we can expect
that the EKG dominant frequency will increase in some special conditions, such as motion sickness. A typical spectral changes of EKG dominant frequency during our experiment are shown in Fig. 3-2.
Fig. 3-2. A typical spectral changes of EKG dominant frequency during our experiment (subject 1).
According to Fig. 3-2, in the “Baseline” session, the dominant frequency was 1.05 Hz with the magnitude of 60-dB, which means 63 cpm of heart rate (1.05 Hz * 60 s = 63 cpm).
The dominant frequency shifted to 1.3 Hz in the “Motion-Sickness” session, which means 78 cpm of heart rate (1.3 Hz * 60 s = 78 cpm). The results indicate that the subject’s heart rate
may change when they are in motion sickness. The variations of the EKG dominant frequency of all subjects during the three sessions: “Baseline”, “Motion-Sickness” and “Rest” in the experiments are summarized in Table 3-2 and Fig. 3-3.
Table 3-2: EKG dominant frequency (Hz) variations of different subjects in the three different sessions.
Subject Baseline Motion-Sickness Rest
Subject 1 1.05 1.3 1.1
* No distinguish able difference between the “Baseline” and “Motion-Sickness” sessions
Some EKG analysis results show no distinguish able difference between the “Baseline”
and “Motion-Sickness” sessions such as the results of subjects 6 and 8. For subjects 1, 3, 5, 7, 9 and 10, a 0.05 Hz to 0.2 Hz raise of EKG dominant frequency can be found in
“Motion-Sickness” sessions compared with the “Baseline” sessions. Some of these subjects could be proved that they could recover from the symptoms of motion sickness after the 10-min “Rest” session by the evidence that the EKG dominant frequency shifted back to the state as “Baseline” session.
Fig. 3-3. Variations of the EKG dominant frequency.
According to Fig. 3-3, EKG changes can be regarded as a good index of motion sickness for subjects 1, 3, 5, 9 and 10. However, EKG is easy to be influenced by many other factors according to some researches [36].
3.2.2 EGG Changes during Motion Sickness
Electrogastrography (EGG) is a method of recording stomach electrical activity from electrodes placed on the abdominal surface. The EGG signals are roughly sinusoidal and typically identified by its slow frequency and low amplitude (between 100 and 500 mV). It was reported that the myoelectrical activities originate from the pacemaker region located at the upper half of the gastric body and migrate aborally towards the duodenum through the gastric body and the antrum. EGG has been used as an objective measure of epigastric
EKG Dominant Freq. (Hz)
symptoms and nausea during motion sickness for more than 10 years [39]. EGG has been reported to be a successful indication for motion sickness by Hu et al. [13], Uijtdehaage et al.
[14] and Xu et al. [15]. In addition, it has been reported that EGG is a reliable measure of motion sickness [16].
The EGG-activity in a normal condition is 3 to 6 cpm of a healthy adult which means 0.05 Hz to 0.1 Hz. The increased activities of EGG will be induced by the symptoms of nausea or vomiting. On the rest time after motion sickness, subjective symptoms of motion sickness should subside, and then the severity of gastric symptoms will expected to be reduced over time as well. The variations of the EGG dominant frequency for different subjects in the three sessions: “Baseline”, “Motion-Sickness” and “Rest” are summarized in Table 3-3 and Fig. 3-4.
Table 3-3: EGG dominant frequency (Hz) variations of different subjects in the three different sessions.
Subject Baseline Motion-Sickness Rest
Subject 1 0.09 0.15 0.09
Fig. 3-4. Variations of the EGG dominant frequency.
Some EGG analysis results show distinguish able differences between the “Baseline”
and “Motion-Sickness” sessions, which indicate the increase gastric activities. The increase of gastric activities is sometimes the direct reason of gastralgia, and highly related to the symptoms of motion sickness. An evident shift of EGG dominant frequency is found in session “Motion-Sickness” of majority subjects. Some of these subjects could be proved that they could recover from the symptoms of motion sickness after the 10-min “Rest” session by the evidence that the EGG dominant frequency shifted back to the state as “Baseline” session.
According to Fig. 3-4, EGG changes can be regarded as a good index of motion sickness for most of the subjects.
3.2.3 GSR Changes during Motion Sickness
The galvanic skin response (GSR) is a simple, useful and reproducible method of capturing the autonomic nerve response as a parameter of the sweat gland function [49]. It is the conductance of the skin to the passage of a very small electric current. Physically GSR is a
EGG Dominant Freq. (Hz)
change in the conductance of the skin in response to different kinds of stimuli. Any stimulus capable of an arousal effect can evoke the response of GSR changes and the amplitude of the response is more dependent on the surprise effect of the stimulus than on the physical stimulus strength [50]. It has been known for decades that the magnitude of this electrical conductance is affected, not only by the subject's general mood, but also by immediate emotional reactions [51]. In measurements changes in the voltage measured from the surface of the skin are recorded.
The GSR or skin conductance will increase when the subject is sweating. Sweating is one of the important symptoms of motion sickness, so one can expect some distinguish changes in the GSR signal. A typical changes of GSR during our experiments are given in Fig.3-5.
Fig. 3-5. The GSR of subject 7.
A 10-second moving average filter was applied to the recoded GSR signals. The blue line in Fig. 3-5 is in the duration of “Baseline” session (i.e. straight road), the red line is the
“Motion-Sickness” session (i.e. consecutive-curve road), and the green line represent the
“Rest” session (i.e. straight road). Firstly, the skin conductance is about 4 μ-mho in the
“Baseline”, and then becomes about 11μ-mho in the “Motion-Sickness”. Finally, the skin conductance is 8μ-mho in the “Rest” session.
However, similar to Min’s study [8] GSR signals of majority subjects have no distinguish able changes during the experiments, as shown in Fig. 3-6. However, the individual difference between subjects is large, and the signals can only to used for the subjects who sweat during motion sickness. GSR changes can be regarded as a good index of motion sickness only for subject 7 and 10. The GSR changes of subject 7 is shown in Fig. 3-5. The analysis of the GSR signals is that a moving average filter in time domain is used for analysis of motion sickness.
So, it is an instantaneous physiological index for the real-time experimental monitoring.
Fig. 3-6. The GSR of subject 1.
3.3 Assessment of Motion Sickness Symptoms with Subjective Evaluation and Physiological Responses
The MSQ has served as a subjective index of motion sickness in many previous researches. However, it is an arbitrary assessment method which can be too subjective for scientific studies. Both subjective and objective evaluations are used in our study for the systematic motion sickness estimation. Table 3-4 is the performance comparison of different motion sickness indices we investigate in this study.
Table 3-4: Variation performance comparison of different motion sickness indices.
Dominant Frequency Subject
MSQ
Score EKG EGG
GSR
Subject 1 17/50 1.05 (Hz) → 1.3 (Hz) 0.09 (Hz) → 0.15 (Hz) X Subject 3 28/50 1.1 (Hz) → 1.2 (Hz) 0.09 (Hz) → 0.15 (Hz) X Subject 5 11/50 1.3 (Hz) → 1.5 (Hz) 0.09 (Hz) → 0.15 (Hz) X Subject 6 08/50 0.9 (Hz) → 0.9 (Hz) 0.09 (Hz) → 0.12 (Hz) X Subject 7 17/50 1.2 (Hz) → 1.3 (Hz) 0.09 (Hz) → 0.12 (Hz) O Subject 8 05/50 1.4 (Hz) → 1.4 (Hz) 0.09 (Hz) → 0.09 (Hz) X Subject 9 34/50 1.1 (Hz) → 1.25 (Hz) 0.06 (Hz) → 0.12 (Hz) X Subject 10 12/50 1.2 (Hz) → 1.4 (Hz) 0.09 (Hz) → 0.15 (Hz) O
According to Table 3-4, for subject 1, 17 points of MSQ score refers to mild sickness in the subjective assessment. The EKG dominant frequency shifted from 1.05 Hz to 1.3 Hz, and the EGG dominant frequency shifted from 0.09 Hz to 0.15 Hz in “Motion-Sickness” session.
Although there is no able distinguish change in the GSR response, subject 1 was selected for the further EEG signals analysis.
Fig. 3-7. The comparison results of both subjective evaluation and physiological responses.
The MSQ scores of subject 3 and subject 9 are 28 and 34, respectively, which indicate severe sickness in the subjective assessment. By contrast, the MSQ score of subject 6 and subject 8 are only 8 and 5, respectively. The MSQ score lower than 10 ponits in our experiment is considered as indistinct-sickness in the subjective assessment. For objective
indices, EKG, EGG and GSR, if more than two of the three indices have significant changes, the subject is considered as a subject with motion sickness in the experiment. The normalized scores of subjective and objective indices of different subjects are shown in Fig. 3-7. The EKG and EGG scores show the normalized signal differences between the “Baseline session”
and the “Motion-Sickness session” for different subjects.
The three curves in Fig. 3-7 are highly related to each other. It shows strong evidence to the validity of the indices such that we can study the EEG changes related to motion sickness base on these results. More details are discussed in the next chapter.
Chapter 4
Analysis Results of EEG Response during Motion Sickness
The EEG data analysis is based on the cross-demonstrations of subjective evaluation and physiological responses to ensure the objectivity of sickness assessment. The EEG signals analysis procedure is given in section 4.1, and the relationship between the power spectrum of ICA components and motion sickness is given in section 4.2. Finally, the results of spectrum ICA component dynamic changes are given in section 4.3.
4.1 EEG Signal Analysis Procedure
Flowchart of the EEG signal processing procedure is show in Fig. 4-1. The 32-ch EEG data was collected through an electrode cap and amplified by the NuAmps. The sampling rate of the EEG data is 500Hz. It consists of artifacts removal, independent component analysis, useless component rejection, spectral analysis, nausea-related components selection and dynamic spectral analysis.
A 500-pt high pass filter with a cut-off frequency at 1 Hz is used to remove breathing artifacts. The width of the transition band of the high pass filter is 0.2 Hz. A 30-pt low pass filter is then applied to the signal with the cut-off frequency at 50 Hz to remove muscle artifacts and line noise. The transition band width of the low pass filter is 7 Hz. The independent component analysis (ICA) is applied to the filtered EEG signals to obtain the independent components. Some artifacts can be rejected in the process of useless components rejection. The effectiveness of eye blinking and other artifacts removal by using ICA had been
demonstrated in the Jung et al.’s study [17]. The spectral analysis is then applied to the useful independent components to calculate their frequency response. The detailed introduction of the algorithm is given in subsection 4.1.1. The feature of the motion sickness symptoms can be evaluated by the selection of nausea-related components. And finally, the continuous frequency responses of the ICA components are evaluated with dynamic spectral analysis.
Fig. 4-1. Flowchart of the EEG signal analysis procedure.
4.1.1 Independent Component Analysis
The joint problems of EEG source segregation, identification, and localization are very difficult since the EEG data collected from any point on the human scalp includes activity generated within a large brain area, and thus, problem of determining brain electrical sources from potential patterns recorded on the scalp surface is mathematically underdetermined [19].
Although the resistivities between the skull and brain are different, the spatial smearing of EEG data by volume conduction does not involve significant time delay and suggests that the ICA algorithm is suitable for performing blind source separation on EEG data by source identification from that of source localization. We attempt to completely separate the twin problems of source identification and source localization by using a generally applicable ICA.
Thus, the artifacts including the eye-movement (EOG), eye-blinking, heart-beating (EKG), muscle-movement (EMG), and line noises can be successfully separated from EEG activities.
Thus, the artifacts including the eye-movement (EOG), eye-blinking, heart-beating (EKG), muscle-movement (EMG), and line noises can be successfully separated from EEG activities.