Chapter 4 Analysis Results of EEG Response during Motion Sickness … 41
4.3 Spectral Dynamic Changes of ICA Components
According to the results discussed in subsection 4.2, the ICA power spectrum suppressed at some specific frequency band when the subject feels nausea. The dynamic changes of the ICA spectrum along the time course is discussed in this subsection. The relationship between the spectral ICA components and the transition of the road-type (straight / curve) will also be investigated.
Fig. 4-7. Subject 10’s spectral dynamic variations of ICA component 13 (left parietal lobe) at 23 Hz.
The continuous changes of ICA component spectrum at 23 Hz in the region of the left central parietal lobe of subject 10 is shown in Fig.4-7. The blue, red, and green line in Fig. 4-7
represents the power spectrums in the “Baseline”, “Motion-Sickness”, and “Rest” sessions, respectively. The x-axis here represents the time steps, and the y-axis is the magnitude of ICA spectrum in dB. A significant drop of spectral magnitude of 23 Hz can be found at the transition point between the “Baseline” session and the “Motion-Sickness” session. The spectral magnitude rises again after the subject drived in the “Rest” session. The phenomenons of other subject is given in Fig. 4-8.
Fig. 4-8. Subject 1’s spectral dynamic variations of ICA component 17 (right parietal lobe) at 18 Hz.
The experimental results shown in this subsection demonstrates that the correlation between the feeling of motion sickness and the spectral suppression of ICA components is high. In addition, the EEG signal variations induced by motion sickness can be monitored by using analyzing the spectral dynamic changes of ICA components change immediately with the variations of road-type.
Chapter 5 Discussions
Some specific phenomena of motion sickness in the physiological signals are discovered in chapter 3 and 4. We are going to further emphasize the discussion of cross-subject specific phenomena in this chapter. The discussion of motion sickness influence region on human cortex is given in section 5.1, and the discussion of power spectrum suppression induced by motion sickness is given in section 5.2.
5.1 Motion Sickness Influence Region on Human Cortex
The influence regions of motion sickness on human cortex are discussed in this section.
Three different phenomena are found in this study including the 20Hz suppression in left and right parietal region, the 10 Hz suppression in left and right parietal region and of 10/20 Hz power suppression in the central parietal region.
5.1.1 The 20 Hz power suppression in left and right parietal region
The spatial distributions on scalp topographies of weighting matrices for dominant ICA components of subject 1 are shown in Fig. 5-1. It indicates the 20 Hz power suppression components occurred with symmetry. In the other word, the 20-Hz suppression phenomena occur in both left and right parietal region at the same time. The curve of 20 Hz power activity suppression is given in Fig. 5-2.
The peak near 20 Hz in “Baseline” session was suppressed in “Motion-Sickness” session obviously. The phenomenon is obviously, and is considered as an important discovery in our study.
(a) Component in the left parietal lobe. (b) Component in the right parietal lobe.
Fig. 5-1. The 20 Hz power activity suppression of subject 1 in two different ICA components.
Fig. 5-2. The 20 Hz power suppressions in the side parietal lobe.
5.1.2 The 10 Hz power suppression in left and right parietal region
The spatial distributions on scalp topographies of weighting matrices for dominant ICA components of subject 5 are shown in Fig. 5-3. It indicates the 10 Hz power suppression components occurred with symmetry. The 10 Hz power suppression is the most common indication for the motion sickness symptoms, since 4 of the 6 subjects have the similar phenomenon in this area.
(a) Component in the left parietal lobe. (b) Component in the right parietal lobe.
Fig. 5-3. The 10 Hz power activity suppression of subject 5 in two different ICA components.
The 10-Hz suppression phenomena occur in both left and right parietal region at the same time. The curve of 10 Hz power activity suppression corresponding to Fig. 5-3 is shown in Fig. 5-4.
Fig. 5-4. The 10 Hz power suppression in the side parietal lobe.
5.1.3 The 10/20 Hz power suppression in the central parietal region
The central parietal region is also considered as an important nausea-related area according to our results. The spatial distributions on scalp topographies of weighting matrices for dominant ICA components of subjects 3, 7, and 10 are shown in Fig. 5-5. All the components are at the central parietal region and they have both 10 Hz and 20 Hz power activity suppressions. The curves of 10/20 Hz power activity suppression corresponding to Fig. 5-5 is shown in Fig. 5-6.
(a) Subject-3. (b) Subject-7. (c) Subject-10.
Fig. 5-5. The nausea-related area in the central parietal region.
Fig. 5-6. The 10 Hz and 20 Hz power suppression in the central parietal lobe.
5.1.4 The nausea-related regions on human cortex
Table 5-1 is a comparison of the nausea-related regions in different subjects. All the subjects have the power-suppressions near 10 Hz or 20 Hz in the parietal lobe, as shown in Fig. 5-7.
Table 5-1: Comparison of nausea-related regions.
Component
In general, the parietal lobe plays important roles in integrating sensory information from various senses and in the manipulation of objects. This area of the cortex is responsible for somatosensation. This cortical region receives inputs from the somatosensory relays of the thalamus.
Fig. 5-7. The region of parietal lobe.
5.2 Power Spectrum Suppression Induced by Motion Sickness
We have concluded that all the subjects have the power-suppressions phenomena near 10 Hz or 20 Hz in the parietal lobe in section 5.1. The frequency band of power-suppression differs from subjects, but inside the range between 8 and 22 Hz. The range of the power-suppression frequency band is sometimes wide for subject 1 and 5, which is about 10 to 20 Hz. And the range for subject 3, 7, and 10 may be narrow with the range about 18 to 19 Hz and 10 to 13 Hz in the central parietal lobe. All the subjects have the power-suppressions in the range from 10 to 13 Hz and more than 80 % of the subjects have the power-suppressions in the range from 18 to 19 Hz.
5.3 Reliability of Different Physiological Responses
The reliabilities of different physiological responses are going to be discussed in this section. The three different objective indices for the assessment of nausea-symptoms are EKG, EGG and GSR. We are going to define some useful assessing parameters for the reliability of each signal. The EKG variation ratio which is corresponding to the EKG signal is defined as:
%
where fMotion−Sickness is the EKG dominant frequency in “Motion-Sickness” session, fBaseLine is the EKG dominant frequency in “Baseline” session. The EGG and GSR variation ratios can also be defined as the same way.
%
Table 5-2 is the comparison of the motion sickness indices we used in this research including the subjective MSQ score and the objective indices.
There is no significant difference in the GSR signals between “Baseline” and
“Motion-Sickness” sessions, except subject 7 and subject 10. A small questionnaire was proposed to the subject before every experiment, which included a question: “Do you feel sweating during motion sickness?” The answer of both subject 7 and subject 10 is “Yes”, while the others answer no. From this point of view, although the GSR signal is useless for most of subjects, but it is a good physiological index for the subjects who feel sweating during motion sickness with significant changes and fast response time.
Table 5-2: Comparisons of indices.
Subject MSQ score
Subject 1 24 % 67 % 4 % * 17/50
Subject 3 9 % 67 % 3 % * 28/50
Subject 5 15 % 67 % 5 % * 11/50
Subject 6 0 % * 33 % 6 % * 8/50
Subject 7 8 % 33 % 175 % 17/50
Subject 8 0 % * 0 % * 1 % * 5/50
Subject 9 14 % 100% 8 % * 34/50
Subject 10 17 % 67 % 54 % 12/50
* No significant difference
The influence factors to the EKG signal is varied. And also, the variation rates of EKG are not as significant as GSR or EGG. It was found from the result that the EGG signal is the most efficient physiological signal, which is suitable for most of subjects and provides excellent response time.
REKG REGG RGSR
Chapter 6
Conclusions and Future Work
The nausea-related EEG dynamics corresponding to motion sickness inclining tasks is studied in this thesis with the virtual-reality based dynamic driving environment. The VR-based dynamic driving environment provides the advantages of safety, low cost, and the realistic stimuli to the subjects. The MSQ is designed and the physiological responses (including EKG, EGG and galvanic skin response) are recorded to assess the 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. In other words, 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. It was found from the result that the EGG signal is an efficient index, which is suitable for most of the subjects with excellent response time.
Using ICA and PSD analysis technology, the power suppression in some specific frequency bands (such as 10 Hz or 20 Hz) of ICA components are proved to be a common phenomenon when most of subjects during motion sickness and the suppressions will release when the subjects recovered from motion sickness after rest. All of subjects indicate that the influence regions of motion sickness on human cortex are in the parietal lobe area. The phenomenon is obviously, and is considered as an important discovery in our study.
The future directions of this study are: (1) assessment of nausea degree, (2) a motion sickness estimator can be developed based on the findings in this thesis, and (3) the comparison study between the drivers and the passengers.
REFERENCES
[1] R. S. Kellog, C. Castore, and R. Coward, “Psychophysiological Effects of Training in a Full Vision Simulator”. Annual Scientific Meeting of the Aerospace Medical Association, 1980.
[2] D. W. Gower and J. E. Fowlkes. “Simulator Sickness in the UH-60 (Black Hawk) Flight Simulator”, U.S. Army Aeromedical Research Laboratory, September, 1989.
[3] Graybiel A. and Lackner J. R., ”Evaluation of the Relationship between Motion Sickness Sympto-matology and Blood Pressure, Heart Rate, and Body Temperature”, Aviat. Space and Environment Medicine., 51, p.211-214, 1980.
[4] Stern R. M., Koch K. L., Leibowitz H. W., Linbald I. M., Shupert C. L., and Stewart W.
R., “Tachygastria and Motion Sickness”, Aviat. Space and Environment Medicine, 56, p.1074-1077, 1985
[5] Cowings P. S., Naifeh K. H., and Toscane W. B., “The Stability of Individual Patterns of Autonomic Responses to Motion Sickness Stimulation.”, Aviat. Space and Environment Medicine, 61, p.399-405, 1990
[6] Miller J. C., Sharkey T. J., Graham G. A., and Mccauley M. E., “Autonomic Physiological Data Associated with Simulator Discomfort.”, Aviat. Space and Environment Medicine, Sep., p.817-819, 1993
[7] Hirohisa Mizuta, Isao Takeuchi, Osamu Tsuda Kazuo Yana, and Takeshi Goto, “Changes in the Heart Rate Variability and Electrogastrogram due to the Motion Sickness.”, 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, p.1636-1637, Amsterdam 1996
[8] Byung Chan Min, Soon Cheol Chung, Yoon Ki Min, and Kazuyoshi Sakamoto,
“Psychophysiological evaluation of simulator sickness evoked by a graphic simulator”, Applied Ergonomics, 35, p. 549-556, 2004
[9] Kolasinski E. M., “Simulator Sickness in Virtual Environments.”, U.S. Army Research Institute Simulator Systems Research Unit, May, 1995
[10] James R. Evans, Andrew Abarbanel, Quantitative EEG and Neuron Feedback, Academic Press, London, 1999
[11] Holmes S. R., “Electrogastric Activity during a Control Condition and as a Measure of Motion Sickness on Exposure to Yaw Occillation.” United Kingdom Group Meeting on Human Response to Vibration, ISVR, University of Southhampton, England, 17-19 September 1997
[12] Kennedy R. S., Lane N. E., and Berbaum K. S., “Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness.”, International Journal of Aviation Psychology, 3, p.203-220, 1993
[13] Hu S., Stern R. M., Vasey M. W., and Koch K. L., “Motion Sickness and Gastric Myoelectric Activity as a Function of Speed of Rotation of a Circular Vection Drum”, Aviat. Space Environment Medicine, 60, p.414-441, 1989
[14] Uijtdehaage S. H. J., Stern R. M., and Koch K. L., “Eating Suppresses Motion Sickness:
The Relationships among Vagal Activity, Electrogastrogram and Motion Sickness.”, Gastroenterology 98, 1987
[15] Xu L. H., Koch K. H., Summy Long J., Stern R. M., Seaton J. F., Harrison T. S., and Demers L. M., “Hypothalamic and Gastric Myoelectrical Responses During Vection-induced Nauses in Healthy Chinese Subjects.”, American Journal Physiology, 265, p. E578-E584, 1993
[16] Romano R. A., “The Evaluation of the Electrogastrogram for use in Simulator Sickness Studies.”, UTIAS Technical Note No. 273, 1990
[17] T. P. Jung, C. Humphries, T. W. Lee, S. Makeig, M. J. McKeown, V. Iragui, and T. J.
Sejnowski, ”Extended ICA removes artifacts from electroencephalographic recordings.”, Advances in Neural Information Processing System, 10, p.894-900, 1998
[18] A. Papoulis, “Minimum Bias Windows for High Resolution Spectral Estimation.”, IEEE Trans. Inform. Theory, Vol. IT-19, p.9-12, 1973
[19] C. T. Lin, R. C. Wu, S. F. Liang, W. H. Chao, Y. J. Chen,and T. P. Jung, “EEG-based Drowsiness Estimation for Safety Driving Using Independent Component Analysis.”, IEEE CAS, 2005
[20] T. P. Jung and Scott Makeig, “Mining Electroencephalographic Data Using Independent Component Analysis.”, EEG Journal ,2003
[21] http://www.webhospital.org.tw/free/question2.html?outpatient=&uid=490&de_id=26 [22] Robert W. Baloh, “Robert Bárány and the Controversy Surrounding His Discovery of the
Caloric Reaction”, Historical Neurology, Vol. 58, p. 1094-1099, 2002
[23] Berthoz A., “The Brain’s Sense of Movement”, Harvard University Press, 2000 [24] J. T. Reason and J. J. Brand, “Motion Sickness”, London Academic Press, 1975 [25] EEGLAB Tools http://www.sccn.ucsd.edu/eeglab/index.html
[26] Chizuru Nakagawa and Mieko Ohsuga, “The Present Situation of the Studies in VE-Sickness and its close field.”, Transactions of the Virtual Reality Society of Japan, Vol. 3, No. 2, 1998
[27] Joseph J. and LaViola, “A Discussion of Cybersickness in Virtual Environments.”, Sigchi Bulletin, Vol. 32, No. 1, 2000
[28] Magellan's Travel Advice, http://www.magellans.com
[29] Andras Kemeny and Francesco Panerai, “Evaluating Perception in Driving Simulation Experiments.”, Transactions in Cognitive Sciences, Vol. 7, No. 1, 2003
[30] Darren Flynn, Paul Van Schaik, Tim Blackman, Clive Femcott, Brian Hobbs,and Carlos Calderon, “Developing a Virtual Reality-Based Methodology for People with Dementia:
A Feasibility Study.”, CyberPsychology and Behavior, Vol. 6, No. 6, 2003
[31] Natalie Owen, Antony Graham Leadbetter, and Lucy Yardley, ”Relationship between Postural Control and Motion Sickness in Healthy Subjects.”, Brain Research Bulletin, Vol. 47, No. 5, p. 471-474, 1998
[32] C. F. Hsu, C. T. Lin, T. Y. Huang, and K. Y. Young, “Development of Multipurpose Virtual-Reality Dynamic Simulator with a Force-Reflection Joystick.”, Proc. ImechE., Vol. 219, 2005
[33] John F. Golding, “Motion Sickness Susceptibility Questionnaire Revised and its Relationship to Other Forms of Sickness.”, Brain Research Bulletin, Vol. 47, No. 5, p.
507-516, 1998
[34] Rindala T. Saliba, S. S. Reisman, and W. N. Tapp, “Vagal Response in Heart Rate Variability under Motion Sickness Stimulation.”, IEEE, 1992
[35] Masanori Ishii, http://homepage3.nifty.com/motion-sickness/
[36] Thomas J. Mullen, Ronald D. Berger, Charles M. Oman, and Richard J. Cohen, ”Human Heart Rate Variability Relation is Unchanged During Motion Sickness.”, Journal of Vestibular Research, Vol. 8 No. 1, p. 95-105, 1998
[37] Manda J. Williasmson, Matthew J. Thomas, and Robert M. Stern, “The Contribution of Expectations to Motion Sickness Symptoms and Gastric Activity.”, Journal of Psychosomatic Research, Vol. 56, p. 721-726, 2004
[38] Ognyan I. Kolev and Ivan A. Altaparmakov, “Changes in the Gastrointestinal Electric Pattern to Motion Sickness in Susceptibles and Insusceptibles During Fasting.”, Journal of Vestibular Research, Vol. 6, No. 1, p. 15-21, 1996
[39] Bob Cheung and Peter Vaitkus, “Perspectives of Electrogastrography and Motion Sickness.”, Brain Research Bulletin, Vol. 47, No. 5, p. 421-431, 1998
[40] Holmes Sharon R. and Griffin Michael, “Correlation Between Heart Rate and the Severity of Motion Sickness Caused by Optokinetic Stimulation.”, Journal of Psychophysiology, Vol. 15, No. 1, p. 35-42, 2001
[41] Thomas A. Stoffregen, Lawrence J. Hettinger, Michael W. Haas, Merry M. Roe, and L.
James Smart, “Postural Instability and Motion Sickness in a Fixed-Base Flight Simulator.”, Human Factors, Vol. 42, No. 3, p. 458-469, 2000
[42] L. James Smart, Thomas A. Stoffregen, and Benoit G. Bardy, “Visually Induced Motion Sickness Predicted by Postural Instability.”, Human Factors, Vol. 44, No. 3, p. 451-465, 2002
[43] Frank Bremmer, “Navigation in Space: The Role of the Macaque Ventral Intraparietal Area.”, The Journal of Physiology Symposium, Vol. 566, No. 1, p. 29-35, 2005
[44] P. J. Durka, H. Klekowicz, K. J. Blinowska, W. Szelenberger, and Sz. Niemcewicz, “A Simple System for Detection of EEG Artifacts in Polysomnographic Recordings.”, IEEE Transactions on Biomedical Engineering, Vol. 50, No. 4, 2003
[45] S. James P. Spottiswoode and E. C. May, “Skin Conductance Prestimulus Response:
Analyses, Artifacts and a Pilot Study.”
[46] Holmes Sharon R., Stuart King, J. R. Rollin Scott, and Stacy Clemes, “Facial Skin Pallor Increases During Motion Sickness.”, Journal of Psychophysiology, Vol. 16, No. 3, p.
150-157, 2002
[47] NeuroScan company, http://www.neuro.com/index.sstg [48] BIOPAC company, http://www.biopac.com/
[49] Shahani B. T., Halperin J. J., Boulu P., and Cohen J., “Sympathetic Skin Response--a Method of Assessing Unmyelinated Axon Dysfunction in Peripheral Neuropathies.”, Journal of the Neurol Neurosurg Psychiatr, vol. 47, p. 536-542, 1984.
[50] M. P. Tarvainen, P. A. Karjalainen, A. S. Koistinen, and M. Valkonen Korhonen,
“Principal Component Analysis of Galvanic Skin Responses.”, IEEE Transactions on Biomedical Engineering, 2000
[51] Peter Shepherd and Gregory Mitchell, “The GSR and Bilateral Meter Course.”, http://www.trans4mind.com/metercourse/
[52] Diana Weedman Molavi, The Washington University School of Medicine Neuroscienee Tutorial, http://thalamus.wustl.edu/course/, 1999.