EEG Alpha and Gamma Modulators Mediate Motion Sickness-Related Spectral Responses

被引:42
作者
Chuang, Shang-Wen [1 ]
Chuang, Chun-Hsiang [1 ,2 ]
Yu, Yi-Hsin [1 ]
King, Jung-Tai [1 ]
Lin, Chin-Teng [1 ,2 ]
机构
[1] Natl Chiao Tung Univ, Brain Res Ctr, 1001 Ta Hsueh Rd, Hsinchu 30010, Taiwan
[2] Univ Technol Sydney, Fac Engn & Informat Technol, 15 Broadway, Ultimo, NSW 2007, Australia
关键词
EEG; motion sickness; co-modulation; gamma; alpha; sensory conflict theory; THEORETICAL-ANALYSIS; BLIND SEPARATION; NEURAL-NETWORK; BAND ACTIVITY; BRAIN; SYNCHRONIZATION; FREQUENCY; OSCILLATIONS; INTEGRATION; FRACTALITY;
D O I
10.1142/S0129065716500076
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Motion sickness (MS) is a common experience of travelers. To provide insights into brain dynamics associated with MS, this study recruited 19 subjects to participate in an electroencephalogram (EEG) experiment in a virtual-reality driving environment. When riding on consecutive winding roads, subjects experienced postural instability and sensory conflict between visual and vestibular stimuli. Meanwhile, subjects rated their level of MS on a six-point scale. Independent component analysis (ICA) was used to separate the filtered EEG signals into maximally temporally independent components (ICs). Then, reduced logarithmic spectra of ICs of interest, using principal component analysis, were decomposed by ICA again to find spectrally fixed and temporally independent modulators (IMs). Results demonstrated that a higher degree of MS accompanied increased activation of alpha (r = 0.421) and gamma (r = 0.478) IMs across remote-independent brain processes, covering motor, parietal and occipital areas. This co-modulatory spectral change in alpha and gamma bands revealed the neurophysiological demand to regulate conflicts among multi-modal sensory systems during MS.
引用
收藏
页数:14
相关论文
共 83 条
[1]  
Adeli H, 2010, AUTOMATED EEG-BASED DIAGNOSIS OF NEUROLOGICAL DISORDERS: INVENTING THE FUTURE OF NEUROLOGY, P1
[2]   Spatiotemporal Analysis of Relative Convergence of EEGs Reveals Differences Between Brain Dynamics of Depressive Women and Men [J].
Ahmadlou, Mehran ;
Adeli, Hojjat ;
Adeli, Amir .
CLINICAL EEG AND NEUROSCIENCE, 2013, 44 (03) :175-181
[3]   Fractality analysis of frontal brain in major depressive disorder [J].
Ahmadlou, Mehran ;
Adeli, Hojjat ;
Adeli, Amir .
INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY, 2012, 85 (02) :206-211
[4]   Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder [J].
Ahmadlou, Mehran ;
Adeli, Hojjat ;
Adeli, Amir .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2012, 391 (20) :4720-4726
[5]   Graph Theoretical Analysis of Organization of Functional Brain Networks in ADHD [J].
Ahmadlou, Mehran ;
Adeli, Hojjat ;
Adeli, Amir .
CLINICAL EEG AND NEUROSCIENCE, 2012, 43 (01) :5-13
[6]   Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems [J].
Ahmadlou, Mehran ;
Adeli, Hojjat .
PHYSICA D-NONLINEAR PHENOMENA, 2012, 241 (04) :326-332
[7]   Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology [J].
Ahmadlou, Mehran ;
Adeli, Hojjat .
NEUROIMAGE, 2011, 58 (02) :401-408
[8]   Fuzzy Synchronization Likelihood with Application to Attention-Deficit/Hyperactivity Disorder [J].
Ahmadlou, Mehran ;
Adeli, Hojjat .
CLINICAL EEG AND NEUROSCIENCE, 2011, 42 (01) :6-13
[9]   Fractality and a Wavelet-chaos-Methodology for EEG-based Diagnosis of Alzheimer Disease [J].
Ahmadlou, Mehran ;
Adeli, Hojjat ;
Adeli, Anahita .
ALZHEIMER DISEASE & ASSOCIATED DISORDERS, 2011, 25 (01) :85-92
[10]   Fractality and a Wavelet-Chaos-Neural Network Methodology for EEG-Based Diagnosis of Autistic Spectrum Disorder [J].
Ahmadlou, Mehran ;
Adeli, Hojjat ;
Adeli, Amir .
JOURNAL OF CLINICAL NEUROPHYSIOLOGY, 2010, 27 (05) :328-333