Application of multivariate autoregressive modeling for analyzing the interaction between EEG and EMG in humans

International Congress Series

Volumn 1270, Issue C, 2004, Pages 249-253

  Shibata, Tomohiro ^a,b,c   Suhara, Yuichi ^a   Oga, Tatsuhide ^d   Ueki, Yoshino ^d   Mima, Tatsuya ^d   Ishii, Shin ^a,c  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^c JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^d KYOTO UNIVERSITY   (Japan)

Author keywords

BIC; EEG; EMG; Multivariate autoregression; Time domain analysis

Indexed keywords

EID: 33846602683     PISSN: 05315131     EISSN: None     Source Type: Book Series    
DOI: 10.1016/j.ics.2004.05.048     Document Type: Article

References (7)

1. Akaike, H, On the use of a linear model for the identification of feedback systems. Ann. Inst. Stat. Math. 20 (1968), 425-439.
2. Kaminski, M.J., Blinowska, K.J., A new method of the description of the information flow in the structures. Biol. Cybern. 65 (1991), 203-210.
3. Mima, T, Matsuoka, T, Hallet, M, Information flow from the sensorimotor cortex to muscle in humans. Clin. Neurophysiol. 112 (2001), 122-126.
4. Farmer, S.F., et al. Abnormal cortex–muscle interactions in subjects with X-linked Kallman's syndrome and mirror movement. Brain 127 (2004), 385-397.
5. Hjorth, B, Source derivation simplifies topographical EEG interpretation. Am. J. E.E.G. Technol. 20 (1980), 121-132.
6. Schwartz, G, Estimating the dimension of a model. Ann. Stat. 8 (1978), 147-164.
7. Akaike, H, A new look at the statistical model identification, IEEE Transaction on Automatic Control AC 19:6 (1974), 716-723.