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Amplitude and phase relationship between alpha and beta oscillations in the human electroencephalogram
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).ORCID iD: 0000-0001-6877-8714
Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, Stockholm.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, Stockholm.
2005 (English)In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 43, no 5, 599-607 p.Article in journal (Refereed) Published
Abstract [en]

The relationship between the electro-encephalographic (EEG) alpha and beta oscillations in the resting condition was investigated in the study. EEGs were recorded in 33 subjects, and alpha (7.5-12.5Hz) and beta (15-25Hz) oscillations were extracted with the use of a modified wavelet transform. Power, peak frequency and phase synchronisation were evaluated for both types of oscillation. The average beta-alpha peak frequency ratio was about 1.9-2.0 for all electrode derivations. The peak frequency of beta activity was within 70-90 % of the 95 % confidence interval of twice the alpha frequency. A significant (p < 0.05) linear regression was found between beta and alpha power in all derivations in 32 subjects, with the slope of the regression line being approximate to 0.3. There was no significant difference in the slope of the line in different electrode locations, although the power correlation was strongest in the occipital locations where alpha and beta oscillations had the largest power. A significant 1:2 phase synchronisation was present between the alpha and beta oscillations, with a phase lag of about pi/2 in all electrode derivations. The strong frequency relationship between the resting beta and alpha oscillations suggests that they are generated by a common mechanism. Power and phase relationships were weaker suggesting that these properties can be modulated by additional mechanisms as well as be influenced by noise. A careful distinction between alpha-dependent and alpha-independent beta activity should be considered when making statements about the possible significance of genuine beta activity in different neurophysiological mechanisms.

Place, publisher, year, edition, pages
2005. Vol. 43, no 5, 599-607 p.
Keyword [en]
alpha and beta oscillations, EEG, Mu rhythm, fast Fourier, transform, neuronal synchrony, rhythms, desynchronization, frequency, locking, band
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-15286DOI: 10.1007/BF02351033ISI: 000234262300009Scopus ID: 2-s2.0-29244450472OAI: oai:DiVA.org:kth-15286DiVA: diva2:333327
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Multiscale analysis of multi-channel signals
Open this publication in new window or tab >>Multiscale analysis of multi-channel signals
2005 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

I: Amplitude and phase relationship between alpha and beta oscillations in the human EEG We have studied the relation between two oscillatory patterns within EEG signals (oscillations with main frequency 10 Hz and 20 Hz), with wavelet-based methods. For better comparison, a variant of the continuous wavelet transform, was derived. As a conclusion, the two patterns were closely related and 70-90 % of the activity in the 20 Hz pattern could be seen as a resonance phenomenon of the 10 Hz activity.

II: A local discriminant basis algorithm using wavelet packets for discrimination between classes of multidimensional signals We have improved and extended the local discriminant basis algorithm for application on multidimensional signals appearing from multichannels. The improvements includes principal-component analysis and crossvalidation- leave-one out. The method is furthermore applied on two classes of EEG signals, one group of control subjects and one group of subjects with type I diabetes. There was a clear discrimination between the two groups. The discrimination follows known differences in the EEG between the two groups of subjects.

III: Improved classification of multidimensional signals using orthogonality properties of a time-frequency library We further improve and refine the method in paper2 and apply it on 4 classes of EEG signals from subjects differing in age and/or sex, which are known factors of EEG alterations. As a method for deciding the best basis we derive an orthogonalbasis- pursuit-like algorithm which works statistically better (Tukey's test for simultaneous confidence intervals) than the basis selection method in the original local discriminant basis algorithm. Other methods included were Fisher's class separability, partial-least-squares and cross-validation-leave-one-subject out. The two groups of younger subjects were almost fully discriminated between each other and to the other groups, while the older subjects were harder to discriminate.

Place, publisher, year, edition, pages
Stockholm: KTH, 2005. vii, 17 p.
Series
Trita-MAT. MA, ISSN 1401-2278 ; 05:09
Keyword
Mathematical statistics, Matematisk statistik
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-230 (URN)91-7178-066-1 (ISBN)
Public defence
2005-06-03, Sal D3, Lindstedtsvägen 5, Stockholm, 14:00
Opponent
Supervisors
Note
QC 20101001Available from: 2005-05-30 Created: 2005-05-30 Last updated: 2010-10-01Bibliographically approved

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