How to compute microstate?
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Microstate analysis is a method for investigating global brain connections using electroencephalography (EEG) . The analysis involves clustering the changing voltage patterns measured by EEG into microstate sequences . These sequences summarize the underlying data and reduce its high dimensionality. One common approach is to restrict the pattern matching step to local maxima of the global field power (GFP) and interpolate the microstate fit in between . The microstate sequences can be analyzed using various techniques such as time-lagged mutual information and Fourier transform methodology . These analyses can provide insights into the periodicity, predictability, and dynamics of microstate sequences. It is important to note that the effects of anesthetics like propofol on microstate sequences and their metrics, such as periodicity and predictability, should be considered .
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Open access•Posted Content | The lectures provide a computational narrative to build proficiency in computing and understanding microstate geometries in five dimensions. |
| The paper does not provide specific details on how to compute microstates. | |
1 Citations | The paper does not provide information on how to compute microstates. |
12 May 2023 | Microstate sequences are computed using a clustering approach to reduce the high dimensionality of the EEG data. |
| The paper does not provide information on how to compute microstates. |
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What are the different methods for selecting k in EEG microstate analysis?4 answersDifferent methods for selecting k in EEG microstate analysis include the extraction, detection, and representation of microstates based on the Local Global graph (LG graph). Another method involves the flavor-tagged amplitude analysis of the decay ${D}^{0}$ to the self-conjugate final state ${K}^{+}{K}^{\ensuremath{-}}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}$. Invasive intracranial EEG recordings can be used to study the intracranial correlates of spontaneous scalp KCs and compare the intracranial activity of scalp KCs associated or not with arousals. Additionally, a method combining segmentation, cluster analysis, and neural networks can be used for searching K-complex. Finally, an open-source Python package can be used to compute information-theoretical quantities for EEG data, including EEG microstate analysis.
How to determine k in EEG microstate analysis?5 answersDetermining the value of k in EEG microstate analysis involves applying various clustering algorithms to the EEG data and comparing the results. Different algorithms, such as (T)AAHC, modified K-Means, and HMM, have been used to compute microstates. These algorithms yield similar results, except for HMM, which generates more dissimilar results. Additionally, the linear characteristics of the underlying EEG signal, such as cross-covariance and autocorrelation structure, play a significant role in determining microstate properties. Linear models, such as vector autoregression, can generate microstates comparable to those estimated from real EEG data, supporting the use of linear EEG models in interpreting microstate properties. Overall, the choice of k in EEG microstate analysis involves comparing the results of different clustering algorithms and considering the linear characteristics of the EEG signal.