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Hub AI
EEG microstates AI simulator
(@EEG microstates_simulator)
Hub AI
EEG microstates AI simulator
(@EEG microstates_simulator)
EEG microstates
EEG microstates are transient, patterned, quasi-stable states or patterns of an electroencephalogram. These tend to last anywhere from milliseconds to seconds and are hypothesized to be the most basic instantiations of human neurological tasks, and are thus nicknamed "the atoms of thought". Microstate estimation and analysis was originally done using alpha band activity, though broader bandwidth EEG bands are now typically used. The quasi-stability of microstates means that the "global [EEG] topography is fixed, but strength might vary and polarity invert."
The concept of temporal microstates of brain electrical activity during no-task resting and task execution (event-related microstates) was developed by Dietrich Lehmann and his collaborators (The KEY Institute for Brain-Mind Research, University of Zurich, Switzerland) between 1971 and 1987,( see "EEG microstates". Scholarpedia.) Drs. Thomas Koenig (University Hospital of Psychiatry, Switzerland) and Dietrich Lehmann (KEY Institute for Brain-Mind Research, Switzerland) are often credited as the pioneers of EEG Microstate analysis. In their 1999 paper in the European Archives of Psychiatry and Clinical Neuroscience, Koenig and Lehmann had been analyzing the EEGs of those with schizophrenia, in order to investigate the potential basic cognitive roots of the disorder. They began to turn their attention to the EEGs on a millisecond scale. They determined that both normal subjects and those with schizophrenia shared these microstates, but they varied in characteristics between the two groups, and concluded that:
Isolating and analyzing one's EEG microstate sequence is a post-hoc operation that typically utilizes several averaging and filtering steps. When Koenig and Lehman ran their experiment in 1999 they constructed these sequences by starting from a subject's eyes-closed resting state EEG. The first several event-free minutes of the EEG were isolated, then periods of around 2 seconds each were refiltered (Band-pass ≈ 2–20 Hz). Once the epochs were filtered, these microstates were analytically clustered into mean classes via k-means clustering, post hoc. A probabilistic approach, using Fuzzy C-Means, to clustering and subsequent assigning (see below) of microstates has also been proposed.
Since the brain goes through so many transformations in such short time scales, microstate analysis is essentially an analysis of average EEG states. Koenig and Lehmann set the standard for creating classes, or recurrent averaged EEG configurations. Once all the EEG data is collected, a "prototype" EEG segment is chosen, with which to compare all other collected microstates. This is how the averaging process starts. Variance from this "prototype" is computed to either add it to an existing class, or to create a separate class. After similar configurations are "clustered" together, the process of selecting and comparing a "prototype" is repeated several times for accuracy. The process is described in more detail by Koenig and Lehmann:
"Similarity of EEG spatial configuration of each prototype map with each of the 10 maps is computed using the coefficient of determination to omit the maps' polarities. ...Separately for each class the prototype maps are updated combining all assigned maps by computing the first spatial principal component of the maps and thereby maximizing the common variance while disregarding the map polarity." This process is repeated several times using different randomly selected prototype maps from among the collected data to use for statistical comparison and variance determination.
Most studies reveal the same 4 classes of microstate topography:
However, many studies have also found other EEG microstate template maps that are likely to be meaningful. converged on 16 maps to explain a high proportion of the observed variance. found 13 maps using an ICA approach. The number of microstates 'found' and used is partly a function of the cognitive state of the person, but also partly the method used to cluster and assign microstates. Though microstates have historically always been assigned deterministically, recent work has also suggested that there are computational, analytical and conceptual issues that may be addressed through a probabilistic analysis of microstates.
It is the current hypothesis that EEG Microstates represent the basic steps of cognition and neural information processing in the brain, but there is still much research that needs to be done to cement this theory.
EEG microstates
EEG microstates are transient, patterned, quasi-stable states or patterns of an electroencephalogram. These tend to last anywhere from milliseconds to seconds and are hypothesized to be the most basic instantiations of human neurological tasks, and are thus nicknamed "the atoms of thought". Microstate estimation and analysis was originally done using alpha band activity, though broader bandwidth EEG bands are now typically used. The quasi-stability of microstates means that the "global [EEG] topography is fixed, but strength might vary and polarity invert."
The concept of temporal microstates of brain electrical activity during no-task resting and task execution (event-related microstates) was developed by Dietrich Lehmann and his collaborators (The KEY Institute for Brain-Mind Research, University of Zurich, Switzerland) between 1971 and 1987,( see "EEG microstates". Scholarpedia.) Drs. Thomas Koenig (University Hospital of Psychiatry, Switzerland) and Dietrich Lehmann (KEY Institute for Brain-Mind Research, Switzerland) are often credited as the pioneers of EEG Microstate analysis. In their 1999 paper in the European Archives of Psychiatry and Clinical Neuroscience, Koenig and Lehmann had been analyzing the EEGs of those with schizophrenia, in order to investigate the potential basic cognitive roots of the disorder. They began to turn their attention to the EEGs on a millisecond scale. They determined that both normal subjects and those with schizophrenia shared these microstates, but they varied in characteristics between the two groups, and concluded that:
Isolating and analyzing one's EEG microstate sequence is a post-hoc operation that typically utilizes several averaging and filtering steps. When Koenig and Lehman ran their experiment in 1999 they constructed these sequences by starting from a subject's eyes-closed resting state EEG. The first several event-free minutes of the EEG were isolated, then periods of around 2 seconds each were refiltered (Band-pass ≈ 2–20 Hz). Once the epochs were filtered, these microstates were analytically clustered into mean classes via k-means clustering, post hoc. A probabilistic approach, using Fuzzy C-Means, to clustering and subsequent assigning (see below) of microstates has also been proposed.
Since the brain goes through so many transformations in such short time scales, microstate analysis is essentially an analysis of average EEG states. Koenig and Lehmann set the standard for creating classes, or recurrent averaged EEG configurations. Once all the EEG data is collected, a "prototype" EEG segment is chosen, with which to compare all other collected microstates. This is how the averaging process starts. Variance from this "prototype" is computed to either add it to an existing class, or to create a separate class. After similar configurations are "clustered" together, the process of selecting and comparing a "prototype" is repeated several times for accuracy. The process is described in more detail by Koenig and Lehmann:
"Similarity of EEG spatial configuration of each prototype map with each of the 10 maps is computed using the coefficient of determination to omit the maps' polarities. ...Separately for each class the prototype maps are updated combining all assigned maps by computing the first spatial principal component of the maps and thereby maximizing the common variance while disregarding the map polarity." This process is repeated several times using different randomly selected prototype maps from among the collected data to use for statistical comparison and variance determination.
Most studies reveal the same 4 classes of microstate topography:
However, many studies have also found other EEG microstate template maps that are likely to be meaningful. converged on 16 maps to explain a high proportion of the observed variance. found 13 maps using an ICA approach. The number of microstates 'found' and used is partly a function of the cognitive state of the person, but also partly the method used to cluster and assign microstates. Though microstates have historically always been assigned deterministically, recent work has also suggested that there are computational, analytical and conceptual issues that may be addressed through a probabilistic analysis of microstates.
It is the current hypothesis that EEG Microstates represent the basic steps of cognition and neural information processing in the brain, but there is still much research that needs to be done to cement this theory.