From 2b7cfce2daeca84057ab3f0cd7a637876afac2ee Mon Sep 17 00:00:00 2001 From: Seyed Yahya Shirazi Date: Wed, 28 Aug 2024 12:20:32 -0700 Subject: [PATCH 1/5] Added the text --- .../component_clustering_tools.md | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/tutorials/10_Group_analysis/component_clustering_tools.md b/tutorials/10_Group_analysis/component_clustering_tools.md index fd33aa83..fec0afac 100644 --- a/tutorials/10_Group_analysis/component_clustering_tools.md +++ b/tutorials/10_Group_analysis/component_clustering_tools.md @@ -286,7 +286,7 @@ You may call the [pop_clust.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=p ![](/assets/images/studyclust5.png) -Several algorithms are available: *kmeans*, *neural network*, and *affinity* clustering. +Several algorithms are available: *kmeans*, *neural network*, *affinity*, and *affinity* clustering. *Kmeans* requires the MATLAB Statistics Toolbox, while *neural network* clustering uses a function from the MATLAB Neural Network Toolbox. A version of *kmeans* that does not require the MATLAB Statistics Toolbox is also available. *Affinity* clustering does not require any toolbox. We recommend using *affinity* clustering which does not require to specify the number of clusters, then try the *kmeans* algorithm if the results are not satisfactory. @@ -303,10 +303,20 @@ defined as components further than a specified number of standard deviations (3, by default) from any of the cluster centroids. To turn on this option, click the upper checkbox on the left. Identified outlier components will be placed into a designated *Outliers* cluster -(Cluster 2). +(Cluster 2). Press *Ok*. The cluster editing interface detailed in one of the following sections will automatically pop up. +Optimal Kmeans clustering +----------------- +We have recently added a new feature to the *kmeans* clustering algorithm in EEGLAB. This feature allows you to find the optimal number of clusters for your data. To use this feature, you need to have the [MATLAB Statistics and Machine Learning Toolbox](https://www.mathworks.com/products/statistics.html) installed. + +To use this feature, you need to select the *Optimal Kmeans* option from the *Clustering algorithm* dropdown menu. Then, you need to input a range of cluster numbers to test (in the screenshot below minimum is set to 10 and maximum is set to 30). The algorithm will then test the clustering for each number of clusters in the range and chooses the optimal number of clusters based on the *silhouette* score. The *silhouette* score is a measure of how similar an object is to its own cluster compared to other clusters. The optimal number of clusters is the one that maximizes the *silhouette* score. Read more about the *silhouette* score from the [MATLAB documentation](https://www.mathworks.com/help/stats/clustering.evaluation.silhouetteevaluation.html). + +*Recommended number of clusters:* Following the estimated number of clusters above, we recommend setting the lower bound of the cluster range to the half the average number of components per subject. For example, if you have 20 components per subject, set the lower bound to 10. Similarly, set the upper bound to 1.5 times the average number of components per subject. For example, if you have 20 components per subject, set the upper bound to 30. If the returned number of clusters is at its lower or upper bound, consider expanding the range. We also strongly recommend using the option to separate outliers. + +![](/assets/images/studyclust5.png) + Other clustering methods ----------------- The main method to cluster components in EEGLAB is the *PCA clustering method* described in this tutorial. Other methods are the *Measure Projection method* and the *Scalp Correlation method* available in the EEGLAB plugins described below. 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zHQLz!Hjl|DM6_g`Ay|1_1O~&z8xV?Q>hTk8J6VD!svqu{<2*xJBsif1bxvY=5Sh>c z0STTI9T0(PNrl+O$`X2J*){5i7#rmYSwb7-8P^fxWeTGNXXEYo<#b;SK>3d|#R z8Jk34R4Fv6Mh=BD>@uQbd1*XT>@qs|M|8(>#(AduE`p+*u>{|5meu)jRAR}KIG002ovPDHLkV1g7dINtyO literal 0 HcmV?d00001 diff --git a/tutorials/10_Group_analysis/component_clustering_tools.md b/tutorials/10_Group_analysis/component_clustering_tools.md index fec0afac..7d3d3a5d 100644 --- a/tutorials/10_Group_analysis/component_clustering_tools.md +++ b/tutorials/10_Group_analysis/component_clustering_tools.md @@ -309,13 +309,13 @@ Press *Ok*. The cluster editing interface detailed in one of the following secti Optimal Kmeans clustering ----------------- -We have recently added a new feature to the *kmeans* clustering algorithm in EEGLAB. This feature allows you to find the optimal number of clusters for your data. To use this feature, you need to have the [MATLAB Statistics and Machine Learning Toolbox](https://www.mathworks.com/products/statistics.html) installed. +We have recently added *Optimal Kmeans* algorithm to the `pop_clust` function. This feature allows you to find the optimal number of clusters for your data. To use this feature, you need to have the [MATLAB Statistics and Machine Learning Toolbox](https://www.mathworks.com/products/statistics.html) installed. To use this feature, you need to select the *Optimal Kmeans* option from the *Clustering algorithm* dropdown menu. Then, you need to input a range of cluster numbers to test (in the screenshot below minimum is set to 10 and maximum is set to 30). The algorithm will then test the clustering for each number of clusters in the range and chooses the optimal number of clusters based on the *silhouette* score. The *silhouette* score is a measure of how similar an object is to its own cluster compared to other clusters. The optimal number of clusters is the one that maximizes the *silhouette* score. Read more about the *silhouette* score from the [MATLAB documentation](https://www.mathworks.com/help/stats/clustering.evaluation.silhouetteevaluation.html). -*Recommended number of clusters:* Following the estimated number of clusters above, we recommend setting the lower bound of the cluster range to the half the average number of components per subject. For example, if you have 20 components per subject, set the lower bound to 10. Similarly, set the upper bound to 1.5 times the average number of components per subject. For example, if you have 20 components per subject, set the upper bound to 30. If the returned number of clusters is at its lower or upper bound, consider expanding the range. We also strongly recommend using the option to separate outliers. +*Recommended number of clusters:* Following the rationale for estimated number of clusters above, we recommend setting the lower bound of the cluster range to the half the average number of components per subject. For example, if there are 20 components per subject, set the lower bound to 10. Similarly, set the upper bound to 1.5 times the average number of components per subject. For example, for 20 components per subject, set the upper bound to 30. If the returned number of clusters is at its lower or upper bound, consider expanding the range. We also strongly recommend using the option to separate outliers. -![](/assets/images/studyclust5.png) +![](/assets/images/studyclust14.png) Other clustering methods ----------------- From cd54574b9ffb9704642537f044c1a44edc42d9a3 Mon Sep 17 00:00:00 2001 From: Seyed Yahya Shirazi Date: Wed, 28 Aug 2024 14:40:39 -0700 Subject: [PATCH 3/5] grammar adjustments --- tutorials/10_Group_analysis/component_clustering_tools.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/10_Group_analysis/component_clustering_tools.md b/tutorials/10_Group_analysis/component_clustering_tools.md index 7d3d3a5d..652e8945 100644 --- a/tutorials/10_Group_analysis/component_clustering_tools.md +++ b/tutorials/10_Group_analysis/component_clustering_tools.md @@ -309,11 +309,11 @@ Press *Ok*. The cluster editing interface detailed in one of the following secti Optimal Kmeans clustering ----------------- -We have recently added *Optimal Kmeans* algorithm to the `pop_clust` function. This feature allows you to find the optimal number of clusters for your data. To use this feature, you need to have the [MATLAB Statistics and Machine Learning Toolbox](https://www.mathworks.com/products/statistics.html) installed. +We have recently added **Optimal Kmeans** algorithm to the `pop_clust` function. This feature allows you to find the optimal number of clusters for your data. To use this feature, you must have the [MATLAB Statistics and Machine Learning Toolbox](~https://www.mathworks.com/products/statistics.html~) installed. -To use this feature, you need to select the *Optimal Kmeans* option from the *Clustering algorithm* dropdown menu. Then, you need to input a range of cluster numbers to test (in the screenshot below minimum is set to 10 and maximum is set to 30). The algorithm will then test the clustering for each number of clusters in the range and chooses the optimal number of clusters based on the *silhouette* score. The *silhouette* score is a measure of how similar an object is to its own cluster compared to other clusters. The optimal number of clusters is the one that maximizes the *silhouette* score. Read more about the *silhouette* score from the [MATLAB documentation](https://www.mathworks.com/help/stats/clustering.evaluation.silhouetteevaluation.html). +To use this feature, select the **Optimal Kmeans** option from the **Clustering algorithm** dropdown menu. Then, you need to input a range of cluster numbers to test (in the screenshot below, the minimum is set to 10, and the maximum is set to 30). The algorithm will then test the clustering for each number of clusters in the range and choose the optimal number of clusters based on the **silhouette** score. The **silhouette** score is a measure of how similar an object is to its own cluster compared to other clusters. The optimal number of clusters is the one that maximizes the **silhouette** score. Read more about the **silhouette** score from the [MATLAB documentation](~https://www.mathworks.com/help/stats/clustering.evaluation.silhouetteevaluation.html~). -*Recommended number of clusters:* Following the rationale for estimated number of clusters above, we recommend setting the lower bound of the cluster range to the half the average number of components per subject. For example, if there are 20 components per subject, set the lower bound to 10. Similarly, set the upper bound to 1.5 times the average number of components per subject. For example, for 20 components per subject, set the upper bound to 30. If the returned number of clusters is at its lower or upper bound, consider expanding the range. We also strongly recommend using the option to separate outliers. +**Recommended number of clusters:** Following the rationale for the estimated number of clusters above, we recommend setting the lower bound of the cluster range to half the average number of components per subject. For example, if there are 20 components per subject, set the lower bound to 10. Similarly, set the upper bound to 1.5 times the average number of components per subject. For example, for 20 components per subject, set the upper bound to 30. If the returned number of clusters is at its lower or upper bound, consider expanding the range. We also strongly recommend using the option to separate outliers. ![](/assets/images/studyclust14.png) From 6152fee0c65245979017f66e554140158f991466 Mon Sep 17 00:00:00 2001 From: Seyed Yahya Shirazi Date: Wed, 28 Aug 2024 14:41:38 -0700 Subject: [PATCH 4/5] check URLs --- tutorials/10_Group_analysis/component_clustering_tools.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/10_Group_analysis/component_clustering_tools.md b/tutorials/10_Group_analysis/component_clustering_tools.md index 652e8945..f869e696 100644 --- a/tutorials/10_Group_analysis/component_clustering_tools.md +++ b/tutorials/10_Group_analysis/component_clustering_tools.md @@ -309,9 +309,9 @@ Press *Ok*. The cluster editing interface detailed in one of the following secti Optimal Kmeans clustering ----------------- -We have recently added **Optimal Kmeans** algorithm to the `pop_clust` function. This feature allows you to find the optimal number of clusters for your data. To use this feature, you must have the [MATLAB Statistics and Machine Learning Toolbox](~https://www.mathworks.com/products/statistics.html~) installed. +We have recently added **Optimal Kmeans** algorithm to the `pop_clust` function. This feature allows you to find the optimal number of clusters for your data. To use this feature, you must have the [MATLAB Statistics and Machine Learning Toolbox](https://www.mathworks.com/products/statistics.html) installed. -To use this feature, select the **Optimal Kmeans** option from the **Clustering algorithm** dropdown menu. Then, you need to input a range of cluster numbers to test (in the screenshot below, the minimum is set to 10, and the maximum is set to 30). The algorithm will then test the clustering for each number of clusters in the range and choose the optimal number of clusters based on the **silhouette** score. The **silhouette** score is a measure of how similar an object is to its own cluster compared to other clusters. The optimal number of clusters is the one that maximizes the **silhouette** score. Read more about the **silhouette** score from the [MATLAB documentation](~https://www.mathworks.com/help/stats/clustering.evaluation.silhouetteevaluation.html~). +To use this feature, select the **Optimal Kmeans** option from the **Clustering algorithm** dropdown menu. Then, you need to input a range of cluster numbers to test (in the screenshot below, the minimum is set to 10, and the maximum is set to 30). The algorithm will then test the clustering for each number of clusters in the range and choose the optimal number of clusters based on the **silhouette** score. The **silhouette** score is a measure of how similar an object is to its own cluster compared to other clusters. The optimal number of clusters is the one that maximizes the **silhouette** score. Read more about the **silhouette** score from the [MATLAB documentation](https://www.mathworks.com/help/stats/clustering.evaluation.silhouetteevaluation.html). **Recommended number of clusters:** Following the rationale for the estimated number of clusters above, we recommend setting the lower bound of the cluster range to half the average number of components per subject. For example, if there are 20 components per subject, set the lower bound to 10. Similarly, set the upper bound to 1.5 times the average number of components per subject. For example, for 20 components per subject, set the upper bound to 30. If the returned number of clusters is at its lower or upper bound, consider expanding the range. We also strongly recommend using the option to separate outliers. 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