ICA - Science topic

In my experiment, I have two groups of subjects (20 in total, 10 in each group) and I want to compare the activity of DMN between two groups. And for each subject, I have four BOLD fMRI scans.

My question is that should I input all of them (80 scans) into Melodic and average these repeated scans after doing dual regression or for each subject combine 4 scans into one scan with a long timeseries before doing ICA?

Any suggestion will be appreciated.

Hi all,

I have a MEG data set which is about 5 minutes long for each subject. I want to detect and correct existing artifacts using the ICA approach using the Brainstorm toolbox. When I performed ICA, the first component (the dominant one) has a very small amplitude, (about 1000 times smaller in comparison to other components) and looks like a DC signal. Should I remove this component? It is the first component and I want to be sure about this decision as it may be a signal, not an artifact. In the following, you can see the components.Thanks!

Hi,

I'm preprocessing some EEG datasets in an EEG-VR experiment with some neck movements.

In some of the datasets, some spindle-like alpha activities appear in most channels (mostly occipital, parietal, and frontal but less central channels). I'm not sure if they're some kind of noise maybe because of the VR headset on the EEG cap and therefore should be removed or not.

After doing ICA, these activities will also appear on some of the components, which ICLable identifies as either 100% or above 95% brain activity. I attached some photos of the channel activities and components. I'll appreciate it if you can help to be sure whether these are noise or not and if they are how should I remove them.

Thank you very much.

Hi everyone, I'm assessing one's cognitive performance based on their brain activity recorded through an electroencephalogram (Emotiv EpocX EEG headset). Recently there is a very high incidence of bad EEG quality during the recording and about 70-80% of dirty data have to be removed. The test was about 120s long and left with <30s after dirty signal removal. May I know how do I determine the minimum EEG data points in that particular test to perform ICA? And how do I confirm if the remaining data is representative due to very limited ''clean'' data after noise removal?

Thank you.

Although I went through the literature, I couldn't understand the identification of the bifurcation point between ECA, ICA and the methodology of filament insertion. It will be helpful for me if you can share a video of the procedure

Hello,

I have 19 EEG channels and use ICA for EEG artifact removal.

Which criteria can be used to remove or accept EEG channels using EEGLAB?

Thanks

Hi, I am currently working on a project which requires me to implement the ICA algorithm in real time, to be specific, I am trying to separate the noise from the audio. When I perform the algorithm in offline, it works fine despite the amplitude of the separated audio is a bit soft. However, when I implement it in real-time, the separated audio becomes very soft. Any source code that I could refer to solve this kind of problem. Thanks.

Hi!

I am currently doing a intercoder reliability test for my phd. I have conducted 17 semi-structured interviews, and segmented/coded them in atlas.ti.

Each code has a upper category it belongs to, for example subcodes “parent”, “uncle”, “child” belong to the upper category “family”. I have allowed sub-codes within the same upper category to overlap while coding, tagging for example both “parent” and “child” in instances where they are both mentioned in the same segment.

I have had a second coder (me being the first) code a sample of the data for a ICR test.

After coding, I realized Cohen’s Kappa and Krippendorff’s Alpha as methods do not allow the usage of more than one code per category for each coded segment. I should have checked this prior to coding, and i now have to work with what i have.

So, I am now asking for advice on which method, or methods to apply in this situation? I am opting to use two, but I don’t know which one’s to use.

I could use a “Fuzzy Kappa” model, which is modelled after Cohen’s Kappa to suit these kinds of overlapping cases. There does not appear to be a standard interpretation of Fuzzy Kappa however. In addition, Cohen’s Kappa in itself still faces some criticism for its shortcomings, so im not completetly sure about this. Another option is for me to calculate a basic cohen’s Kappa on each code separately and then take the average of that, this is an advice I have seen on this forum earlier.

I could also go back to my data and split the codes/ redefine the upper categories to avoid overlap so I could attempt a Krippendorff’s Alhpa test. However, I am not sure if I could get the second coder to come back for splitting their codes, and redefining the categories would be counterintuitive. Krippendorff’s Alpha seems like a more reliable method, but it also has stricter requirements than my resources may take ( requires specific sample sizes, does not allow for the primary researcher/code developer to be one of 2 raters).

Any ideas of which methods to use/ combine are of great help!

I would highly appreciate if you can comment down below some leading works on ICA in the context of cascading disasters (disasters whose effects spread through space and time).

I prefer numerical modeling works.

Many thanks!

I have simulated fmri image. Also performed the ica using GIFT tool over the simulated image. I would like to know how to perform comparison between these two to know the performance level of the ica algorithm used.

Hello! I have rsfMRI data from one subject with two conditions.

I need: 1) to identify major networks 2) compare these networks between two conditions.

Do you think ICA is appropriate method to use? If yes, should I run ICA for each condition separately and should I restrict the number of components?

I tried to run ICA for one of the condition, and I got results with 200 components, looks like a problem with overfitting (attached). This is my first time when I work on ICA, and I do not know how to interpret it.

what is better: duplex for CCA or ICA or ECA?

I have data from 6 EEG channels (F3-A2, C3-A2, A1-A2, F4-A1, C4-A1, A2-A1) sleep data. And 1 channel for ECG and EOG, are these enough to run ICA to remove artifacts caused by EOG and ECG from the EEG channels?

Also which algorithm would be recommended to use?

Greetings RG community,

I have been working on a pipeline for the classification of EEG motor imagery signals. This is currently being done on the Giga Science MI dataset with 52 subjects, using all 64 EEG channels. The question can be isolated to my first preprocessing step involving ICA by way of Hyvarinen's fast fixed-point algorithm. If I develop spatial filters (vectors of the unmixing matrix) using only the intended training data, is it a violation of appropriate protocol if I then project all raw data (which includes testing data) on these vectors in an attempt at blind source separation? The nuanced thing that brings about concerns is that the raw data is provided in two matrices each containing LH+RS and RH+RS signals (LH = left hand; RH = right hand; and RS = resting state). If the spatial filters wL and wR were constructed using the LH and RH training data respectively, then the original raw data of the LH and RH matrices (including both training and testing data) were projected into these directions prior to the partitioning of trials, is this considered using knowledge of the classes thereby rendering the entire analysis ineffective? At first, I thought I was in the clear because class labels were not used as ICA is unsupervised, now I think it pertinent to ask someone that may have experience in this field.

My results were great under these conditions (perhaps this would be obvious). To check if I could replicate results a different way, I vertically concatenated the LH and RH training data (doubling the number of samples in comparison to the conditions described above) and developed a single matrix of spatial filters then projected all original data onto these but the results were poor, indicating a large drop in spatial resolution. Ideally, I would like to develop a single set of spatial filters that can be applied to all data indiscriminately, if anyone has any advice given the situation it would be greatly appreciated. Since this step is being done prior to the partitioning of MI trials, I have considered performing ICA on some vertically concatenated trials after partitioning and was wondering if this would yield good results as I have also read that the resting state signals contain important information for the minimization of mutual information (maximization of differential entropy), so I am uncertain with this approach. I am also in the process of replacing FastICA with SOBI, JADE, and infomax in an attempt to gain higher spatial resolution. Please excuse any off-putting terminology as I recently pivoted from functional protein dynamics recognition and prediction to BCI-EEG motor imagery classification. Feel free to share any thoughts, all advice is welcomed.

Thank you for your time,

Tyler J Grear

I have used ICA for the problem of mixing acoustic and UHF signals from partial discharges. I know that it has been used in Biomedical, Acoustic, and many other fields. Do you comment on how you have used it?

Hello, I used EEGLAB interface to run ICA and remove artifact components, after which I saved the dataset in a .mat format. When I load the set, the structure contains the file name under 'data' in .fdt format. How can I access that file? I have tried loading it separately as an ASCII, but it results in an error: 'Unknown text on line number 1 of ASCII file p1_ICA.fdt' Is there any other way to access the cleaned , post ICA, data? Thank you, Rawan

I need MATLAB, C# or R code of above mentioned models. How could I find those?

Dear all,

I recently tried to optimize my scripts to preprocess EEG data using EEGLAB2019. I found that including EOG channels in ICA significantly improved the subsequent ERP results, i.e., the classic LPP appeared. However, if I included EOG channels in ICA, the subsequent average re-reference had to also include them. If I performed average re-reference on only brain channels, the eeg_checkset would report an error ( ICA Index exceeds matrix dimensions). If I excluded EOG channels in ICA, average re-reference on only brain channels worked well, which, however, generated a bad ERP wave.

The figure attached is the ERP waves based on four-subject preprocessed data. The only difference between them is whether the EOG channels were included in ICA. ICALabel was used to automatically remove the EOG components.

Good morning!

We are planning to run an fMRI experiment on patient. I would like to add a resting state acquisition. However, to reduce to the minimum the time in the machine for the patient, I was wondering the minimal number of acquisition necessary if I am planning to analyze the data with an ICA. Moreover, I know that my group of patient will be not really big (probably around 20 so the double with my control group).

Thanks a lot your insight and feedback!

I have been working with FSL software (melodic & dual_regression) to parcellate intrinsic connectivity networks both at a group and subject level. I successfully ran dual_regression and have the outputted z-maps for each subject and component. I want to create a summary statistic for each outputted z-map so that I can compare differences across participants using a dimensional framework. I am not interested in analyzing subgroups, even though all the FSL documentation is all about group differences.

I am wondering how I could calculate a summary statistic for each z-map that represents the network strengthen/integrity of a given subject. I basically want to know how over-expressed or under-expressed is a given network is for each subject. I want to use this approach to test a hypothesis regarding individual differences in symptoms of depression. I read this in a few books and papers, but no one has explained how to quantify these subject-level networks from nifti files into numeric summary scores that could be inputted into a multiple regression.

Attached here is my group ICA output that was manually capped at 30 components (melodic_IC.nii.gz) and a couple dual regression output files (original and z-maps) from two subjects to give you an idea about what I am working with. The 3rd component is of most interest as it most resembles the default mode network. Please help someone!

Have implement neural network based mask detection algorithm with different colors of masks and that is working with 99% accuracy (around 2 fails in 850 tests) but the CNN based algorithm is too slow on boards like Raspberry Pi around 1-2 FPS at 720p. Shall I try PCA /ICA based techniques?

We have tried various modifications to our standard Western Blotting procedures now trying to detect CHOP (GADD153) in our human cell lysates after e.g. stimulation with 1 µg/l tunicamycine, but to no avail.

Do you have some helpful pointers as to how to detect CHOP?

We have used nitrocellulose and PVDF membranes in the past. Primary antibody is the recommended Santa Cruz monoclonal mouse anti-GADD153 (B-3) at 1:250. For technical reasons, we don't do the transfer on ice, however, actin signal is always strong on our membranes.

Is it a matter of amount of protein loaded? We have done roughly 20-30 µg/lane so far.

Thanks for any good ideas or working protocols!

What is mean by Eigenvalues in ENVI, when performing PCA or ICA operations in ENVI environment? And what is mean by Covariance Matrix or Correlation Matrix statistics?

would like to know if Imperialist competitive algorithm(ICA) can be used in medical diagnosis. For ex : Retinal image analysis to detect DR, AMD etc

Hi folks,

I am using 14 channels of EEG system.

In the step of artifacts removal, I wonder how to detect it for low resolution (14 electrodes) system. I’ve tried ICA, but it doesn’t give good result.

Would appreciate if you could attach any published papers/conference papers that I can follow.

Would appreciate any ideas/suggestions with it.

Thank you so much!

I am working on optimizing well placement in the condensate reservoir model using an algorithm. Any kind of code example will be appreciated.

hi

I am working on EEG's data and I am confused about some of the components. I know the eye blinking one and some others but still I have a doubt on some of them. I use adjust program but I can't trust it due to its some mistakes that I have been seen from it.

I would be so happy to help me and tell me if any of this ICA components in these 4 pictures should be removed or not? please tell me your reason. (I will put component activation picture of them if you need)

thank you

Hi friends, Can you Please tell me analysis of EEG signal using independent component analysis using MATLAB. If possible can you give me the source code for EEG analysis using ICA.

Hai friends,

I have started my research recently, working on Artifacts removal EEG signal. I have done the literature survey on PCA, ICA,CCA, EMD,EEMD. I have understood somehow. Can you please tell me how to take EEG signal as input and how to get EEG database.

Hi all,

I have this doubt that I could not solve by myself. I'm performing time-frequency analysis with EEGlab software for my master thesis and I'm not sure whether it is more correct to perform ICA and epoching in a precise order. I found some reccomendations in EEGLAB tutorial manual saying that it is advisable to perform ICA on a greater amount of data as possible. However, if I run ICA on EEGLAB before epoching, I cannot have a good visualisation of the ICA components (only the maps and components scroll). Instead, I have seen in a tutorial by Mike Cohen, that he performs epoching before ICA, so there is the possiblity to look at each component latency and frequency before rejecting. Personally, I found myself better with the second method, but I am not sure wheather it is correct or not.

I hope you can help me in this dilemma! Thank you!

I am using a 24-channel system with 19 active electrodes to record the brain activity during flanker task. I run ICA to remove ocular artifacts. Although ICA can identify 1 or 2 eye-blink components, time course and ERP image show there still task-related brain signal in these eye-blink components. My question, is there any tips that can help to keep the real brain signal?

e.g. default mode to performance on attention tasks, salience network to emotional salience tasks, and executive motor component to executive tasks.

I am working with data that is very messy such that, for a given file, the decomposition will contain a noise artifact IC that accounts for >70% of the variance. Out of curiosity, I reconstituted the data without that high variance noise IC (the reconstituted data still looked very messy), then reran the reconstituted data through ICA and achieved a much more reasonable decomposition and fairly clean reconstituted data. Any thoughts as to whether this is an allowable method for cleaning data?

I usually read something along the lines of "the fewer the better".

But what if you have multiple components that seem to contain artifacts?

I have a dataset of an EEG recording referring to a task characterized by several "rest periods", during which the recording is not interrupted. For this reason, I was thinking that removing all these periods by epoching the recording and time-locking it to my ERPs of interest would facilitate the ICA by isolating only the artifacts occurring during my time-window of interest. However, I was warned to be careful with this operation since it can cause artifacts occurring along different epochs to overlap with each other. Any suggestion about the feasibility of this approach would be greatly appreciated. Thanks

I did FSL FEAT on resting fMRI with TR=2s, 3x3x4 mm^3 resolution. What is the best way to remove noise ICA components? I tried ICA-FIX (using standard trained data) and ICA-AROMA. the results are quite different. FIX identified only couple noise components while AROMA detected a lot noise components. Any suggestion which one to choose and what is the best way to use FIX and AROMA? any other alternative beside manual removal? Thanks.

Hi,

is there an existing Python implementation of Fourier ICA?

I only found the original MatLab implementation of Hyvärinen at

Thanks!

I've collected EEG data using 24 channels system. Twenty-three channels (19 active EEG, 1 VEOG, 1HEOG, 1 right-earlobe) were referenced by left-earlobe, and one channel used for EOG bipolar (V-EOG referenced by H-EOG). I'm trying to clean my data using ICA.

My question, what is the minimum number of EEG channels that required to properly run ICA?

and, is it better to run ICA with all the channels (24) or i need to remove the bipolar EOG channel that not sharing the same common reference?

Thank you,

Hi everybody,

I am using a 32 electrodes EEG from Brain Products GmbH. The electrodes of this device are all connected to a splitterbox, that in turn is connected to the amplifier. I have not the adapter required for the EOG electrodes to be connected to the amplifier, then I will have to use the cap's electrodes as EOG.

Can I use these electrodes as EOG? The reference for the EOG will then be the same as the one for the electrodes on the scalp? How many of these electrodes should I use as EOG? Can I use this kind of EOG for running an ICA for eye artefacts removal? Or can ICA be also run without EOG?

Thank you very much,

Alessandro

Hi,

I am trying to find templates/mask of the DMN, saliency network and attention network.

I did an ICA analysis and want to be able to classified my different ICA in specific networks...and eventually quantify the overlap.

Does this kind of template/mask exist? or do I have to create them myself?

Thanks a lot for your help!

Hi,

I have a question about EEG signal processing in EEGLAB. I am not sure whether the preprocessing steps that I did are suitable. It would be great if someone can look at it and maybe give me tips on how I could improve my signal preprocessing.

I did a relaxation task and a attention test using the Emotiv EPOC+ device. Now, I want to find out if the ratio of the band power alpha/theta are different in these two phases. For that reason I processed the raw EEG signal as followed:

1. Import raw data

2. read channel locations

3. FIR filter: High-pass filter at 0.16 Hz to remove background signal and DC offset, Notch filter at 50 Hz to remove the interference

4. Scrolling the data and rejecting clearly bad stretches but ignore the eye blinkings

5. ICA

6. Reject components

7. Scroll the data and remove signals which exceeds 100 microvolts interval

8. FFT

9. Filtering the alpha and theta band

Thanks for your help

Hello,

I have a question regarding synchronization of artifact rejection in eeglab. I use the version: "eeglab 14_1_1b" (MATLAB plugin)

I work with epoched data. To find artifacts like blinks and eye movements/saccades I have used the built-in ICA function and followed the recommended guidelines from Chaumon et al. 2015 to pick out components. Furthermore I have used the "simple voltage treshold" til sort out any voltage below -75 microvolt and voltage above 75 microvolt.

My issue arises when I want to synchronize the artifact info in EEG and EVENTLIST. This is a must do step in order for me to compute the average ERPs. eeglab won't let me do this, and it brings me this exact message when i try to synchronize:

"It looks like you have deleted some epochs from your dataset. At the current version of ERPLAB, artifact info synchronization cannot be performed in this case. So, artifact info synchronization will be skipped, and the corrosponding averaged ERP will rely on the information at EEG.event only. Do you want to continue anyway

(yes, no)." I need to find a solution, so that every rejected trails and removed components will be removed from the dataset when averaging the ERPs.

I have not been able to find any version of eeglab which seems to solve the problem.

Any suggestions?

Best regards

Morten

I want to know how to extract the most relevant variables of a model using ICA.

When using PCA I can just verify the eigenvalues and select the new variables based on its variance. If I have 100 variables, but only 60 have relevant variances I can ignore the other 40. How does this procedure work in ICA ?

Dear all,

It seems my data are suffering from alpha distortion. It is possible to reduct it from some data with the help of ICA but for half of the participant ICA does not work that well.

So for now I tried to reject more components than usual (15-18 out of 64 ICs) and run the ICA again if it can catch the alpha. But it doesn't work.

In a nutshell, I wonder what are the best ways to reduce the alpha disturtion from data

Note: I also heart that PARAFAC seems to work better then ICA for reduction of Alpha but I cannot find a way to apply PARAFAC with EEGLAB GUI

Best,

Behcet

Hello everyone,

I wonder how many ICA component do you generally eleminate aproximately? Do you think eleminating 25 out of 64 component sounds reasonable.

ICA(Independent Component Analysis) is a computational method for separating a multivariate signal into additive subcomponents that they are statistically independent from each other. If so, it is necessary that there are no similar ICs between each other, because there're independent from each other. But I saw very similar ICs in my part of EEG processing. How do we interpret it?

* I attached a example for better understanding. In pictures, these look very similar.

A. I'm trying to separate signals of different artefacts from brain signals given 8 sensor outputs(S) of a patient using ICA. However, the ICA algorithm also returns 8 source signals (S') which look the same as the input and hence I can't differentiate between signal and noise. How can I solve for this? Is there a preprocessing step to be done before feeding raw data to ICA?

I'm using Python and its packages for the same.

B. Of the 8 sensor outputs(S) I suspect some of them may be dependent causing the problem in (A). I suspect even in EEG recordings, a similar issue arises that electrode signals may be dependent. How can I solve for these dependent sources?

Hello,

We are currently working on an ischemic stroke model on mice using intraluminal monofilament occlusion (using Koizumi's method). We use laser doppler to confirm the decrease in cerebral blood flow during MCA occlusion, and then the increase in cerebral blood flow during reperfusion. My concern is this: how can we determine if a resulting infarct as seen by TTC/cresyl violet staining is due to transient occlusion of the MCA (typically for 1 hour), or if it is due to permanent occlusion that may have developed post reperfusion (development of a thrombus due to arterial wall damage during surgery). We became aware of this issue after performing MRA on one of our mice post occlusion and then 24 hours post reperfusion; the MCA and part of the ICA had minimal collateral blood flow in the first image, then absolutely no blood flow in the 24 hour image. We could use laser doppler again to visualize cerebral blood flow just before animal sacrifice at 24 hours post reperfusion, however, it would be almost impossible to place the doppler probe in the exact same position as the initial recording. Any suggestions? Thank you very much.

Hey,

No error message, no warning. The desktop is simply restarting itself... the script is working fine on my laptop. Windows' log says nothing about the crash event.

I tried several combinations of matlab (2014a, 2017b) and eeglab (12, 14...). This is a win10 x64 operation system (similar to my laptop).

"EEG = pop_runica(EEG, 'extended',1,'interupt','on','icatype','runica'); "

Since Biological Parametric Mapping (BPM) is no more supported, I would like to find another tool to combine several brain images acquired for each subjects or different groups.

I saw fusion ICA but it is not convenient for my data....

any other idea ?

Thank you

Isabelle

I've got two raw data sets that has been gotten using OpenBCI.

I'm trying to understand the data from the channel spectra and maps(spectopo).

And, what does running ICA do? The components and the graphs, what do they mean?

I want to epoch the continuous EEG data of 8 minutes long into non overlapping epoch window in EEGLAB without any event information .When I select the 'Extracting Epoch " the GUI will pop up to set the Event information first .After epoch of the data I want to run ICA on the data to get the clean data .Kindly help me in this regard

I have obtaind 9 group ICs using GIFT. Now I want to extract each ICs of each subject using group ICs.

Subject matrx(contains voxels of each subject)=Mixing matrix(contains weight of each subject's ICA for each group component)*source matrix (voxels of each component)

I did group ICA of 18 Subjects resting state fmri data now i want to regress the ICA output with smith 10 networks, so how do i design glm for this.

Currently ICA is applied for functional data. But...I feel this could be little debating.

This an example paper

Hi all,

I would like to correlate several resting-state fMRI components extracted by ICA (using GIFT) with different behavioral measures related to my participants (as tests or questionnaires). Does anyone know the correct procedure?

Thank you in advance,

Edoardo

Let's say I have 3 signals of specific brain areas that are linearly mixed, which I recorded with 3 electrodes plus a reference ground. I used FASTICA to separate the signals and I get 3 new separated signals. However I don't know which signal corresponds to each brain area, since the waveform resemblance with the original measurements is not obvious. I tried calculating the absolute value of the correlation between separated and measured signals to find which combination had a maximum correlation and this way classify the separated signals into their correct measured signal, however this does not always work. Does anyone have a suggestion or know another approach to solving this?

I doing signal processing and classification. May i know if i have 4 input signal, if there i combine 4 signal in one? or i need used something like ICA? or i add all 4 signal together before proceed with cnn? Or is that any other method i can do for multiple input of CNN.

I did separate ICA analysis on 2 gropus (gift ICA), found components having highest correlations with DMN.

Should I next select the components and do one sample t-test in SPM on both groups separately (for thresholding) and then compare the results with two sample t-test?

I will be very thankful if anyone can help me.

I am using FastICA to unmix some measurements. Although my initial measurements are positive, I obtain independent components that has positive and negative signals.

When I made a search about it I only found a forum entry:

"...The ICA components are estimated "up" to a permutation, a scale and a sign factor. That means the ICA components are NOT intrisically ordered, are "convetionally" scaled to spatial z-scores and the sign does not carry any "absolute" information"

Source: http://www.brainvoyager.com/ubb/Forum4/HTML/000613.html

Can I do some operations on the results like taking the absolute value or shifting everything up by subtracting the minimum? In the case of shifting, the whole background (which supposed to be zero) takes a positive value.

Is there a way to force output to be positive values only? Or why those polarity occurs. I would be glad if someone could help me to interpret the results.

in some parts of applying ICA to an EEG data we have blind source seperation problem. how can we solve this problem?

Hello 

I have been trying MELODIC group analysis and get error after completing and didn't get final group ICA:

child process exited abnormally

> while executing

> "fsl:exec "${FSLDIR}/bin/feat ${fsfroot}.fsf -D $FD -I $session -prestats" -b $howlong -h $ID -N feat2_pre -l logs "

> invoked from within

> "if { $done_something == 0 } {

>

> if { ! $fmri(inmelodic) } {

> if { $fmri(level) == 1 } {

> for { set session 1 } { $session <= $fmri(mult..."

> (file "/usr/local/fsl/bin/feat" line 387)

 

 

When the MELODIC start, then I get this message

Feat main script

/bin/cp /tmp/feat_0y46We.fsf design.fsf

mkdir .files;cp /usr/share/fsl/5.0/doc/fsl.css .files;cp -r

/usr/share/fsl/5.0/doc/images .files/images

Please let me know how to reslove this problem.

Thanks and regards

Sadhana

I do the data analysis in EEGLAB and want to do ICA to remove the artifact. As the EEG data is recorded in .bdf, so I have two questions:

1. There are no reference in the recorded data and when I import EEG data into EEGLAB, I had to chose one channel as the ref, this may cause the rank-deficient;

2. I had to deal with the bad channels before I do the ICA, but I find if I do the interpolation before ICA, the rank of the data also reduced (interpolate one channel make the rank-1).

As rank deficient will make mistake for ICA result, so does someone konw how to deal with these two problems? Thanks.

Once a professor mentioned us that certain algorithms for dimensionality reduction and visualization work for certain types of data. Many of us know that, for example, PCA, ICA, t-SNE, autoencoders etc work well in continuous data type. But what if you have a combination of categorical, categorical-ordinal data, binary and continuos data... what are the correct steps and algorithm to manage such combination of information or by separate (categorical data, binary, categorical-ordinal data)?

Do the previous algorithms work on well on these type of mix data types? The one-hot encoding is enough to threat the categorical data and then use the respective transformations? How we treat categorical-ordinal data... do we apply one-hot encoding and then transformation techniques or make the assumption as it is a continuous data?

I am looking for a library to be integrated in LabWINDOWS CVI.

Hi experts

I want to compare ICA methods using ICASSO in Gift toolbox for rsfMRI. How can I find out what parameter settings are appropriate for each method?

I run 6 methods with same parameter setting and as the result, some of them were good some were bad and some of them were irrational. I attached a picture that shows some of my results. Please help me find out.

regards

dual_regression is a powerful script for extracting time series data for each ICA maps in RSN analysis. Can we use dual_regression for task-based fMRI studies? Another thing can we use a special cortical mask instead of ICA maps files. Any ideas

I have an EEG data set which is about 5 minutes long for each subject. I want to detect and correct existing artifacts using ICA approach. I can apply this method on the whole data of each person or first epoch this data and then apply ICA on each epoch separately. Which one is more accurate? 

Hi all,

I used intependent component analysis to sepereate speech from noise.i used 2 microphones and two sources (speech and noise) . I got improvement in SNR when I used ICA but when I used ICA in speaker verification . I got bad performance compared with noisy test signal. Does any one used ICA to remove noise and used the enhanced speech after ICA in speaker verification?

 Can rythm isolation ( mu and beta ) be done on