So I wanted to split the 1X15 cell to different cells.

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CalebJones 2019-9-18

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评论： CalebJones 2019-9-23

采纳的回答： Guillaume

result.mat

Yes this is a screenshot of a screenshot.

So I have a 1x15 cell

Each (cell)column is one channel so a total of 15 channels.

2nd screenshot is a 1st channel with 2 states.

So now i wanted to put each state of one channel into a seperate matrix or a cell(preferably a cell).The state changes from rest to active every 200th iteration and vice versa.

So new cell of channel 1 would contain 1 cell for with values for rest block and next cell for active block and so on.

so repeat this for 15 channels.

I have attached mat file of my data.

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CalebJones 2019-9-18

Guillaume

I wanted to split it because i wanted calculate mean,slope,kurtosis,peak which i will use as my feature set for training my model.

CalebJones 2019-9-18

Guillaume any thoughts?

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Guillaume 2019-9-18

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If you want to perform group statistics, the simplest is indeed to put everything in just one table:

allresults = vertcat(result{:});  %concatenate everything into one table
allresults.channel = repelem((1:numel(result))', cellfun(@height, result));  %and add a channel column

It's then trivial to calculate group statistics with groupsummary or grpstats:

stats = groupsummary(allresults, {'state', 'channel'}, {'mean', @kurtosis});  %not sure what you mean by slope and peak

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Guillaume 2019-9-19

编辑：Guillaume 2019-9-19

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No, I've given you an example above:

stats = groupsummary(allresults, {'state', 'channel', 'eventid'}, {'mean', @kurtosis});  %not sure what you mean by slope and peak

Since I don't have the stats toolbox (with kurtosis), I can't show you the result for that, but here is an example with a made-up function:

function s = slope(values)
    %fit a linear polynomial to the data, and return the slope of the line
    if ~isempty(values)
        p = polyfit(1:numel(values), values', 1);
        s = p(1);
    else
        s = NaN;
    end
end

and using it on your data:

%merge all into one table with extra columns:
allresults = vertcat(result{:});  %concatenate everything into one table
allresults.channel = repelem((1:numel(result))', cellfun(@height, result));  %and add a channel column
allresults.eventid = cumsum([1; diff(findgroups(allresults.state)) ~= 0]);
%get stats: mean and slope
stats = groupsummary(allresults, {'state', 'channel', 'eventid'}, {'mean', @slope});  %not sure what you mean by slope and peak
stats.Properties.VariableNames{end} = 'slope_HbO'; %better variable name that what matlab autogenerate

head(stats) returns:

>> head(stats)  %first 8 rows
ans =
  8×6 table
    state     channel    eventid    GroupCount        mean_HbO              slope_HbO     
    ______    _______    _______    __________    _________________    ___________________
    active       1          2          200        -7.67231127895478     -0.091165802772305
    active       1          4          200         35.5962270614642      0.076534807760347
    active       1          6          197        -19.7591859046008      0.252152452160729
    active       2          8          200        -15.9692390786319      0.235640586381289
    active       2         10          200         20.5796697869384    -0.0664683822345007
    active       2         12          197        -17.5921264661321      0.200172452731579
    active       3         14          200         7.17042032876279    -0.0507776796191182
    active       3         16          200          12.637946834818     -0.218112096344478