You can't remove the NaN values without changing the shape of the data. You could replace the NaN values with a different place holder but if you remove them, the 64x250 shape will change. Usually the size of matrices means something (e.g. number of observations, conditions, groups, etc). But after you remove the NaN values, you lose that information.
Nevertheless, there's how to remove the NaN values. Assuming the NaNs have no pattern, the result will be a row vector of non-nan values and the length of the row is determined by the number of non-nan values.
% Create fake data
C = cell(49,26);
for i = 1:numel(C)
x = round(rand(64,250),1);
C{i} = {x./x};
end
% Show sample of data
C
C = 49×26 cell array
{1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell}
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{1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell}
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{1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell}
{1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell}
C{1}
ans = 1×1 cell array
{64×250 double}
% Function to count total number of NaNs
nanCountFcn = @(C)sum(isnan(cell2mat([C{:}])), 'all');
nanCountFcn(C) % number of NaN values
ans = 1018165
% Delete NaN values
% All 64x250 arrays will become row vectors
% of various lengths depending on the number of
% NaNs removed
for i = 1:numel(C)
C{i}{:}(isnan(C{i}{:})) = [];
end
% Count remaining NaNs
nanCountFcn(C)
ans = 0
% Show sample of updated data
C
C = 49×26 cell array
{1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell}
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{1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell} {1×1 cell}
C{1}
ans = 1×1 cell array
{[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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