Training Data type error for a CNN using trainnet function

Trying to use a convolution1dLayer for my sequence input data put when I try to train it i get the error:
"Error using trainnet
Invalid targets. Network expects numeric or categorical targets, but received a cell array."
I’ve looked at many exemples of how the data must be structed but even if is in the same format, it doesn’t work.
For the predictors I’m doing a test with only 4 observations, each one with 4 features and 36191 points:

For the targets there are also for observations with only one target each and also 36191 points:

I can’t understand why it doesn’t accept it, like I said, its equal to many other exemples. I leave down here the code for the CNN-LSTM network and the trainnet function:
lgraph = layerGraph();
tempLayers = [
sequenceInputLayer(4,"Name","input")
convolution1dLayer(4,32,"Name","conv1d","Padding","same")
globalAveragePooling1dLayer("Name","gapool1d")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = lstmLayer(25,"Name","lstm");
lgraph = addLayers(lgraph,tempLayers);

tempLayers = lstmLayer(25,"Name","lstm_1");
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
concatenationLayer(1,2,"Name","concat")
lstmLayer(55,"Name","lstm_2")
dropoutLayer(0.5,"Name","drop")
fullyConnectedLayer(1,"Name","fc")
sigmoidLayer("Name","sigmoid")];
lgraph = addLayers(lgraph,tempLayers);

% clean up helper variable
clear tempLayers;

lgraph = connectLayers(lgraph,"gapool1d","lstm");
lgraph = connectLayers(lgraph,"gapool1d","lstm_1");
lgraph = connectLayers(lgraph,"lstm","concat/in1");
lgraph = connectLayers(lgraph,"lstm_1","concat/in2");

plot(lgraph);

epochs = 800;
miniBatchSize = 128;
LRDropPeriod = 200;
InitialLR = 0.01;
LRDropFactor = 0.1;
valFrequency = 30;

options = trainingOptions("adam", …
MaxEpochs=epochs, …
SequencePaddingDirection="left", …
Shuffle="every-epoch", …
GradientThreshold=1, …
InitialLearnRate=InitialLR, …
LearnRateSchedule="piecewise", …
LearnRateDropPeriod=LRDropPeriod, …
LearnRateDropFactor=LRDropFactor, …
MiniBatchSize=miniBatchSize, …
Plots="training-progress", …
Metrics="rmse", …
Verbose=0, …
ExecutionEnvironment="parallel");

CNN_LTSM = trainnet(trainDataX, trainDataY, dlnetwork(lgraph),"mse",options);
using version 2023bTrying to use a convolution1dLayer for my sequence input data put when I try to train it i get the error:
"Error using trainnet
Invalid targets. Network expects numeric or categorical targets, but received a cell array."
I’ve looked at many exemples of how the data must be structed but even if is in the same format, it doesn’t work.
For the predictors I’m doing a test with only 4 observations, each one with 4 features and 36191 points:

For the targets there are also for observations with only one target each and also 36191 points:

tempLayers = lstmLayer(25,"Name","lstm");
lgraph = addLayers(lgraph,tempLayers);

tempLayers = lstmLayer(25,"Name","lstm_1");
lgraph = addLayers(lgraph,tempLayers);

% clean up helper variable
clear tempLayers;

plot(lgraph);

epochs = 800;
miniBatchSize = 128;
LRDropPeriod = 200;
InitialLR = 0.01;
LRDropFactor = 0.1;
valFrequency = 30;

CNN_LTSM = trainnet(trainDataX, trainDataY, dlnetwork(lgraph),"mse",options);
using version 2023b Trying to use a convolution1dLayer for my sequence input data put when I try to train it i get the error:
"Error using trainnet
Invalid targets. Network expects numeric or categorical targets, but received a cell array."
I’ve looked at many exemples of how the data must be structed but even if is in the same format, it doesn’t work.
For the predictors I’m doing a test with only 4 observations, each one with 4 features and 36191 points:

For the targets there are also for observations with only one target each and also 36191 points:

tempLayers = lstmLayer(25,"Name","lstm");
lgraph = addLayers(lgraph,tempLayers);

tempLayers = lstmLayer(25,"Name","lstm_1");
lgraph = addLayers(lgraph,tempLayers);

% clean up helper variable
clear tempLayers;

plot(lgraph);

epochs = 800;
miniBatchSize = 128;
LRDropPeriod = 200;
InitialLR = 0.01;
LRDropFactor = 0.1;
valFrequency = 30;

CNN_LTSM = trainnet(trainDataX, trainDataY, dlnetwork(lgraph),"mse",options);
using version 2023b deep learning, cnn, data, neural network, machine learning MATLAB Answers — New Questions

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