layers = [ ...
sequenceInputLayer(48) % Assuming sEMG_channels = 48
lstmLayer(numHiddenUnits, 'OutputMode', 'last')
reshape([1, 1, numHiddenUnits]) % Reshape LSTM output to have a spatial dimension
convolution1dLayer(3, 25, 'Padding', 'same')
batchNormalizationLayer
reluLayer
dropoutLayer(0.4)
convolution1dLayer(3, 10, 'Padding', 'same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(23)
softmaxLayer
classificationLayer];
Check this.
When you set the OutputMode of an LSTM layer to 'last', the output of that layer will be a 2D matrix of size numObservations x numHiddenUnits. Here, numObservations is the number of sequences in your input data, and numHiddenUnits is the number of hidden units in the LSTM layer.
In your case, since your input data XTrain is a 1000x1 cell array, where each cell contains a 48x30000 matrix (48 channels and 30000 time points), the output of the lstmLayer(numHiddenUnits,'OutputMode','last') will be a 2D matrix of size 1000 x numHiddenUnits.
To connect this LSTM output to a 1D convolutional layer, you need to reshape the output to have a valid spatial dimension. You can do this by adding a permute layer or a reshape layer after the LSTM layer.
