1D convolution for uni-variate data Every one. I have EEG dataset with 80 subjects, 3072 data points and 100 trials. This a univariate data, it mean there is only one channel. I am confused how to feed this data to convolution neural network.
 Most of blog and tutorial deals with multivariate data. But i have univariate and i am clueless how to move ahead
I believe that the following image (original link) will be helpful to understand. 1D convolutions work exactly the same way as 2D convolutions, the main difference is how the filter/mask is applied. 1D convolution is intuitively like a sliding window of a certain width.

Many packages like Keras or PyTorch have native 1D convolution function/modules, so I would recommend checking their documentation and perhaps source code for deeper understanding.