  Nowadays, machine and deep learning techniques are widely used in different
areas, ranging from economics to biology. In general, these techniques can be
used in two ways: trying to adapt well-known models and architectures to the
available data, or designing custom architectures. In both cases, to speed up
the research process, it is useful to know which type of models work best for a
specific problem and/or data type. By focusing on EEG signal analysis, and for
the first time in literature, in this paper a benchmark of machine and deep
learning for EEG signal classification is proposed. For our experiments we used
the four most widespread models, i.e., multilayer perceptron, convolutional
neural network, long short-term memory, and gated recurrent unit, highlighting
which one can be a good starting point for developing EEG classification
models.
