Analyzing EEG Data with Machine and Deep Learning: A Benchmark
This provides a practical guide for researchers in EEG analysis, though it is incremental as it benchmarks existing methods on a specific data type.
The paper tackles the problem of selecting effective models for EEG signal classification by proposing the first benchmark comparing four widespread machine and deep learning models, finding that certain models serve as good starting points for development.
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.