Analyzing EEG Data with Machine and Deep Learning: A Benchmark

Autor:	Avola, Danilo, Cascio, Marco, Cinque, Luigi, Fagioli, Alessio, Foresti, Gian Luca, Marini, Marco Raoul, Pannone, Daniele
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: conference, 11 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.10009 Zobrazit plný text záznamu View this record from Arxiv