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.

Analyzing EEG Data with Machine and Deep Learning: A Benchmark / Avola, D.; Cascio, M.; Cinque, L.; Fagioli, A.; Foresti, G. L.; Marini, M. R.; Pannone, D.. - 13231:(2022), pp. 335-345. (Intervento presentato al convegno 21st International Conference on Image Analysis and Processing, ICIAP 2022 tenutosi a Lecce, Italia) [10.1007/978-3-031-06427-2_28].

Analyzing EEG Data with Machine and Deep Learning: A Benchmark

Avola D.
Primo
;
Cascio M.;Cinque L.;Fagioli A.;Foresti G. L.;Marini M. R.;Pannone D.
2022

Abstract

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.
2022
21st International Conference on Image Analysis and Processing, ICIAP 2022
Benchmark; Brain computer interfaces (BCI); Classification; Deep learning; Electroencephalography (EEG)
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Analyzing EEG Data with Machine and Deep Learning: A Benchmark / Avola, D.; Cascio, M.; Cinque, L.; Fagioli, A.; Foresti, G. L.; Marini, M. R.; Pannone, D.. - 13231:(2022), pp. 335-345. (Intervento presentato al convegno 21st International Conference on Image Analysis and Processing, ICIAP 2022 tenutosi a Lecce, Italia) [10.1007/978-3-031-06427-2_28].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1639443
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