Continuous electroencephalographic signal automatic classification using Deep Differential Neural Networks

D. Llorente-Vidrio, M. Ballesteros, D. Cruz, I. Salgado, I. Chairez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This manuscript presents an algorithm to classify continuously electroencephalographic (EEG) signals based on deep differential neural networks (DDNNs). The learning laws are obtained by the second stability method of Lyapunov that requires the solution of a set of matrix differential equations. The robustness of this technique allows the analysis and classification of bio-signals like EEG signals. The EEG signals have complex dynamics and they are strongly affected by noises in the measurements and a high degree of variability between different studies in patients. The main strength of DDNNs is their feedback property, which allows them to work with the time-dependent variation of the EEG signals. The DDNNs are tested in a database constituted of EEG signals acquired from a study made in ten volunteers. The study consisted of the acquisition of EEG measurements of the volunteers recognizing geometrical figures appearing in a graphic user interface. The DDNN obtained better performance than a single layer differential neural network and a convolutional neural network.

Idioma originalInglés
Título de la publicación alojada7th International Conference on Control, Decision and Information Technologies, CoDIT 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas358-363
Número de páginas6
ISBN (versión digital)9781728159539
DOI
EstadoPublicada - 29 jun 2020
Evento7th International Conference on Control, Decision and Information Technologies, CoDIT 2020 - Prague, República Checa
Duración: 29 jun 20202 jul 2020

Serie de la publicación

Nombre7th International Conference on Control, Decision and Information Technologies, CoDIT 2020

Conferencia

Conferencia7th International Conference on Control, Decision and Information Technologies, CoDIT 2020
País/TerritorioRepública Checa
CiudadPrague
Período29/06/202/07/20

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