Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73270
Título: Unsupervised learning in reservoir computing for EEG-based emotion recognition
Autores/as: Fourati, Rahma
Ammar, Boudour
Sánchez Medina, Javier Jesús 
Alimi, Adel M.
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Brain modeling
Echo state network
Electroencephalogram
Electroencephalography
Emotion recognition, et al.
Fecha de publicación: 2022
Publicación seriada: IEEE Transactions on Affective Computing 
Resumen: In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. The developed network could automatically extract valid features from EEG signals. We use the filtered signals as the network input and do not take any feature extraction methods. Evaluated on two well-known benchmarks, the DEAP dataset, and the SEED dataset, the performance of the ESN with intrinsic plasticity greatly outperforms the feature-based methods and shows certain advantages compared with other existing methods. Thus, the proposed network can form a more complete and efficient representation, whilst retaining the advantages such as faster learning speed and more reliable performance.
URI: http://hdl.handle.net/10553/73270
ISSN: 1949-3045
DOI: 10.1109/TAFFC.2020.2982143
Fuente: IEEE Transactions on Affective Computing [EISSN 1949-3045], v. 13(2), p. 972-984 (2022)
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