Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition

نویسندگان

چکیده

In real-world applications such as emotion recognition from recorded brain activity, data are captured electrodes over time. These signals constitute a multidimensional time series. this article, Echo State Network (ESN), recurrent neural network with great success in series prediction and classification, is optimized different plasticity rules for classification of emotions based on electroencephalogram (EEG) The developed could automatically extract valid features EEG signals. We use the filtered input do not take any feature extraction methods. Evaluated two well-known benchmarks, DEAP dataset, SEED performance ESN intrinsic greatly outperforms feature-based methods shows certain advantages compared other existing Thus, proposed can form more complete efficient representation, whilst retaining faster learning speed reliable performance.

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ژورنال

عنوان ژورنال: IEEE Transactions on Affective Computing

سال: 2022

ISSN: ['1949-3045', '2371-9850']

DOI: https://doi.org/10.1109/taffc.2020.2982143