Mar 7, 2020 · In this paper, we propose an automatical optimization framework using binary coding system and GPSO with gradient penalties to select the structure.

Mar 7, 2020 · In this paper, we propose an automatical optimization framework using binary coding system and GPSO with gradient penalties to select the structure.

Results indicate that our method based on the GPSO-optimized CNN model enables us to achieve a prominent classification accuracy, and the proposed method ...

We design an experiment to arouse three types of emotion states for each subject, and simultaneously collect EEG signals corresponding to each emotion category.

This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, ...

We design an experiment to arouse three types of emotion states for each subject, and simultaneously collect EEG signals corresponding to each emotion category.

Bibliographic details on A GPSO-optimized convolutional neural networks for EEG-based emotion recognition.

Feb 27, 2023 · We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We ...

Aug 8, 2023 · This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features.

Article "A GPSO-optimized convolutional neural networks for EEG-based emotion recognition" Detailed information of the J-GLOBAL is an information service ...