Hu, Jingzhao and Wang, Chen and Jia, Qiaomei and Bu, Qirong and Sutcliffe, Richard and Feng, Jun (2021) ScalingNet: Extracting features from raw EEG data for emotion recognition. Neurocomputing, 463. pp. 177-184. DOI https://doi.org/10.1016/j.neucom.2021.08.018
Hu, Jingzhao and Wang, Chen and Jia, Qiaomei and Bu, Qirong and Sutcliffe, Richard and Feng, Jun (2021) ScalingNet: Extracting features from raw EEG data for emotion recognition. Neurocomputing, 463. pp. 177-184. DOI https://doi.org/10.1016/j.neucom.2021.08.018
Hu, Jingzhao and Wang, Chen and Jia, Qiaomei and Bu, Qirong and Sutcliffe, Richard and Feng, Jun (2021) ScalingNet: Extracting features from raw EEG data for emotion recognition. Neurocomputing, 463. pp. 177-184. DOI https://doi.org/10.1016/j.neucom.2021.08.018
Abstract
Convolutional Neural Networks (CNNs) have achieved remarkable performance breakthroughs in a variety of tasks. Recently, CNN-based methods that are fed with hand-extracted EEG features have steadily improved their performance on the emotion recognition task. In this paper, we propose a novel convolutional layer, called the Scaling Layer, which can adaptively extract effective data-driven spectrogram-like features from raw EEG signals. Furthermore, it exploits convolutional kernels scaled from one data-driven pattern to exposed a frequency-like dimension to address the shortcomings of prior methods requiring hand-extracted features or their approximations. ScalingNet, the proposed neural network architecture based on the Scaling Layer, has achieved state-of-the-art results across the established DEAP and AMIGOS benchmark datasets.
Item Type: | Article |
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Uncontrolled Keywords: | Deep learning; Convolutional Neural Networks; EEG; Emotion recognition; ScalingNet |
Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 01 Nov 2024 15:33 |
Last Modified: | 01 Nov 2024 15:34 |
URI: | http://repository.essex.ac.uk/id/eprint/36884 |
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Filename: hu_jingzhao_scalingnet_eeg_2021.pdf
Licence: Creative Commons: Attribution 4.0