ScalingNet: Extracting features from raw EEG data for emotion recognition

Jingzhao Hu, Chen Wang, Qiaomei Jia, Qirong Bu, Richard Sutcliffe, Jun Feng

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)177-184
Number of pages8
JournalNeurocomputing
Volume463
DOIs
Publication statusPublished - 6 Nov 2021
Externally publishedYes

Keywords

  • Convolutional Neural Networks
  • Deep learning
  • EEG
  • Emotion recognition
  • ScalingNet

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