ScalingNet: Extracting features from raw EEG data for emotion recognition

[摘要] 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. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

[发布日期] 2021-11-06

[关键词] Deep learning;Convolutional Neural Networks;EEG;Emotion recognition;ScalingNet