ScalingNet: extracting features from raw EEG data for emotion recognition

• URL: http://arxiv.org/abs/2105.13987v1
• Date: Sun, 7 Feb 2021 08:54:27 GMT
• Title: ScalingNet: extracting features from raw EEG data for emotion recognition
• Authors: Jingzhao Hu, Chen Wang, Qiaomei Jia, Qirong Bu, Jun Feng
• Abstract summary: We propose a novel convolutional layer allowing to adaptively extract effective data-driven spectrogram-like features from raw EEG signals. The proposed neural network architecture based on the scaling layer, references as ScalingNet, has achieved the state-of-the-art result across the established DEAP benchmark dataset.
• Score: 4.047737925426405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks(CNNs) has achieved remarkable performance breakthrough in a variety of tasks. Recently, CNNs based methods that are fed with hand-extracted EEG features gradually produce a powerful performance on the EEG data based emotion recognition task. In this paper, we propose a novel convolutional layer allowing to adaptively extract effective data-driven spectrogram-like features from raw EEG signals, which we reference as scaling layer. Further, it leverages 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. The proposed neural network architecture based on the scaling layer, references as ScalingNet, has achieved the state-of-the-art result across the established DEAP benchmark dataset.

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