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arXiv · 2105.13987

ScalingNet: extracting features from raw EEG data for emotion recognition

Jingzhao Hu·Chen Wang·Qiaomei Jia·Qirong Bu·Jun Feng
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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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Jingzhao Hu, Chen Wang, Qiaomei Jia, Qirong Bu, Jun Feng. 2021-02-07. ScalingNet: extracting features from raw EEG data for emotion recognition. https://arxiv.org/abs/2105.13987

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