  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.
