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We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision isdoi:10.3390/s19214736 pmid:31683608 pmcid:PMC6865186 fatcat:5z647sxn7raixkhmzjaa45jtcu