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 is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.

We present a stylized scheme that produces pencil drawings in a range of styles from an image. To produce controllable pencil drawing effects and remedy the problems of existing convolution‐based schemes, we develop a swing bilateral LIC (SBL) filter. Our first approach to express the styled pencil drawings is to control the directions of pencil strokes that depicts both shapes and smooth tone. Another approach is to produce colors of pencil drawings by sampling colors from real color pencils. The third approach is to mimic the artistic technique that increases the details of drawings in a progressive manner. We present drawings in several styles and compare some of them directly with illustrations taken from an artists' work.

We re-render a photographic image as a simulated pencil drawing using two independent line integral convolution (LIC) algorithms that express tone and feature lines. The LIC for tone is then applied in the same direction across the image, while the LIC for features is applied in pixels close to each feature line in the direction of that line. Features are extracted using the coherent line scheme. Changing the direction and range of the LICs allows a wide range of pencil drawing style to be mimicked. We tested our algorithm on diverse images and obtained encouraging results.

Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell’s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.

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