The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals. This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by biosignal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chestband respiration sensor, and five HRV parameters were acquired from a fingerclip blood volume pulse (BVP) sensor. A newly designed deeplearning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

Shorttimespan EEGbased personalized emotion recognition with deep convolutional neural network Emotion recognition can be useful in various applications such as in neurofeedback training for functional enhancement. A practically realizable emotion recognition system should rely on as little physiological signalsmodalities as possible. Also, emotionrelated neurological activities may be vastly different from person to person. Hence, this paper presents the singlemodal EEGbased personalized emotion recognition convolutional neural network (CNN) models working on the DEAP dataset. The valence and arousal level classification performance of our presented CNN classifiers have surpassed the other emotion classifiers working on the DEAP dataset based on our scope of literature reviewed. The models, which are deep CNN, rely on only plain EEG data and require no preextracted EEG features. The design and application of the CNN models is aimed at possible future work of identification of new emotionrelated EEG features, relying on the automated feature extraction capability of the CNN. The two CNN models presented have achieved the 3class valence classification test accuracy of 97.59% and 98.75% respectively, and the 3class arousal classification test accuracy of 98.48% and 97.58%.