02512nas a2200253   4500000000100000000000100001008004100002260001200043653002400055653003900079653001800118653004700136653003300183100001600216700001800232700001100250700001400261245011000275856008100385300001000466490000600476520176200482022001402244       2021                            d  c12/202110aAffective Computing10aConvolutional Neural Network (CNN)10aEye Detection10aDeep Gradient Convolutional Neural Network10aShort Time Fourier Transform1 aYuanfeng Li1 aJiangang Deng1 aQun Wu1 aYing Wang00aEye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks  uhttps://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_4_0.pdf  a34-430 v73 aUtilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General
signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process.  a1989-1660