Development and Validation of an EEG-Based Real-Time Emotion Recognition System Using Edge AI Computing Platform With Convolutional Neural Network System-on-Chip Design

• Published in: IEEE journal on emerging and selected topics in circuits and systems Vol. 9; no. 4; pp. 645 - 657
• Main Authors: Fang, Wai-Chi, Wang, Kai-Yen, Fahier, Nicolas, Ho, Yun-Lung, Huang, Yu-De
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; affective computing; Algorithms; Arousal; Artificial intelligence; Artificial neural networks; Classification; Computer architecture; convolutional neural network (CNN); Convolutional neural networks; Electroencephalography; Emotion recognition; Feature extraction; Field programmable gate arrays; Fourier transforms; Graphical user interface; Hardware; Image reconstruction; Microprocessors; Multiphase; Neural networks; Object recognition; Power consumption; Real time; Real-time systems; System on chip; Training

This study proposed an electroencephalogram (EEG)-based real-time emotion recognition hardware system architecture based on multiphase convolutional neural network (CNN) algorithm implemented on a 28-nm technology chip and on field programmable gate array (FPGA) for binary and quaternary classificat...