Title: EEG-based emotion recognition via improved evolutionary convolutional neural network

Authors: Lexiang Guo; Nan Li; Tian Zhang

Addresses: School of Instrument and Electronics, North University of China, Taiyuan, China ' College of Software, Northeastern University, Shenyang, China ' College of Software, Northeastern University, Shenyang, China

Abstract: Deep learning has emerged in many practical applications, such as vascular segmentation, fault diagnosis, and human detection. More recently, convolutional neural networks (CNNs), representative techniques of deep learning, have been used to solve emotion recognition. However, the current design of CNNs for emotion recognition is highly dependent on domain knowledge and needs a large amount of trial and error. For this reason, an evolutionary CNN framework is developed to automatically find network architecture for EEG-based emotion recognition. Specifically, we firstly design a search space based on three advanced network basic units. Based on this, a flexible variable-length encoding is proposed and the corresponding reproduction operators (i.e., crossover and mutation) are designed. To reduce search overhead, this paper proposes an acceleration strategy based on the similarity metric for population memory. A series of experimental results show that the architecture by ECNN-ER method achieves higher accuracy (96.47%) compared to the state-of-the-art results (i.e., DARTS-PV) on the DEAP dataset, as well as competitive results (accuracy = 95.78%) on the DREAMER dataset.

Keywords: neural architecture search; evolutionary computation; population memory; emotion recognition; convolutional neural network; CNN.

DOI: 10.1504/IJBIC.2024.139268

International Journal of Bio-Inspired Computation, 2024 Vol.23 No.4, pp.203 - 213

Received: 29 Nov 2023
Accepted: 23 Jan 2024
Published online: 28 Jun 2024 *

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