Title: Related technologies for image feature and affective computing mapping via psychological cognition

Authors: Jie Jiang

Addresses: Center for Student Potential Development, Xi'an Peihua University, Xi'an – 710125, Shaanxi, China

Abstract: Emotional computing has high computational costs, poor classification accuracy, lack of psychological theoretical support, and physiological signal acquisition is susceptible to noise interference. In response, this study proposes a deep learning based emotion recognition model that predicts a person's psychological state by using EEG signal data. This study preprocesses the proposed framework and extracts features from image data using discrete wavelet transform (DWT) and power spectral density (PSD). DWT extracts features as frequency bands, while PSD exports statistical features and parameters. Afterwards, this study retrieves spatial (channel) and temporal (brain peak and related latency) features from EEG data, and uses a 3D convolutional neural network for emotion classification to evaluate the proposed model. Meanwhile, this study also employs both subject dependent and subject independent procedures. The results indicate that extracting multidimensional complementary features in both frequency and spatial domains can improve recognition ability.

Keywords: affective computing; psychological cognition; emotional recognition; convolutional neural network; image features; deep learning; electroencephalogram data.

DOI: 10.1504/IJDSDE.2026.154404

International Journal of Dynamical Systems and Differential Equations, 2026 Vol.15 No.1, pp.1 - 18

Received: 15 Oct 2025
Accepted: 27 Nov 2025
Published online: 26 Jun 2026 *

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