Author(s): Gu, XQ (Gu, Xiaoqing); Cai, WW (Cai, Weiwei); Gao, M (Gao, Ming); Jiang, YZ (Jiang, Yizhang); Ning, X (Ning, Xin); Qian, PJ (Qian, Pengjiang)

Source: IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS DOI: 10.1109/TCSS.2022.3153660 Early Access Date: MAR 2022

Abstract: Cognitive computing is dedicated to researching a computing principle and method that can simulate the intelligence ability of human brain. Human emotion is the basic component of human cognitive activities. Electroencephalogram (EEG) computer signals obtained from a brain computer interface are difficult to conceal, and using machine learning methods to analyze EEG emotion is a hot topic in artificial intelligence. However, the EEG signal is non-stationary, making it difficult to select sufficient data from the same person to train a classifier for a subject. To promote the performance of emotion recognition methods, a multi-source domain transfer discriminative dictionary learning modeling (MDTDDL) is proposed in this study. The method integrates transfer learning and dictionary learning in a learning model, including the concepts of subspace learning, manifold smoothness, margin-based discriminant embedding, and large margin. The domain-specific transformation matrix projects EEG signals from various domains into the transfer subspace. The domain-invariant dictionary can find potential connections between multiple source domains and target domain. The manifold smoothness and margin-based discriminant embedding term further improve the model's learning ability. The alternating optimization technique is used in model solving to efficiently compute model parameters. Experiments on the SEED and DEAP datasets demonstrate the effectiveness of MDTDDL.

Accession Number: WOS:000767822500001

Author Identifiers:

Author Web of Science ResearcherID ORCID Number

Cai, Weiwei AAH-5456-2020 0000-0001-6795-6152

ISSN: 2329-924X

Full Text: https://ieeexplore.ieee.org/document/9730074