Work

Self‐training maximum classifier discrepancy for EEG emotion recognition

Year: 2023

Type: article

Abstract: Even with an unprecedented breakthrough of deep learning in electroencephalography (EEG), collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling. Recent study proposed to solve the limited label problem via domain adaptation methods. However, they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries, which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely. A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this study. The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers' outputs. Besides, a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed. Finally, a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network (CNN) is constructed. Extensive experiments on SEED and SEED‐IV are conducted. The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

Source: CAAI Transactions on Intelligence Technology

DOI: 10.1049/cit2.12174

Authors: Xu Zhang, Dengbing Huang, Hanyu Li, Youjia Zhang, Ying Xia, Jinzhuo Liu

Institutions: Chongqing University of Posts and Telecommunications, Inha University, Yunnan University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,600

Funders: National Natural Science Foundation of China, Natural Science Foundation of Chongqing