Chinese Journal of Magnetic Resonance ›› 2024, Vol. 41 ›› Issue (3): 304-314.doi: 10.11938/cjmr20243094

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Semantic Audiovisual Single-trial Detection Based on the New Generation of Magnetoencephalography

GUO Xu1,2, WANG Chenxu1,2, ZHANG Xin2,3, CHANG Yan2,3, CUI Feng2, GUO Qingqian2,3, HU Tao2,3, YANG Xiaodong1,2,3,*()   

  1. 1. School of Medical Imaging, Xuzhou Medical University, Xuzhou 221004, China
    2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
    3. Jihua Laboratory, Foshan 528200, China
  • Received:2024-01-04 Published:2024-09-05 Online:2024-01-19
  • Contact: *Tel: 18900616030, E-mail: xiaodong.yang@sibet.ac.cn.

Abstract:

In order to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context, this study designed a related task paradigm and applied a new generation magnetoencephalogram combined with the machine learning model to analyze the collected signals from three perspectives: behavioral response, event-related field (ERF) and single-trial detection. Results show that the unimodal semantic response was mainly concentrated in the occipital cortex, while the bimodal semantic response was mainly concentrated in the parietal cortex. At the same time, respondents' response rate and the detection accuracy of single-trial in bimodal mode were significantly higher than that in unimodal mode. Moreover, the support vector machine (SVM) showed the best classification performance among the four machine learning models, with an average classification accuracy of 75.16% for within-subject classification and 80.56% for between-subject classification. This research concludes that the combination of optically pumped magnetometer-magnetoencephalography (OPM-MEG) and machine learning model provides an efficient approach to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context.

Key words: OPM-MEG, semantic, audiovisual bimodal, machine learning, event-related field

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