[1]ZHANG Hangkui,LIU Dongjun,KONG Wanzeng.ERP detection of multi-feature embedding in the low-dimensional subspace for cross-subject RSVP[J].CAAI Transactions on Intelligent Systems,2022,17(5):1054-1061.[doi:10.11992/tis.202111059]
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ERP detection of multi-feature embedding in the low-dimensional subspace for cross-subject RSVP

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 5 Page number: 1054-1061 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-09-05
Title:
ERP detection of multi-feature embedding in the low-dimensional subspace for cross-subject RSVP
Author(s):
ZHANG Hangkui1; 2;  LIU Dongjun1; 2;  KONG Wanzeng1; 2
1. College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China;
2. Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
Keywords:
rapid serial visual presentation; event-related potential; Euclidean space data alignment; cross-subject; multiple features; low-dimensional subspace embedding; leave-one-subject-out
CLC:
TP18
DOI:
10.11992/tis.202111059
Abstract:
The rapid serial visual presentation (RSVP)-based target image retrieval method finishes complex target image retrieval by relying on the event-related potentials (ERP) generated by the human brain when noticing a target image. When applying the RSVP paradigm to complex target image retrieval, the problems of cross-period and even cross-subjects often arise. To solve these problems, this paper proposes an ERP detection method of multi-feature embedding in a low-dimensional subspace for cross-subject RSVP. First, the Euclidean space data alignment in the transfer learning method is used to align the EEG data. Then, supervised dimensionality reduction and reconstruction are conducted on features from different spaces, respectively. Finally, the leave-one-subject-out method is used as the test method and the balanced classification accuracy rate as the evaluation indicator. Consequently, out of 14 length segments under the PhysioNetRSVP dataset and the Tsinghua RSVP dataset, 12 length segments achieve optimal classification results. Experimental results show that multi-feature embedding in the low-dimensional subspace proposed in this paper can effectively improve the stability of ERP detection.
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