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Guo K, Li J, Zhang X. Notes on the improvement of concept-cognitive learning accuracy. Int J Approx Reason 2023. [DOI: 10.1016/j.ijar.2023.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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Li F, Li H, Li Y, Wu H, Fu B, Ji Y, Wang C, Shi G. Decoupling Representation Learning for Imbalanced Electroencephalography Classification in Rapid Serial Visual Presentation Task. J Neural Eng 2022; 19. [PMID: 35472762 DOI: 10.1088/1741-2552/ac6a7d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 04/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The class imbalance problem considerably restricts the performance of electroencephalography (EEG) classification in the rapid serial visual presentation (RSVP) task. Existing solutions typically employ re-balancing strategies (e.g. re-weighting and re-sampling) to alleviate the impact of class imbalance, which enhances the classifier learning of deep networks but unexpectedly damages the representative ability of the learned deep features as original distributions become distorted. APPROACH In this study, a novel decoupling representation learning (DRL) model, has been proposed that separates the representation learning and classification processes to capture the discriminative feature of imbalanced RSVP EEG data while classifying it accurately. The representation learning process is responsible for learning universal patterns for the classification of all samples, while the classifier determines a better bounding for the target and non-target classes. Specifically, the representation learning process adopts a dual-branch architecture, which minimizes the contrastive loss to regularize the representation space. In addition, to learn more discriminative information from RSVP EEG data, a novel multi-granular information (MGI) based extractor is designed to extract spatial-temporal information. Considering the class re-balancing strategies can significantly promote classifier learning, the classifier was trained with rebalanced EEG data while freezing the parameters of the representation learning process. MAIN RESULTS To evaluate the proposed method, experiments were conducted on two public datasets and one self-conducted dataset. The results demonstrate that the proposed DRL can achieve state-of-the-art performance for EEG classification in the RSVP task. SIGNIFICANCE This is the first study to focus on the class imbalance problem and propose a generic solution in the RSVP task. Furthermore, multi-granular data was explored to extract more complementary spatial-temporal information. The code is open-source and available at https://github.com/Tammie-Li/DRL.
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Affiliation(s)
- Fu Li
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Hongxin Li
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Yang Li
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, 710071, CHINA
| | - Hao Wu
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Boxun Fu
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Youshuo Ji
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Chong Wang
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
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Yang Y, Zhang H, Lee S. EEG Signal Discrimination with Permutation Entropy. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Li P, Wang G, Hu J, Li Y. Multi-granularity Complex Network Representation Learning. ROUGH SETS 2020. [PMCID: PMC7338194 DOI: 10.1007/978-3-030-52705-1_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Network representation learning aims to learn the low dimensional vector of the nodes in a network while maintaining the inherent properties of the original information. Existing algorithms focus on the single coarse-grained topology of nodes or text information alone, which cannot describe complex information networks. However, node structure and attribution are interdependent, indecomposable. Therefore, it is essential to learn the representation of node based on both the topological structure and node additional attributes. In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. Experiments show that our method can not only capture indecomposable multi-granularity information, but also retain various potential similarities of both topology and node attributes. It has achieved effective results in the downstream work of node classification and the link prediction on real-world datasets.
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Liu W, Huang A, Wang P, Chu CH. PbFG: Physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Shao MW, Lv MM, Li KW, Wang CZ. The construction of attribute (object)-oriented multi-granularity concept lattices. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-00955-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Liu H, Li W, Li R. A comparative analysis of granular computing clustering from the view of set. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-152327] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Data selection in EEG signals classification. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2016; 39:157-65. [DOI: 10.1007/s13246-015-0414-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Accepted: 12/09/2015] [Indexed: 10/22/2022]
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